B. Shyrokau
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Safety-critical vehicle control systems require both accurate state estimation and reliable uncertainty quantification, particularly for vehicle sideslip angle estimation. In production vehicles, sensor suites are inherently heterogeneous and distributed, with inertial measurement units, wheel speed sensors, and optionally wheel force transducers possibly operating at different native frequencies and exhibiting asynchronous dropouts and rate degradation. However, most learning-based estimators assume idealised synchronous sampling, often relying on resampling strategies such as zero-order hold, which remove explicit information about sensor staleness and temporal irregularity.
This work investigates whether neural estimators can maintain both accuracy and calibrated uncertainty under both synchronous and asynchronous sensor rate degradation, using an extensive experimental vehicle dynamics dataset. To address the brittleness of learned temporal representations, a targeted data augmentation strategy simulating asynchronous dropouts using a Bernoulli dropout is proposed. This strategy is evaluated across three data-driven architectures that differ in how deeply time is embedded: a discrete-time recurrent network operating on fixed-grid resampled inputs, a time-augmented recurrent network explicitly including per-channel staleness features, and a closed-form continuous-time model that embeds inter-arrival time directly into its hidden-state dynamics. These are evaluated against a model-based unscented Kalman filter and a proposed continuous-discrete hybrid estimator, in which the neural network provides pseudo-measurements and uncertainty estimates to the filter.
The estimation pipelines are evaluated across extensive synchronous and asynchronous sensor rate degradation sweeps. Results reveal a clear hierarchy driven by how temporal information is encoded and its interaction with data augmentation. The impact of augmentation varies significantly with the depth of the temporal embedding. The time-augmented network is highly sensitive to distribution shifts and critically requires training on augmented data to maintain functional performance. The zero-order hold approach achieves strong mean accuracy but obscures temporal irregularity from its uncertainty mechanism and lacks native support for operating on event-driven timelines. The continuous-time model provides the most inherently balanced behaviour, combining competitive accuracy with robust continuous-time updates. However, all purely data-driven models exhibit increasing statistical overconfidence under severe degradation, with unbounded neural estimators prone to non-physical errors on out-of-distribution inputs.
The proposed hybrid framework addresses this limitation by combining neural pseudo-measurements with physically consistent filtering. The unscented Kalman filter provides a structured, physics-based fallback in the presence of observability loss, while the continuous-time network contributes high-fidelity predictions and learned uncertainty estimates. This fusion significantly reduces worst-case errors under moderate and severe sensor rate degradation (10 Hz and 5 Hz), decreasing the maximum estimation error beyond either standalone component, albeit with a trade-off in nominal accuracy.
Overall, the results demonstrate that while continuous-time neural models improve robustness to irregular sampling, safe deployment in real-world automotive systems requires explicit coupling with model-based constraints to ensure bounded worst-case behaviour under sensor sparsity. ...
This work investigates whether neural estimators can maintain both accuracy and calibrated uncertainty under both synchronous and asynchronous sensor rate degradation, using an extensive experimental vehicle dynamics dataset. To address the brittleness of learned temporal representations, a targeted data augmentation strategy simulating asynchronous dropouts using a Bernoulli dropout is proposed. This strategy is evaluated across three data-driven architectures that differ in how deeply time is embedded: a discrete-time recurrent network operating on fixed-grid resampled inputs, a time-augmented recurrent network explicitly including per-channel staleness features, and a closed-form continuous-time model that embeds inter-arrival time directly into its hidden-state dynamics. These are evaluated against a model-based unscented Kalman filter and a proposed continuous-discrete hybrid estimator, in which the neural network provides pseudo-measurements and uncertainty estimates to the filter.
The estimation pipelines are evaluated across extensive synchronous and asynchronous sensor rate degradation sweeps. Results reveal a clear hierarchy driven by how temporal information is encoded and its interaction with data augmentation. The impact of augmentation varies significantly with the depth of the temporal embedding. The time-augmented network is highly sensitive to distribution shifts and critically requires training on augmented data to maintain functional performance. The zero-order hold approach achieves strong mean accuracy but obscures temporal irregularity from its uncertainty mechanism and lacks native support for operating on event-driven timelines. The continuous-time model provides the most inherently balanced behaviour, combining competitive accuracy with robust continuous-time updates. However, all purely data-driven models exhibit increasing statistical overconfidence under severe degradation, with unbounded neural estimators prone to non-physical errors on out-of-distribution inputs.
The proposed hybrid framework addresses this limitation by combining neural pseudo-measurements with physically consistent filtering. The unscented Kalman filter provides a structured, physics-based fallback in the presence of observability loss, while the continuous-time network contributes high-fidelity predictions and learned uncertainty estimates. This fusion significantly reduces worst-case errors under moderate and severe sensor rate degradation (10 Hz and 5 Hz), decreasing the maximum estimation error beyond either standalone component, albeit with a trade-off in nominal accuracy.
Overall, the results demonstrate that while continuous-time neural models improve robustness to irregular sampling, safe deployment in real-world automotive systems requires explicit coupling with model-based constraints to ensure bounded worst-case behaviour under sensor sparsity. ...
Safety-critical vehicle control systems require both accurate state estimation and reliable uncertainty quantification, particularly for vehicle sideslip angle estimation. In production vehicles, sensor suites are inherently heterogeneous and distributed, with inertial measurement units, wheel speed sensors, and optionally wheel force transducers possibly operating at different native frequencies and exhibiting asynchronous dropouts and rate degradation. However, most learning-based estimators assume idealised synchronous sampling, often relying on resampling strategies such as zero-order hold, which remove explicit information about sensor staleness and temporal irregularity.
This work investigates whether neural estimators can maintain both accuracy and calibrated uncertainty under both synchronous and asynchronous sensor rate degradation, using an extensive experimental vehicle dynamics dataset. To address the brittleness of learned temporal representations, a targeted data augmentation strategy simulating asynchronous dropouts using a Bernoulli dropout is proposed. This strategy is evaluated across three data-driven architectures that differ in how deeply time is embedded: a discrete-time recurrent network operating on fixed-grid resampled inputs, a time-augmented recurrent network explicitly including per-channel staleness features, and a closed-form continuous-time model that embeds inter-arrival time directly into its hidden-state dynamics. These are evaluated against a model-based unscented Kalman filter and a proposed continuous-discrete hybrid estimator, in which the neural network provides pseudo-measurements and uncertainty estimates to the filter.
The estimation pipelines are evaluated across extensive synchronous and asynchronous sensor rate degradation sweeps. Results reveal a clear hierarchy driven by how temporal information is encoded and its interaction with data augmentation. The impact of augmentation varies significantly with the depth of the temporal embedding. The time-augmented network is highly sensitive to distribution shifts and critically requires training on augmented data to maintain functional performance. The zero-order hold approach achieves strong mean accuracy but obscures temporal irregularity from its uncertainty mechanism and lacks native support for operating on event-driven timelines. The continuous-time model provides the most inherently balanced behaviour, combining competitive accuracy with robust continuous-time updates. However, all purely data-driven models exhibit increasing statistical overconfidence under severe degradation, with unbounded neural estimators prone to non-physical errors on out-of-distribution inputs.
The proposed hybrid framework addresses this limitation by combining neural pseudo-measurements with physically consistent filtering. The unscented Kalman filter provides a structured, physics-based fallback in the presence of observability loss, while the continuous-time network contributes high-fidelity predictions and learned uncertainty estimates. This fusion significantly reduces worst-case errors under moderate and severe sensor rate degradation (10 Hz and 5 Hz), decreasing the maximum estimation error beyond either standalone component, albeit with a trade-off in nominal accuracy.
Overall, the results demonstrate that while continuous-time neural models improve robustness to irregular sampling, safe deployment in real-world automotive systems requires explicit coupling with model-based constraints to ensure bounded worst-case behaviour under sensor sparsity.
This work investigates whether neural estimators can maintain both accuracy and calibrated uncertainty under both synchronous and asynchronous sensor rate degradation, using an extensive experimental vehicle dynamics dataset. To address the brittleness of learned temporal representations, a targeted data augmentation strategy simulating asynchronous dropouts using a Bernoulli dropout is proposed. This strategy is evaluated across three data-driven architectures that differ in how deeply time is embedded: a discrete-time recurrent network operating on fixed-grid resampled inputs, a time-augmented recurrent network explicitly including per-channel staleness features, and a closed-form continuous-time model that embeds inter-arrival time directly into its hidden-state dynamics. These are evaluated against a model-based unscented Kalman filter and a proposed continuous-discrete hybrid estimator, in which the neural network provides pseudo-measurements and uncertainty estimates to the filter.
The estimation pipelines are evaluated across extensive synchronous and asynchronous sensor rate degradation sweeps. Results reveal a clear hierarchy driven by how temporal information is encoded and its interaction with data augmentation. The impact of augmentation varies significantly with the depth of the temporal embedding. The time-augmented network is highly sensitive to distribution shifts and critically requires training on augmented data to maintain functional performance. The zero-order hold approach achieves strong mean accuracy but obscures temporal irregularity from its uncertainty mechanism and lacks native support for operating on event-driven timelines. The continuous-time model provides the most inherently balanced behaviour, combining competitive accuracy with robust continuous-time updates. However, all purely data-driven models exhibit increasing statistical overconfidence under severe degradation, with unbounded neural estimators prone to non-physical errors on out-of-distribution inputs.
The proposed hybrid framework addresses this limitation by combining neural pseudo-measurements with physically consistent filtering. The unscented Kalman filter provides a structured, physics-based fallback in the presence of observability loss, while the continuous-time network contributes high-fidelity predictions and learned uncertainty estimates. This fusion significantly reduces worst-case errors under moderate and severe sensor rate degradation (10 Hz and 5 Hz), decreasing the maximum estimation error beyond either standalone component, albeit with a trade-off in nominal accuracy.
Overall, the results demonstrate that while continuous-time neural models improve robustness to irregular sampling, safe deployment in real-world automotive systems requires explicit coupling with model-based constraints to ensure bounded worst-case behaviour under sensor sparsity.
Dynamic Tube MPC for Hybrid Fault-Tolerant Torque Vectoring
Extending zoRO and acados for Parametric Uncertainty
This thesis develops the groundwork for a hybrid fault-tolerant torque-vectoring controller for four-wheel independently driven electric vehicles. Actuator faults are modeled as loss-of-effectiveness gains on the applied wheel torques. An external estimator is assumed to provide both a nominal effectiveness estimate and a corresponding uncertainty description. The nominal estimate is used to update the prediction model, while the remaining uncertainty is handled through a robust tube nonlinear model predictive control formulation. Since classical tube model predictive control formulations are often too expensive for real-time use, this thesis builds on the zoRO framework, which preserves near-nominal computational complexity by propagating the uncertainty outside the optimal control problem.
A central difficulty in this setting is that the relevant uncertainty acts through the model parameters rather than through a standard additive disturbance channel. The first part of the thesis therefore studies how the zoRO framework can account for nonlinearly acting uncertainty represented through the model parameters. The resulting controller-side formulation covers both stage-wise parameter perturbations and constant parameter mismatch, and was implemented and validated in acados through numerical tests, generated-code verification, and closed-loop simulations on a differential-drive robot example with actuator uncertainty.
The second part of the thesis applies this framework to centralized torque vectoring under actuator-effectiveness uncertainty. The focus is placed on the fault direction in which the true actuator effectiveness is higher than assumed by the controller, so that the realized wheel torque is effectively amplified. A high-fidelity IPG CarMaker vehicle model is used for validation. First, it is shown that the nominal controller produces the intended change in handling behavior and extends the approximately linear operating region of the vehicle. The fault-related validation is then carried out in two representative maneuvers: hard-braking and Sine-with-Dwell.
The hard-braking results show that the robustified controller keeps the rear axle closer to the friction limit and prevents repeated excursions into deep slip, which leads to better braking performance than the nominal controller under mismatch. At the same time, introducing uncertainty in the healthy case increases conservatism and therefore reduces nominal braking performance. The Sine-with-Dwell results show that under combined longitudinal and lateral tire-force demand, the robustified controller can prevent the instability that arises in the nominal controller for sufficiently large mismatch. In a representative faulty case, the resulting yaw-rate response remains close to that of the healthy vehicle.
The final controller runs with average solver times around 10 ms in the main configuration, with lower times observed in reduced-iteration tests. The thesis therefore shows that uncertainty acting through the parameter channel can be incorporated in zoRO in a computationally practical way, and that this leads to a meaningful robustness benefit in fault-tolerant torque vectoring. The main limitations are the absence of an integrated fault estimator, the restricted uncertainty description used in the vehicle study, and the need for validation on real-time automotive hardware.
Merged acados contributions: https://github.com/acados/acados/pulls?q=is%3Apr+is%3Amerged+author%3Aivandoornen ...
A central difficulty in this setting is that the relevant uncertainty acts through the model parameters rather than through a standard additive disturbance channel. The first part of the thesis therefore studies how the zoRO framework can account for nonlinearly acting uncertainty represented through the model parameters. The resulting controller-side formulation covers both stage-wise parameter perturbations and constant parameter mismatch, and was implemented and validated in acados through numerical tests, generated-code verification, and closed-loop simulations on a differential-drive robot example with actuator uncertainty.
The second part of the thesis applies this framework to centralized torque vectoring under actuator-effectiveness uncertainty. The focus is placed on the fault direction in which the true actuator effectiveness is higher than assumed by the controller, so that the realized wheel torque is effectively amplified. A high-fidelity IPG CarMaker vehicle model is used for validation. First, it is shown that the nominal controller produces the intended change in handling behavior and extends the approximately linear operating region of the vehicle. The fault-related validation is then carried out in two representative maneuvers: hard-braking and Sine-with-Dwell.
The hard-braking results show that the robustified controller keeps the rear axle closer to the friction limit and prevents repeated excursions into deep slip, which leads to better braking performance than the nominal controller under mismatch. At the same time, introducing uncertainty in the healthy case increases conservatism and therefore reduces nominal braking performance. The Sine-with-Dwell results show that under combined longitudinal and lateral tire-force demand, the robustified controller can prevent the instability that arises in the nominal controller for sufficiently large mismatch. In a representative faulty case, the resulting yaw-rate response remains close to that of the healthy vehicle.
The final controller runs with average solver times around 10 ms in the main configuration, with lower times observed in reduced-iteration tests. The thesis therefore shows that uncertainty acting through the parameter channel can be incorporated in zoRO in a computationally practical way, and that this leads to a meaningful robustness benefit in fault-tolerant torque vectoring. The main limitations are the absence of an integrated fault estimator, the restricted uncertainty description used in the vehicle study, and the need for validation on real-time automotive hardware.
Merged acados contributions: https://github.com/acados/acados/pulls?q=is%3Apr+is%3Amerged+author%3Aivandoornen ...
This thesis develops the groundwork for a hybrid fault-tolerant torque-vectoring controller for four-wheel independently driven electric vehicles. Actuator faults are modeled as loss-of-effectiveness gains on the applied wheel torques. An external estimator is assumed to provide both a nominal effectiveness estimate and a corresponding uncertainty description. The nominal estimate is used to update the prediction model, while the remaining uncertainty is handled through a robust tube nonlinear model predictive control formulation. Since classical tube model predictive control formulations are often too expensive for real-time use, this thesis builds on the zoRO framework, which preserves near-nominal computational complexity by propagating the uncertainty outside the optimal control problem.
A central difficulty in this setting is that the relevant uncertainty acts through the model parameters rather than through a standard additive disturbance channel. The first part of the thesis therefore studies how the zoRO framework can account for nonlinearly acting uncertainty represented through the model parameters. The resulting controller-side formulation covers both stage-wise parameter perturbations and constant parameter mismatch, and was implemented and validated in acados through numerical tests, generated-code verification, and closed-loop simulations on a differential-drive robot example with actuator uncertainty.
The second part of the thesis applies this framework to centralized torque vectoring under actuator-effectiveness uncertainty. The focus is placed on the fault direction in which the true actuator effectiveness is higher than assumed by the controller, so that the realized wheel torque is effectively amplified. A high-fidelity IPG CarMaker vehicle model is used for validation. First, it is shown that the nominal controller produces the intended change in handling behavior and extends the approximately linear operating region of the vehicle. The fault-related validation is then carried out in two representative maneuvers: hard-braking and Sine-with-Dwell.
The hard-braking results show that the robustified controller keeps the rear axle closer to the friction limit and prevents repeated excursions into deep slip, which leads to better braking performance than the nominal controller under mismatch. At the same time, introducing uncertainty in the healthy case increases conservatism and therefore reduces nominal braking performance. The Sine-with-Dwell results show that under combined longitudinal and lateral tire-force demand, the robustified controller can prevent the instability that arises in the nominal controller for sufficiently large mismatch. In a representative faulty case, the resulting yaw-rate response remains close to that of the healthy vehicle.
The final controller runs with average solver times around 10 ms in the main configuration, with lower times observed in reduced-iteration tests. The thesis therefore shows that uncertainty acting through the parameter channel can be incorporated in zoRO in a computationally practical way, and that this leads to a meaningful robustness benefit in fault-tolerant torque vectoring. The main limitations are the absence of an integrated fault estimator, the restricted uncertainty description used in the vehicle study, and the need for validation on real-time automotive hardware.
Merged acados contributions: https://github.com/acados/acados/pulls?q=is%3Apr+is%3Amerged+author%3Aivandoornen
A central difficulty in this setting is that the relevant uncertainty acts through the model parameters rather than through a standard additive disturbance channel. The first part of the thesis therefore studies how the zoRO framework can account for nonlinearly acting uncertainty represented through the model parameters. The resulting controller-side formulation covers both stage-wise parameter perturbations and constant parameter mismatch, and was implemented and validated in acados through numerical tests, generated-code verification, and closed-loop simulations on a differential-drive robot example with actuator uncertainty.
The second part of the thesis applies this framework to centralized torque vectoring under actuator-effectiveness uncertainty. The focus is placed on the fault direction in which the true actuator effectiveness is higher than assumed by the controller, so that the realized wheel torque is effectively amplified. A high-fidelity IPG CarMaker vehicle model is used for validation. First, it is shown that the nominal controller produces the intended change in handling behavior and extends the approximately linear operating region of the vehicle. The fault-related validation is then carried out in two representative maneuvers: hard-braking and Sine-with-Dwell.
The hard-braking results show that the robustified controller keeps the rear axle closer to the friction limit and prevents repeated excursions into deep slip, which leads to better braking performance than the nominal controller under mismatch. At the same time, introducing uncertainty in the healthy case increases conservatism and therefore reduces nominal braking performance. The Sine-with-Dwell results show that under combined longitudinal and lateral tire-force demand, the robustified controller can prevent the instability that arises in the nominal controller for sufficiently large mismatch. In a representative faulty case, the resulting yaw-rate response remains close to that of the healthy vehicle.
The final controller runs with average solver times around 10 ms in the main configuration, with lower times observed in reduced-iteration tests. The thesis therefore shows that uncertainty acting through the parameter channel can be incorporated in zoRO in a computationally practical way, and that this leads to a meaningful robustness benefit in fault-tolerant torque vectoring. The main limitations are the absence of an integrated fault estimator, the restricted uncertainty description used in the vehicle study, and the need for validation on real-time automotive hardware.
Merged acados contributions: https://github.com/acados/acados/pulls?q=is%3Apr+is%3Amerged+author%3Aivandoornen
A Tentacle-Based Motion Planning Algorithm for Automated Vehicles
Ensuring Real-Time Performance and Passenger Comfort through Limiting Jerk
Master thesis
(2025)
-
Z. Li, B. Shyrokau, A. Bertipaglia, S.H. Hossein Nia Kani, Cosimo Della Santina
Automated driving systems are expected to be revolutionary technologies that will reconstruct mobility by improving safety and efficiency. Among the components of automated driving systems, motion planning plays a critical role as it determines how the vehicle reaches its target location from a given origin. The current challenges of motion planning lie in real-time performance and passenger comfort, which is also the main objective of motion planning algorithms. These challenges often occur simultaneously but are treated separately. Neither of these challenges could be solved at a global level of motion planning, which is mainly concerned with route selection and travel time minimization, and could be computed in advance. Consequently, this thesis primarily focuses on local motion planning related to the maneuvering of a vehicle, ensuring real-time performance and passenger comfort.
To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.
In most motion planning algorithms, geometry path planning and speed planning are treated separately. However, in the proposed planning algorithm, the speed profile is generated based on the selected best tentacle, thus making the speed profile more rational and adaptable to current maneuvers. In addition, the target speed of the speed profile is decided based on road curvature and traffic conditions, ensuring safety and avoiding wasting time on some low-speed traffic participants. More importantly, the speed profile limits the maximum longitudinal jerk, thus making jerk limited in all directions and ensuring passenger comfort.
To evaluate the performance of the proposed motion planning algorithm, simulations using a high-fidelity vehicle model through IPG CarMaker and MATLAB/Simulink are implemented under various conditions. Because this report does not focus on the design of the path-following controller, the vehicle directly uses the output of the planner as control input during simulations. Even so, the algorithm still manages to complete static and dynamic obstacle avoidance as well as adaptive following and overtaking maneuvers at various speed ranges and road conditions. A virtual map of part of the campus of TU Delft is also constructed using Carmaker and used for validation of the proposed algorithm under urban traffic conditions. The proposed algorithm works effectively in complex environments and can operate in real time at a frequency of $20$ $Hz$ while constraining the total jerk.
This research illustrated the capability of the developed tentacle-based motion planning algorithm to ensure passenger comfort and safety under various traffic conditions. Although primarily serving as a concept emphasizing feasibility through simulations rather than immediate on-road verification, the proposed algorithm establishes a foundation for future real-vehicle implementation, thus contributing towards resolving normal driving conditions essential for achieving fully automated driving. ...
To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.
In most motion planning algorithms, geometry path planning and speed planning are treated separately. However, in the proposed planning algorithm, the speed profile is generated based on the selected best tentacle, thus making the speed profile more rational and adaptable to current maneuvers. In addition, the target speed of the speed profile is decided based on road curvature and traffic conditions, ensuring safety and avoiding wasting time on some low-speed traffic participants. More importantly, the speed profile limits the maximum longitudinal jerk, thus making jerk limited in all directions and ensuring passenger comfort.
To evaluate the performance of the proposed motion planning algorithm, simulations using a high-fidelity vehicle model through IPG CarMaker and MATLAB/Simulink are implemented under various conditions. Because this report does not focus on the design of the path-following controller, the vehicle directly uses the output of the planner as control input during simulations. Even so, the algorithm still manages to complete static and dynamic obstacle avoidance as well as adaptive following and overtaking maneuvers at various speed ranges and road conditions. A virtual map of part of the campus of TU Delft is also constructed using Carmaker and used for validation of the proposed algorithm under urban traffic conditions. The proposed algorithm works effectively in complex environments and can operate in real time at a frequency of $20$ $Hz$ while constraining the total jerk.
This research illustrated the capability of the developed tentacle-based motion planning algorithm to ensure passenger comfort and safety under various traffic conditions. Although primarily serving as a concept emphasizing feasibility through simulations rather than immediate on-road verification, the proposed algorithm establishes a foundation for future real-vehicle implementation, thus contributing towards resolving normal driving conditions essential for achieving fully automated driving. ...
Automated driving systems are expected to be revolutionary technologies that will reconstruct mobility by improving safety and efficiency. Among the components of automated driving systems, motion planning plays a critical role as it determines how the vehicle reaches its target location from a given origin. The current challenges of motion planning lie in real-time performance and passenger comfort, which is also the main objective of motion planning algorithms. These challenges often occur simultaneously but are treated separately. Neither of these challenges could be solved at a global level of motion planning, which is mainly concerned with route selection and travel time minimization, and could be computed in advance. Consequently, this thesis primarily focuses on local motion planning related to the maneuvering of a vehicle, ensuring real-time performance and passenger comfort.
To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.
In most motion planning algorithms, geometry path planning and speed planning are treated separately. However, in the proposed planning algorithm, the speed profile is generated based on the selected best tentacle, thus making the speed profile more rational and adaptable to current maneuvers. In addition, the target speed of the speed profile is decided based on road curvature and traffic conditions, ensuring safety and avoiding wasting time on some low-speed traffic participants. More importantly, the speed profile limits the maximum longitudinal jerk, thus making jerk limited in all directions and ensuring passenger comfort.
To evaluate the performance of the proposed motion planning algorithm, simulations using a high-fidelity vehicle model through IPG CarMaker and MATLAB/Simulink are implemented under various conditions. Because this report does not focus on the design of the path-following controller, the vehicle directly uses the output of the planner as control input during simulations. Even so, the algorithm still manages to complete static and dynamic obstacle avoidance as well as adaptive following and overtaking maneuvers at various speed ranges and road conditions. A virtual map of part of the campus of TU Delft is also constructed using Carmaker and used for validation of the proposed algorithm under urban traffic conditions. The proposed algorithm works effectively in complex environments and can operate in real time at a frequency of $20$ $Hz$ while constraining the total jerk.
This research illustrated the capability of the developed tentacle-based motion planning algorithm to ensure passenger comfort and safety under various traffic conditions. Although primarily serving as a concept emphasizing feasibility through simulations rather than immediate on-road verification, the proposed algorithm establishes a foundation for future real-vehicle implementation, thus contributing towards resolving normal driving conditions essential for achieving fully automated driving.
To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.
In most motion planning algorithms, geometry path planning and speed planning are treated separately. However, in the proposed planning algorithm, the speed profile is generated based on the selected best tentacle, thus making the speed profile more rational and adaptable to current maneuvers. In addition, the target speed of the speed profile is decided based on road curvature and traffic conditions, ensuring safety and avoiding wasting time on some low-speed traffic participants. More importantly, the speed profile limits the maximum longitudinal jerk, thus making jerk limited in all directions and ensuring passenger comfort.
To evaluate the performance of the proposed motion planning algorithm, simulations using a high-fidelity vehicle model through IPG CarMaker and MATLAB/Simulink are implemented under various conditions. Because this report does not focus on the design of the path-following controller, the vehicle directly uses the output of the planner as control input during simulations. Even so, the algorithm still manages to complete static and dynamic obstacle avoidance as well as adaptive following and overtaking maneuvers at various speed ranges and road conditions. A virtual map of part of the campus of TU Delft is also constructed using Carmaker and used for validation of the proposed algorithm under urban traffic conditions. The proposed algorithm works effectively in complex environments and can operate in real time at a frequency of $20$ $Hz$ while constraining the total jerk.
This research illustrated the capability of the developed tentacle-based motion planning algorithm to ensure passenger comfort and safety under various traffic conditions. Although primarily serving as a concept emphasizing feasibility through simulations rather than immediate on-road verification, the proposed algorithm establishes a foundation for future real-vehicle implementation, thus contributing towards resolving normal driving conditions essential for achieving fully automated driving.
Grid-Based Stochastic Model Predictive Control for Motion-Planning in Low-Friction Conditions
Addressing Perception Uncertainties in Challenging Road Conditions in Automated Driving
Automated driving is poised to transform the transportation landscape of the future, but several challenges remain before full automation is achieved. One of these challenges lies in managing perception uncertainties, such as those arising from radar and sensor measurements, while maintaining control in low tire-road friction conditions. These challenges often occur simultaneously in adverse weather conditions, but are typically researched separately. Their combined effect on safety and performance remains underexplored, even though addressing them together is critical for robust and reliable automated driving systems.
Lower tire-road friction limits the available tire force required for obstacle avoidance. Properly modeling these tire friction limits is particularly important in dynamic and uncertain environments to adequately account for the changing environment responsively. Moreover, addressing perception uncertainties and modeling low-friction conditions individually can significantly increase computational demands which poses challenges to achieve real-time performance required for real-world implementation. Therefore, this thesis jointly considers perception uncertainties and low tire-road friction conditions, accounting for their interacting effects. This is accomplished by addressing the following research question: "How can perception uncertainties be effectively integrated into motion-planning models for obstacle avoidance to improve performance and safety of automated vehicles in various road conditions?"
To address this question, a grid-based stochastic model predictive control framework is extended, implementing a non-linear bicycle model and a Fiala tire model (brush model) to consider realistic vehicle capabilities in low-friction conditions. Grid-based stochastic model predictive control reduces the uncertain obstacle environment into a set of linear constraints, utilizing an occupancy probability grid to effectively consider perception uncertainties. While the developed framework is presented as a proof-of-concept with a focus on safety and feasibility over real-time implementation, computational efficiency is not overlooked. By reformulating the constraints, the uncertainties are effectively accounted for while also reducing the optimization time by simplifying the probabilistic obstacle space to a deterministic convex region. If the nominal reformulation fails, a novel back-up method generates a conservative back-up set of constraints improving the safety and feasibility of the method. Two different back-up strategies are proposed, providing a trade-off between accuracy and computational effort.
To evaluate the contributions, three simulations were conducted. The first comparing the non-linear bicycle model and Fiala tire model to more simplistic models at various tire-road friction coefficients, highlighting the improvement in control of the proposed model in low-friction conditions. The second simulation evaluated the performance of the proposed back-up methods and environment representation by simulating tight scenarios that would fail using only the nominal approach. Feasibility rates increased compared to the baseline back-up method (43.8%) with feasibility rates of 62.5% for the precomputed back-up method and 75.0% for the current-state back-up method. These simulation results demonstrate that the capability of the proposed framework to compute feasible solutions and that the framework is able to compute valid hulls that are safe when the nominal approach fails to reformulate the constraints. The final simulation evaluated the performance of the complete proposed method by simulating both tight scenarios requiring a back-up method as well as various friction levels. These simulations demonstrated that the proposed framework is effective at lower friction levels, achieving high feasibility rates of 87.5% for the current-state back-up method, and 91.6% for the precomputed back-up method. This high accuracy is particularly promising for real-time applications using the precomputed back-up strategy, since this method leverages parallel computation of the back-up constraints.
This thesis demonstrated the effectiveness of the proposed extended grid-based stochastic model predictive control framework in considering perception uncertainties in low-friction road conditions. While presented as a proof-of-concept with an emphasis on feasibility and safety rather than real-time implementation, the proposed framework lays the groundwork for real-time applications, marking a step in solving all edge cases required to reach fully automated vehicles.
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Lower tire-road friction limits the available tire force required for obstacle avoidance. Properly modeling these tire friction limits is particularly important in dynamic and uncertain environments to adequately account for the changing environment responsively. Moreover, addressing perception uncertainties and modeling low-friction conditions individually can significantly increase computational demands which poses challenges to achieve real-time performance required for real-world implementation. Therefore, this thesis jointly considers perception uncertainties and low tire-road friction conditions, accounting for their interacting effects. This is accomplished by addressing the following research question: "How can perception uncertainties be effectively integrated into motion-planning models for obstacle avoidance to improve performance and safety of automated vehicles in various road conditions?"
To address this question, a grid-based stochastic model predictive control framework is extended, implementing a non-linear bicycle model and a Fiala tire model (brush model) to consider realistic vehicle capabilities in low-friction conditions. Grid-based stochastic model predictive control reduces the uncertain obstacle environment into a set of linear constraints, utilizing an occupancy probability grid to effectively consider perception uncertainties. While the developed framework is presented as a proof-of-concept with a focus on safety and feasibility over real-time implementation, computational efficiency is not overlooked. By reformulating the constraints, the uncertainties are effectively accounted for while also reducing the optimization time by simplifying the probabilistic obstacle space to a deterministic convex region. If the nominal reformulation fails, a novel back-up method generates a conservative back-up set of constraints improving the safety and feasibility of the method. Two different back-up strategies are proposed, providing a trade-off between accuracy and computational effort.
To evaluate the contributions, three simulations were conducted. The first comparing the non-linear bicycle model and Fiala tire model to more simplistic models at various tire-road friction coefficients, highlighting the improvement in control of the proposed model in low-friction conditions. The second simulation evaluated the performance of the proposed back-up methods and environment representation by simulating tight scenarios that would fail using only the nominal approach. Feasibility rates increased compared to the baseline back-up method (43.8%) with feasibility rates of 62.5% for the precomputed back-up method and 75.0% for the current-state back-up method. These simulation results demonstrate that the capability of the proposed framework to compute feasible solutions and that the framework is able to compute valid hulls that are safe when the nominal approach fails to reformulate the constraints. The final simulation evaluated the performance of the complete proposed method by simulating both tight scenarios requiring a back-up method as well as various friction levels. These simulations demonstrated that the proposed framework is effective at lower friction levels, achieving high feasibility rates of 87.5% for the current-state back-up method, and 91.6% for the precomputed back-up method. This high accuracy is particularly promising for real-time applications using the precomputed back-up strategy, since this method leverages parallel computation of the back-up constraints.
This thesis demonstrated the effectiveness of the proposed extended grid-based stochastic model predictive control framework in considering perception uncertainties in low-friction road conditions. While presented as a proof-of-concept with an emphasis on feasibility and safety rather than real-time implementation, the proposed framework lays the groundwork for real-time applications, marking a step in solving all edge cases required to reach fully automated vehicles.
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Automated driving is poised to transform the transportation landscape of the future, but several challenges remain before full automation is achieved. One of these challenges lies in managing perception uncertainties, such as those arising from radar and sensor measurements, while maintaining control in low tire-road friction conditions. These challenges often occur simultaneously in adverse weather conditions, but are typically researched separately. Their combined effect on safety and performance remains underexplored, even though addressing them together is critical for robust and reliable automated driving systems.
Lower tire-road friction limits the available tire force required for obstacle avoidance. Properly modeling these tire friction limits is particularly important in dynamic and uncertain environments to adequately account for the changing environment responsively. Moreover, addressing perception uncertainties and modeling low-friction conditions individually can significantly increase computational demands which poses challenges to achieve real-time performance required for real-world implementation. Therefore, this thesis jointly considers perception uncertainties and low tire-road friction conditions, accounting for their interacting effects. This is accomplished by addressing the following research question: "How can perception uncertainties be effectively integrated into motion-planning models for obstacle avoidance to improve performance and safety of automated vehicles in various road conditions?"
To address this question, a grid-based stochastic model predictive control framework is extended, implementing a non-linear bicycle model and a Fiala tire model (brush model) to consider realistic vehicle capabilities in low-friction conditions. Grid-based stochastic model predictive control reduces the uncertain obstacle environment into a set of linear constraints, utilizing an occupancy probability grid to effectively consider perception uncertainties. While the developed framework is presented as a proof-of-concept with a focus on safety and feasibility over real-time implementation, computational efficiency is not overlooked. By reformulating the constraints, the uncertainties are effectively accounted for while also reducing the optimization time by simplifying the probabilistic obstacle space to a deterministic convex region. If the nominal reformulation fails, a novel back-up method generates a conservative back-up set of constraints improving the safety and feasibility of the method. Two different back-up strategies are proposed, providing a trade-off between accuracy and computational effort.
To evaluate the contributions, three simulations were conducted. The first comparing the non-linear bicycle model and Fiala tire model to more simplistic models at various tire-road friction coefficients, highlighting the improvement in control of the proposed model in low-friction conditions. The second simulation evaluated the performance of the proposed back-up methods and environment representation by simulating tight scenarios that would fail using only the nominal approach. Feasibility rates increased compared to the baseline back-up method (43.8%) with feasibility rates of 62.5% for the precomputed back-up method and 75.0% for the current-state back-up method. These simulation results demonstrate that the capability of the proposed framework to compute feasible solutions and that the framework is able to compute valid hulls that are safe when the nominal approach fails to reformulate the constraints. The final simulation evaluated the performance of the complete proposed method by simulating both tight scenarios requiring a back-up method as well as various friction levels. These simulations demonstrated that the proposed framework is effective at lower friction levels, achieving high feasibility rates of 87.5% for the current-state back-up method, and 91.6% for the precomputed back-up method. This high accuracy is particularly promising for real-time applications using the precomputed back-up strategy, since this method leverages parallel computation of the back-up constraints.
This thesis demonstrated the effectiveness of the proposed extended grid-based stochastic model predictive control framework in considering perception uncertainties in low-friction road conditions. While presented as a proof-of-concept with an emphasis on feasibility and safety rather than real-time implementation, the proposed framework lays the groundwork for real-time applications, marking a step in solving all edge cases required to reach fully automated vehicles.
Lower tire-road friction limits the available tire force required for obstacle avoidance. Properly modeling these tire friction limits is particularly important in dynamic and uncertain environments to adequately account for the changing environment responsively. Moreover, addressing perception uncertainties and modeling low-friction conditions individually can significantly increase computational demands which poses challenges to achieve real-time performance required for real-world implementation. Therefore, this thesis jointly considers perception uncertainties and low tire-road friction conditions, accounting for their interacting effects. This is accomplished by addressing the following research question: "How can perception uncertainties be effectively integrated into motion-planning models for obstacle avoidance to improve performance and safety of automated vehicles in various road conditions?"
To address this question, a grid-based stochastic model predictive control framework is extended, implementing a non-linear bicycle model and a Fiala tire model (brush model) to consider realistic vehicle capabilities in low-friction conditions. Grid-based stochastic model predictive control reduces the uncertain obstacle environment into a set of linear constraints, utilizing an occupancy probability grid to effectively consider perception uncertainties. While the developed framework is presented as a proof-of-concept with a focus on safety and feasibility over real-time implementation, computational efficiency is not overlooked. By reformulating the constraints, the uncertainties are effectively accounted for while also reducing the optimization time by simplifying the probabilistic obstacle space to a deterministic convex region. If the nominal reformulation fails, a novel back-up method generates a conservative back-up set of constraints improving the safety and feasibility of the method. Two different back-up strategies are proposed, providing a trade-off between accuracy and computational effort.
To evaluate the contributions, three simulations were conducted. The first comparing the non-linear bicycle model and Fiala tire model to more simplistic models at various tire-road friction coefficients, highlighting the improvement in control of the proposed model in low-friction conditions. The second simulation evaluated the performance of the proposed back-up methods and environment representation by simulating tight scenarios that would fail using only the nominal approach. Feasibility rates increased compared to the baseline back-up method (43.8%) with feasibility rates of 62.5% for the precomputed back-up method and 75.0% for the current-state back-up method. These simulation results demonstrate that the capability of the proposed framework to compute feasible solutions and that the framework is able to compute valid hulls that are safe when the nominal approach fails to reformulate the constraints. The final simulation evaluated the performance of the complete proposed method by simulating both tight scenarios requiring a back-up method as well as various friction levels. These simulations demonstrated that the proposed framework is effective at lower friction levels, achieving high feasibility rates of 87.5% for the current-state back-up method, and 91.6% for the precomputed back-up method. This high accuracy is particularly promising for real-time applications using the precomputed back-up strategy, since this method leverages parallel computation of the back-up constraints.
This thesis demonstrated the effectiveness of the proposed extended grid-based stochastic model predictive control framework in considering perception uncertainties in low-friction road conditions. While presented as a proof-of-concept with an emphasis on feasibility and safety rather than real-time implementation, the proposed framework lays the groundwork for real-time applications, marking a step in solving all edge cases required to reach fully automated vehicles.
Towards Sickness-free Automated Driving
Control Algorithms for Motion Sickness Mitigation in Automated Vehicles and Enhanced Immersion in Driving Simulators
This thesis examines approaches for motion sickness reduction in automated driving applications. It focuses on motion planning methods designed to limit the motions that contribute to motion-sickness onset, and presents an algorithm that incorporates motion-sickness considerations into trajectory generation. Beyond real-world automated driving scenarios, the thesis also examines driving simulators as a platform for evaluating motion sickness oriented planning strategies. Methods for increasing simulator realism are explored to narrow the gap between simulated and on-road experiences. Mitigating motion sickness within the simulator itself is also examined, aimed at improving both simulator fidelity and the reliability of motion-sickness assessments.
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This thesis examines approaches for motion sickness reduction in automated driving applications. It focuses on motion planning methods designed to limit the motions that contribute to motion-sickness onset, and presents an algorithm that incorporates motion-sickness considerations into trajectory generation. Beyond real-world automated driving scenarios, the thesis also examines driving simulators as a platform for evaluating motion sickness oriented planning strategies. Methods for increasing simulator realism are explored to narrow the gap between simulated and on-road experiences. Mitigating motion sickness within the simulator itself is also examined, aimed at improving both simulator fidelity and the reliability of motion-sickness assessments.
Control of Evasive Manoeuvres for Automated Driving
Solving the Edge Cases
This thesis addresses the challenge of controlling automated vehicles performing evasive manoeuvres at the limit of handling. Special attention is paid to the development of nonlinear controllers, which can prioritise obstacle avoidance over path tracking objectives while considering vehicle stability constraints, to improve passenger safety. The thesis develops the entire pipeline for obstacle avoidance controllers, focusing on three aspects: vehicle state estimation, collision avoidance and control beyond the stable handling limits, e.g. drifting.
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This thesis addresses the challenge of controlling automated vehicles performing evasive manoeuvres at the limit of handling. Special attention is paid to the development of nonlinear controllers, which can prioritise obstacle avoidance over path tracking objectives while considering vehicle stability constraints, to improve passenger safety. The thesis develops the entire pipeline for obstacle avoidance controllers, focusing on three aspects: vehicle state estimation, collision avoidance and control beyond the stable handling limits, e.g. drifting.
Model Predictive Approaches for Automated Emergency Maneuvers
A Comparative Analysis of Hybridization for Vehicle Control
Model Predictive Control (MPC) is an effective reference tracking strategy for automated vehicle control, particularly useful during emergency evasive maneuvers such as double lane changes. This control method often requires a high-fidelity vehicle model to accurately capture nonlinearities and uncertainties, significantly increasing computational demand. Hybrid systems modeling frameworks have been developed to approximate these nonlinearities, thereby reducing computational complexities while maintaining satisfactory tracking performance. However, existing benchmarks that evaluate the impact of these hybrid approximations on tracking performance and computational demands are lacking. Establishing such comparative benchmarks is crucial for understanding how different levels of model complexity affect the overall efficiency and effectiveness of model predictive approaches in automated driving scenarios.
This research addresses this gap by presenting a comparative analysis of various levels of hybrid model complexity. It assesses their tracking performance and computational demand using both MPC formulation and Model Predictive Contouring Control (MPCC) formalism in different emergency maneuver scenarios.
Four hybrid approximations of the nonlinear single-track vehicle model with varying complexity levels are considered and employed as prediction models in both MPC and MPCC optimization problems. The closed-loop behavior of these control frameworks is simulated in double-lane change maneuver scenarios, evaluating tracking performance scenarios with varying levels of curvature. Additionally, variations in friction and velocity are evaluated in different scenarios to assess controller robustness to model uncertainty.
Results indicate that reducing the complexity of hybrid approximations can decrease computational demand, albeit at the expense of tracking performance. Moreover, MPC formalism offers a more robust approach to tracking performance and provides a feasible solution in a broader range of scenarios than the MPCC framework. By shedding light on the impact of different complexity levels for the hybrid approximation of the nonlinear model and the control optimization problem formulation, this work offers comprehensive guidelines for hybrid MPC applications for automated driving in emergency scenarios. ...
This research addresses this gap by presenting a comparative analysis of various levels of hybrid model complexity. It assesses their tracking performance and computational demand using both MPC formulation and Model Predictive Contouring Control (MPCC) formalism in different emergency maneuver scenarios.
Four hybrid approximations of the nonlinear single-track vehicle model with varying complexity levels are considered and employed as prediction models in both MPC and MPCC optimization problems. The closed-loop behavior of these control frameworks is simulated in double-lane change maneuver scenarios, evaluating tracking performance scenarios with varying levels of curvature. Additionally, variations in friction and velocity are evaluated in different scenarios to assess controller robustness to model uncertainty.
Results indicate that reducing the complexity of hybrid approximations can decrease computational demand, albeit at the expense of tracking performance. Moreover, MPC formalism offers a more robust approach to tracking performance and provides a feasible solution in a broader range of scenarios than the MPCC framework. By shedding light on the impact of different complexity levels for the hybrid approximation of the nonlinear model and the control optimization problem formulation, this work offers comprehensive guidelines for hybrid MPC applications for automated driving in emergency scenarios. ...
Model Predictive Control (MPC) is an effective reference tracking strategy for automated vehicle control, particularly useful during emergency evasive maneuvers such as double lane changes. This control method often requires a high-fidelity vehicle model to accurately capture nonlinearities and uncertainties, significantly increasing computational demand. Hybrid systems modeling frameworks have been developed to approximate these nonlinearities, thereby reducing computational complexities while maintaining satisfactory tracking performance. However, existing benchmarks that evaluate the impact of these hybrid approximations on tracking performance and computational demands are lacking. Establishing such comparative benchmarks is crucial for understanding how different levels of model complexity affect the overall efficiency and effectiveness of model predictive approaches in automated driving scenarios.
This research addresses this gap by presenting a comparative analysis of various levels of hybrid model complexity. It assesses their tracking performance and computational demand using both MPC formulation and Model Predictive Contouring Control (MPCC) formalism in different emergency maneuver scenarios.
Four hybrid approximations of the nonlinear single-track vehicle model with varying complexity levels are considered and employed as prediction models in both MPC and MPCC optimization problems. The closed-loop behavior of these control frameworks is simulated in double-lane change maneuver scenarios, evaluating tracking performance scenarios with varying levels of curvature. Additionally, variations in friction and velocity are evaluated in different scenarios to assess controller robustness to model uncertainty.
Results indicate that reducing the complexity of hybrid approximations can decrease computational demand, albeit at the expense of tracking performance. Moreover, MPC formalism offers a more robust approach to tracking performance and provides a feasible solution in a broader range of scenarios than the MPCC framework. By shedding light on the impact of different complexity levels for the hybrid approximation of the nonlinear model and the control optimization problem formulation, this work offers comprehensive guidelines for hybrid MPC applications for automated driving in emergency scenarios.
This research addresses this gap by presenting a comparative analysis of various levels of hybrid model complexity. It assesses their tracking performance and computational demand using both MPC formulation and Model Predictive Contouring Control (MPCC) formalism in different emergency maneuver scenarios.
Four hybrid approximations of the nonlinear single-track vehicle model with varying complexity levels are considered and employed as prediction models in both MPC and MPCC optimization problems. The closed-loop behavior of these control frameworks is simulated in double-lane change maneuver scenarios, evaluating tracking performance scenarios with varying levels of curvature. Additionally, variations in friction and velocity are evaluated in different scenarios to assess controller robustness to model uncertainty.
Results indicate that reducing the complexity of hybrid approximations can decrease computational demand, albeit at the expense of tracking performance. Moreover, MPC formalism offers a more robust approach to tracking performance and provides a feasible solution in a broader range of scenarios than the MPCC framework. By shedding light on the impact of different complexity levels for the hybrid approximation of the nonlinear model and the control optimization problem formulation, this work offers comprehensive guidelines for hybrid MPC applications for automated driving in emergency scenarios.
As part of the global trend towards carbon emission reduction, the road transport sector is motivated to produce solutions that reduce the use of fossil fuels. One of the posed solutions for this purpose is the so-called ‘e-axle’ for freight truck trailers. As a world-leading production company of air suspension systems for truck trailers, VDL Weweler B.V. wishes to provide suspension systems that can be used in combination with e-axles. An e-axle has as much as double the mass compared to a passive axle currently used in truck trailers. As the axle is part of the unsprung mass of the system, the increase will have a significant impact on the dynamics and forces within the air suspension system. Getting a better understanding of these impacts will help formulate the requirements that using an e-axle will pose to the suspension systems produced by VDL Weweler. Of particular interest is the impact on the durability of the trailing arms as a consequence of doubling the unsprung mass. The goal of this research project is therefore to quantify the stresses and the associated durability impacts as a first step towards finding the necessity of design adaptations.
During experiments conducted as part of this research project, an axle was fitted with a 500 kg ballast to reproduce the increased unsprung mass of an e-axle. From data analysis strain gauge data was concluded that the durability of the trailing arm could be reduced with at least 30.8\%. A significant impact to the dynamics of the system was observed in various dynamic indicators of the vehicle. A significant increase in the suspension deflections, which have a direct relation with the load on the trailing arm was observed. Furthermore, a significant reduction of the natural bounce frequency of the axle and higher transmission of axle vibrations to the chassis were found.
A novel kinematic vehicle model was derived to allow the creation of a digital twin model of the RRT durability test. A model validation was conducted to establish the ability of the vehicle and component models to reproduce the RRT. The numerical model proved to strongly reproduce the axle motions and suspension deflections in terms of Power Spectral Density of the dynamic response. The impact of unsprung mass was modelled with high accuracy in relative terms.
Based on the results of this research project, a significant impact to the durability of the trailing arm is expected. Furthermore, a solid start was made in the development of a model that could predict the durability impact of varying system parameters in an air suspension system of a truck trailer. ...
During experiments conducted as part of this research project, an axle was fitted with a 500 kg ballast to reproduce the increased unsprung mass of an e-axle. From data analysis strain gauge data was concluded that the durability of the trailing arm could be reduced with at least 30.8\%. A significant impact to the dynamics of the system was observed in various dynamic indicators of the vehicle. A significant increase in the suspension deflections, which have a direct relation with the load on the trailing arm was observed. Furthermore, a significant reduction of the natural bounce frequency of the axle and higher transmission of axle vibrations to the chassis were found.
A novel kinematic vehicle model was derived to allow the creation of a digital twin model of the RRT durability test. A model validation was conducted to establish the ability of the vehicle and component models to reproduce the RRT. The numerical model proved to strongly reproduce the axle motions and suspension deflections in terms of Power Spectral Density of the dynamic response. The impact of unsprung mass was modelled with high accuracy in relative terms.
Based on the results of this research project, a significant impact to the durability of the trailing arm is expected. Furthermore, a solid start was made in the development of a model that could predict the durability impact of varying system parameters in an air suspension system of a truck trailer. ...
As part of the global trend towards carbon emission reduction, the road transport sector is motivated to produce solutions that reduce the use of fossil fuels. One of the posed solutions for this purpose is the so-called ‘e-axle’ for freight truck trailers. As a world-leading production company of air suspension systems for truck trailers, VDL Weweler B.V. wishes to provide suspension systems that can be used in combination with e-axles. An e-axle has as much as double the mass compared to a passive axle currently used in truck trailers. As the axle is part of the unsprung mass of the system, the increase will have a significant impact on the dynamics and forces within the air suspension system. Getting a better understanding of these impacts will help formulate the requirements that using an e-axle will pose to the suspension systems produced by VDL Weweler. Of particular interest is the impact on the durability of the trailing arms as a consequence of doubling the unsprung mass. The goal of this research project is therefore to quantify the stresses and the associated durability impacts as a first step towards finding the necessity of design adaptations.
During experiments conducted as part of this research project, an axle was fitted with a 500 kg ballast to reproduce the increased unsprung mass of an e-axle. From data analysis strain gauge data was concluded that the durability of the trailing arm could be reduced with at least 30.8\%. A significant impact to the dynamics of the system was observed in various dynamic indicators of the vehicle. A significant increase in the suspension deflections, which have a direct relation with the load on the trailing arm was observed. Furthermore, a significant reduction of the natural bounce frequency of the axle and higher transmission of axle vibrations to the chassis were found.
A novel kinematic vehicle model was derived to allow the creation of a digital twin model of the RRT durability test. A model validation was conducted to establish the ability of the vehicle and component models to reproduce the RRT. The numerical model proved to strongly reproduce the axle motions and suspension deflections in terms of Power Spectral Density of the dynamic response. The impact of unsprung mass was modelled with high accuracy in relative terms.
Based on the results of this research project, a significant impact to the durability of the trailing arm is expected. Furthermore, a solid start was made in the development of a model that could predict the durability impact of varying system parameters in an air suspension system of a truck trailer.
During experiments conducted as part of this research project, an axle was fitted with a 500 kg ballast to reproduce the increased unsprung mass of an e-axle. From data analysis strain gauge data was concluded that the durability of the trailing arm could be reduced with at least 30.8\%. A significant impact to the dynamics of the system was observed in various dynamic indicators of the vehicle. A significant increase in the suspension deflections, which have a direct relation with the load on the trailing arm was observed. Furthermore, a significant reduction of the natural bounce frequency of the axle and higher transmission of axle vibrations to the chassis were found.
A novel kinematic vehicle model was derived to allow the creation of a digital twin model of the RRT durability test. A model validation was conducted to establish the ability of the vehicle and component models to reproduce the RRT. The numerical model proved to strongly reproduce the axle motions and suspension deflections in terms of Power Spectral Density of the dynamic response. The impact of unsprung mass was modelled with high accuracy in relative terms.
Based on the results of this research project, a significant impact to the durability of the trailing arm is expected. Furthermore, a solid start was made in the development of a model that could predict the durability impact of varying system parameters in an air suspension system of a truck trailer.
Model-Based-Control for Trajectory Tracking with a Mecanum Wheeled Vehicle
A performance comparison between kinematic and dynamic model-based control
This research investigates the benefits of using a trajectory tracking controller based on a dynamic model for a four-Mecanum-wheeled vehicle (FMWV) over a kinematic-model-based controller. An FMWV was designed and built, incorporating both hardware and software components. Two Linear Quadratic Regulators (LQRs) based on kinematic and dynamic models were implemented. The dynamic model includes friction estimation, while the kinematic model assumes a no-slip condition. Simulation results indicate that the dynamic model reduces overshoot and improves trajectory tracking in low-friction scenarios compared to the kinematic model. High-friction scenarios show comparable performance for both controllers. Experimental results align with simulations, though some deviations highlight areas for further improvement. Overall, the dynamic-model-based controller demonstrates superior performance in low-friction conditions, reducing translational root mean square error (RMSE) and maximum path deviation (MaxE).
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This research investigates the benefits of using a trajectory tracking controller based on a dynamic model for a four-Mecanum-wheeled vehicle (FMWV) over a kinematic-model-based controller. An FMWV was designed and built, incorporating both hardware and software components. Two Linear Quadratic Regulators (LQRs) based on kinematic and dynamic models were implemented. The dynamic model includes friction estimation, while the kinematic model assumes a no-slip condition. Simulation results indicate that the dynamic model reduces overshoot and improves trajectory tracking in low-friction scenarios compared to the kinematic model. High-friction scenarios show comparable performance for both controllers. Experimental results align with simulations, though some deviations highlight areas for further improvement. Overall, the dynamic-model-based controller demonstrates superior performance in low-friction conditions, reducing translational root mean square error (RMSE) and maximum path deviation (MaxE).
Automated control beyond the limits of friction
A nonlinear model predictive approach for with production vehicle experimental verification
Abstract—This paper proposes a Nonlinear Model Predictive Control (NMPC) application for automated drifting, with exper- imental verification on a standard production vehicle, without hardware modifications. The controller stabilizes the vehicle on a high sideslip angle, which implies an equilibrium condition beyond tyre friction limits. The proposed control strategy shows feasible results that succesfully brings the vehicle towards a high sideslip state, for a variety of drifting scenarios. Simulations show that the control structure is able to sustain an automated drift along a desired path, with maximal lateral path deviation of 1 meter. An experimental implementation on a production vehicle testbench without hardware modifications has shown feasible control of bringing the vehicle into a high sideslip state, for both low- and high μ situations.
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Abstract—This paper proposes a Nonlinear Model Predictive Control (NMPC) application for automated drifting, with exper- imental verification on a standard production vehicle, without hardware modifications. The controller stabilizes the vehicle on a high sideslip angle, which implies an equilibrium condition beyond tyre friction limits. The proposed control strategy shows feasible results that succesfully brings the vehicle towards a high sideslip state, for a variety of drifting scenarios. Simulations show that the control structure is able to sustain an automated drift along a desired path, with maximal lateral path deviation of 1 meter. An experimental implementation on a production vehicle testbench without hardware modifications has shown feasible control of bringing the vehicle into a high sideslip state, for both low- and high μ situations.
Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload and increased road safety. Considering a driver model in the assist helps increase user acceptance. Therefore, we propose a torque-based hybrid controller for a human-centric haptic shared Lane Keeping Assist, pairing a data-driven driver model with a model-based controller to foster the collaboration between the driver and assist. First, the driver's non-linear steering wheel torque behaviour is modelled and predicted using a Bidirectional Long Short-Term Memory network with an accuracy ≥72.4% and a smoothness ≥0.85Nm/s over a 0.4s prediction horizon. Second, a Model Predictive Controller with a linear bicycle and steering model is developed, where it utilises the driver model's predictions as a time-varying reference. We developed three human-centric controllers for comparison and used a state-of-the-art commercial solution as the baseline controller. The experiments were performed in Toyota Motor Europe's fixed-base driving simulator, where 15 participants tested and evaluated the four controllers. The results show a 113.1% increase in collaborative ratio while maintaining a similar path tracking performance compared to the baseline.
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Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload and increased road safety. Considering a driver model in the assist helps increase user acceptance. Therefore, we propose a torque-based hybrid controller for a human-centric haptic shared Lane Keeping Assist, pairing a data-driven driver model with a model-based controller to foster the collaboration between the driver and assist. First, the driver's non-linear steering wheel torque behaviour is modelled and predicted using a Bidirectional Long Short-Term Memory network with an accuracy ≥72.4% and a smoothness ≥0.85Nm/s over a 0.4s prediction horizon. Second, a Model Predictive Controller with a linear bicycle and steering model is developed, where it utilises the driver model's predictions as a time-varying reference. We developed three human-centric controllers for comparison and used a state-of-the-art commercial solution as the baseline controller. The experiments were performed in Toyota Motor Europe's fixed-base driving simulator, where 15 participants tested and evaluated the four controllers. The results show a 113.1% increase in collaborative ratio while maintaining a similar path tracking performance compared to the baseline.
In the coming decade, Advanced Driver Assistance Systems (ADAS) will play a crucial role in improving traffic safety within the European Union (EU). Notably, the European Commission mandates that from July 2024, all new vehicles must be equipped with Lane Keeping Assistance (LKA) systems. LKA systems provide force feedback to drivers when vehicles cross lane markings, employing cameras, radar, or LiDAR systems for lane detection.
To optimize LKA performance, this research focuses on understanding the influence of lane marking properties and adverse scenarios on detection ability.
Field Test and Setup:
A field test was conducted at a track in Lelystad, featuring various state-of-the-art lane markings: an old white paint lane marking as a reference, and three new lane markings (two tape types from 3M and one cold spray plast from Triflex). Lane markings were tested under dry and wet conditions.
Data Collection:
Measurements included luminance coefficient (Qd) and retroreflectivity values (Rl for dry and Rw for wet) of lane markings and asphalt, along with contrast ratios. Test runs were conducted during sunset, in complete darkness, and under different scenarios involving oncoming traffic and street lights.
Analysis and Findings:
After analyzing 414 valid runs, key findings were identified:
Lane markings are 3.3 times more likely to be detected in dry versus wet conditions.
Driving towards a light source reduces detection likelihood by 4.5 to 5 times compared to driving away from the light.
Higher wet retroreflectivity (Rw) improves LKA system performance.
Oncoming traffic with main beam headlights reduces detection likelihood significantly.
Bright light sources negatively affect contrast in camera-detected images.
New lane markings outperform old white paint markings by 2.1 to 4.3 times in detection likelihood.
Implications and Recommendations:
The study confirms the importance of lane marking detectability for LKA systems reliant on cameras. Enhancing retroreflection and contrast improves detection ratios, thereby enhancing traffic safety and potentially preventing accidents. Further research is recommended on the impact of oncoming traffic and street lights, including light type and brightness.
For future lane marking developments, testing under adverse conditions with varying light sources and wet surfaces is advised to optimize LKA system performance and ensure safety on the roads. ...
To optimize LKA performance, this research focuses on understanding the influence of lane marking properties and adverse scenarios on detection ability.
Field Test and Setup:
A field test was conducted at a track in Lelystad, featuring various state-of-the-art lane markings: an old white paint lane marking as a reference, and three new lane markings (two tape types from 3M and one cold spray plast from Triflex). Lane markings were tested under dry and wet conditions.
Data Collection:
Measurements included luminance coefficient (Qd) and retroreflectivity values (Rl for dry and Rw for wet) of lane markings and asphalt, along with contrast ratios. Test runs were conducted during sunset, in complete darkness, and under different scenarios involving oncoming traffic and street lights.
Analysis and Findings:
After analyzing 414 valid runs, key findings were identified:
Lane markings are 3.3 times more likely to be detected in dry versus wet conditions.
Driving towards a light source reduces detection likelihood by 4.5 to 5 times compared to driving away from the light.
Higher wet retroreflectivity (Rw) improves LKA system performance.
Oncoming traffic with main beam headlights reduces detection likelihood significantly.
Bright light sources negatively affect contrast in camera-detected images.
New lane markings outperform old white paint markings by 2.1 to 4.3 times in detection likelihood.
Implications and Recommendations:
The study confirms the importance of lane marking detectability for LKA systems reliant on cameras. Enhancing retroreflection and contrast improves detection ratios, thereby enhancing traffic safety and potentially preventing accidents. Further research is recommended on the impact of oncoming traffic and street lights, including light type and brightness.
For future lane marking developments, testing under adverse conditions with varying light sources and wet surfaces is advised to optimize LKA system performance and ensure safety on the roads. ...
In the coming decade, Advanced Driver Assistance Systems (ADAS) will play a crucial role in improving traffic safety within the European Union (EU). Notably, the European Commission mandates that from July 2024, all new vehicles must be equipped with Lane Keeping Assistance (LKA) systems. LKA systems provide force feedback to drivers when vehicles cross lane markings, employing cameras, radar, or LiDAR systems for lane detection.
To optimize LKA performance, this research focuses on understanding the influence of lane marking properties and adverse scenarios on detection ability.
Field Test and Setup:
A field test was conducted at a track in Lelystad, featuring various state-of-the-art lane markings: an old white paint lane marking as a reference, and three new lane markings (two tape types from 3M and one cold spray plast from Triflex). Lane markings were tested under dry and wet conditions.
Data Collection:
Measurements included luminance coefficient (Qd) and retroreflectivity values (Rl for dry and Rw for wet) of lane markings and asphalt, along with contrast ratios. Test runs were conducted during sunset, in complete darkness, and under different scenarios involving oncoming traffic and street lights.
Analysis and Findings:
After analyzing 414 valid runs, key findings were identified:
Lane markings are 3.3 times more likely to be detected in dry versus wet conditions.
Driving towards a light source reduces detection likelihood by 4.5 to 5 times compared to driving away from the light.
Higher wet retroreflectivity (Rw) improves LKA system performance.
Oncoming traffic with main beam headlights reduces detection likelihood significantly.
Bright light sources negatively affect contrast in camera-detected images.
New lane markings outperform old white paint markings by 2.1 to 4.3 times in detection likelihood.
Implications and Recommendations:
The study confirms the importance of lane marking detectability for LKA systems reliant on cameras. Enhancing retroreflection and contrast improves detection ratios, thereby enhancing traffic safety and potentially preventing accidents. Further research is recommended on the impact of oncoming traffic and street lights, including light type and brightness.
For future lane marking developments, testing under adverse conditions with varying light sources and wet surfaces is advised to optimize LKA system performance and ensure safety on the roads.
To optimize LKA performance, this research focuses on understanding the influence of lane marking properties and adverse scenarios on detection ability.
Field Test and Setup:
A field test was conducted at a track in Lelystad, featuring various state-of-the-art lane markings: an old white paint lane marking as a reference, and three new lane markings (two tape types from 3M and one cold spray plast from Triflex). Lane markings were tested under dry and wet conditions.
Data Collection:
Measurements included luminance coefficient (Qd) and retroreflectivity values (Rl for dry and Rw for wet) of lane markings and asphalt, along with contrast ratios. Test runs were conducted during sunset, in complete darkness, and under different scenarios involving oncoming traffic and street lights.
Analysis and Findings:
After analyzing 414 valid runs, key findings were identified:
Lane markings are 3.3 times more likely to be detected in dry versus wet conditions.
Driving towards a light source reduces detection likelihood by 4.5 to 5 times compared to driving away from the light.
Higher wet retroreflectivity (Rw) improves LKA system performance.
Oncoming traffic with main beam headlights reduces detection likelihood significantly.
Bright light sources negatively affect contrast in camera-detected images.
New lane markings outperform old white paint markings by 2.1 to 4.3 times in detection likelihood.
Implications and Recommendations:
The study confirms the importance of lane marking detectability for LKA systems reliant on cameras. Enhancing retroreflection and contrast improves detection ratios, thereby enhancing traffic safety and potentially preventing accidents. Further research is recommended on the impact of oncoming traffic and street lights, including light type and brightness.
For future lane marking developments, testing under adverse conditions with varying light sources and wet surfaces is advised to optimize LKA system performance and ensure safety on the roads.
The speed of the unsprung mass has a direct effect on the comfort of the ride. Too much unsprung mass movement and high speeds can make the ride rough and uncomfortable by sending vibrations and other disturbances to the people inside the car. By getting an accurate estimate of the speed of the unsprung mass, the suspension system can adjust the damping forces to better absorb uneven road surfaces, reducing vibrations and making the ride more comfortable. In active suspension, controllable actuators are installed between the unsprung and sprung mass. However, incorporating sensors into suspension introduces complexity to the system and increases the vehicle’s mass, production, and maintenance costs. Further, there are unmeasurable or complex quantities which make sensing more difficult. The use of virtual sensing offers a useful opportunity for estimating suspension parameters that are not directly measurable or complex to measure. This research aims to reduce the complexity of a system through the development of virtual sensor using parameters that can be easily measured. This developed virtual sensor is then incorporated in a suspension controller to estimate the effect on the ride comfort of a vehicle.
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The speed of the unsprung mass has a direct effect on the comfort of the ride. Too much unsprung mass movement and high speeds can make the ride rough and uncomfortable by sending vibrations and other disturbances to the people inside the car. By getting an accurate estimate of the speed of the unsprung mass, the suspension system can adjust the damping forces to better absorb uneven road surfaces, reducing vibrations and making the ride more comfortable. In active suspension, controllable actuators are installed between the unsprung and sprung mass. However, incorporating sensors into suspension introduces complexity to the system and increases the vehicle’s mass, production, and maintenance costs. Further, there are unmeasurable or complex quantities which make sensing more difficult. The use of virtual sensing offers a useful opportunity for estimating suspension parameters that are not directly measurable or complex to measure. This research aims to reduce the complexity of a system through the development of virtual sensor using parameters that can be easily measured. This developed virtual sensor is then incorporated in a suspension controller to estimate the effect on the ride comfort of a vehicle.
This thesis introduces a novel model predictive controller (MPC) that integrates both torque vectoring and path following into one controller. Due to a need to improve vehicle safety, systems are being developed in order to improve vehicle handling. One system that is able to improve the vehicle handling is torque vectoring (TV). With torque vectoring, the magnitude and the direction of torque can be controlled by either applying the motor or brake torques. Additionally, in order to eliminate human error as a cause of accident, automated drive (AD) vehicles are being developed. A key task for AD vehicles is to perform path following (PF), where the vehicle follows a predetermined reference path generated by path planning.
Beforehand, these tasks were performed by separate controllers, where one controller performed path following and the other controller focused on torque vectoring. The disadvantage of this method is that it leads to sub-optimal results as both controllers have opposing objectives. The TV controller is able to decrease the steering angle in order to improve the vehicle handling, whereas the PF controller could require a higher steering angle in order to follow the path. By integrating both tasks, the novel
controller is able to optimise the control output such that both objectives are achieved.
The use of model predictive control strategies with TV have been studied and its ability to deal with hard constraints, while decreasing the state errors and control input, makes it a suitable choice to use it for TV. When the MPC strategy is compared to more common control strategies it is clear that the MPC TV algorithm provides better results in terms of responsiveness, lateral acceleration and vehicle handling. Furthermore, due to its ability to integrate multiple applications and its ability to handle a greater level of complexity, a nonlinear model predictive control (NMPC) formulation will be used to perform both torque vectoring and path following.
In order to test the new MPC controller, benchmark controllers have been created for comparison. The benchmark controllers are two controllers that are able to perform torque vectoring by generating a corrective yaw moment to follow the yaw rate reference. The torques are then allocated based on the size and direction of the corrective yaw moment. Then, the NMPC controller that performs path following by using both the steering angle and torques as an input will be compared to a controller that is able to perform path following by using the steering angle as an input.
The controllers are compared by using the sine with dwell test and the double lane change manoeuvre. These manoeuvres are used to test the lateral performance, vehicle handling, responsiveness and tracking performance of the vehicles. Key performance indicators (KPI) are used in order to evaluate the results regarding tracking performance and the vehicle handling.
The results show that the NMPC controller has an increase in performance regarding both path following and vehicle handling. When compared to the benchmark torque vectoring controller, the vehicle handling is increased by 5% and the lateral performance is increased by 6 %. Additionally, compared to the path following controller, by adding torque vectoring, the NMPC controller has improved the path
following by 4 %, the vehicle handling has been improved by 5 % and the responsiveness has been improved by 11 %. ...
Beforehand, these tasks were performed by separate controllers, where one controller performed path following and the other controller focused on torque vectoring. The disadvantage of this method is that it leads to sub-optimal results as both controllers have opposing objectives. The TV controller is able to decrease the steering angle in order to improve the vehicle handling, whereas the PF controller could require a higher steering angle in order to follow the path. By integrating both tasks, the novel
controller is able to optimise the control output such that both objectives are achieved.
The use of model predictive control strategies with TV have been studied and its ability to deal with hard constraints, while decreasing the state errors and control input, makes it a suitable choice to use it for TV. When the MPC strategy is compared to more common control strategies it is clear that the MPC TV algorithm provides better results in terms of responsiveness, lateral acceleration and vehicle handling. Furthermore, due to its ability to integrate multiple applications and its ability to handle a greater level of complexity, a nonlinear model predictive control (NMPC) formulation will be used to perform both torque vectoring and path following.
In order to test the new MPC controller, benchmark controllers have been created for comparison. The benchmark controllers are two controllers that are able to perform torque vectoring by generating a corrective yaw moment to follow the yaw rate reference. The torques are then allocated based on the size and direction of the corrective yaw moment. Then, the NMPC controller that performs path following by using both the steering angle and torques as an input will be compared to a controller that is able to perform path following by using the steering angle as an input.
The controllers are compared by using the sine with dwell test and the double lane change manoeuvre. These manoeuvres are used to test the lateral performance, vehicle handling, responsiveness and tracking performance of the vehicles. Key performance indicators (KPI) are used in order to evaluate the results regarding tracking performance and the vehicle handling.
The results show that the NMPC controller has an increase in performance regarding both path following and vehicle handling. When compared to the benchmark torque vectoring controller, the vehicle handling is increased by 5% and the lateral performance is increased by 6 %. Additionally, compared to the path following controller, by adding torque vectoring, the NMPC controller has improved the path
following by 4 %, the vehicle handling has been improved by 5 % and the responsiveness has been improved by 11 %. ...
This thesis introduces a novel model predictive controller (MPC) that integrates both torque vectoring and path following into one controller. Due to a need to improve vehicle safety, systems are being developed in order to improve vehicle handling. One system that is able to improve the vehicle handling is torque vectoring (TV). With torque vectoring, the magnitude and the direction of torque can be controlled by either applying the motor or brake torques. Additionally, in order to eliminate human error as a cause of accident, automated drive (AD) vehicles are being developed. A key task for AD vehicles is to perform path following (PF), where the vehicle follows a predetermined reference path generated by path planning.
Beforehand, these tasks were performed by separate controllers, where one controller performed path following and the other controller focused on torque vectoring. The disadvantage of this method is that it leads to sub-optimal results as both controllers have opposing objectives. The TV controller is able to decrease the steering angle in order to improve the vehicle handling, whereas the PF controller could require a higher steering angle in order to follow the path. By integrating both tasks, the novel
controller is able to optimise the control output such that both objectives are achieved.
The use of model predictive control strategies with TV have been studied and its ability to deal with hard constraints, while decreasing the state errors and control input, makes it a suitable choice to use it for TV. When the MPC strategy is compared to more common control strategies it is clear that the MPC TV algorithm provides better results in terms of responsiveness, lateral acceleration and vehicle handling. Furthermore, due to its ability to integrate multiple applications and its ability to handle a greater level of complexity, a nonlinear model predictive control (NMPC) formulation will be used to perform both torque vectoring and path following.
In order to test the new MPC controller, benchmark controllers have been created for comparison. The benchmark controllers are two controllers that are able to perform torque vectoring by generating a corrective yaw moment to follow the yaw rate reference. The torques are then allocated based on the size and direction of the corrective yaw moment. Then, the NMPC controller that performs path following by using both the steering angle and torques as an input will be compared to a controller that is able to perform path following by using the steering angle as an input.
The controllers are compared by using the sine with dwell test and the double lane change manoeuvre. These manoeuvres are used to test the lateral performance, vehicle handling, responsiveness and tracking performance of the vehicles. Key performance indicators (KPI) are used in order to evaluate the results regarding tracking performance and the vehicle handling.
The results show that the NMPC controller has an increase in performance regarding both path following and vehicle handling. When compared to the benchmark torque vectoring controller, the vehicle handling is increased by 5% and the lateral performance is increased by 6 %. Additionally, compared to the path following controller, by adding torque vectoring, the NMPC controller has improved the path
following by 4 %, the vehicle handling has been improved by 5 % and the responsiveness has been improved by 11 %.
Beforehand, these tasks were performed by separate controllers, where one controller performed path following and the other controller focused on torque vectoring. The disadvantage of this method is that it leads to sub-optimal results as both controllers have opposing objectives. The TV controller is able to decrease the steering angle in order to improve the vehicle handling, whereas the PF controller could require a higher steering angle in order to follow the path. By integrating both tasks, the novel
controller is able to optimise the control output such that both objectives are achieved.
The use of model predictive control strategies with TV have been studied and its ability to deal with hard constraints, while decreasing the state errors and control input, makes it a suitable choice to use it for TV. When the MPC strategy is compared to more common control strategies it is clear that the MPC TV algorithm provides better results in terms of responsiveness, lateral acceleration and vehicle handling. Furthermore, due to its ability to integrate multiple applications and its ability to handle a greater level of complexity, a nonlinear model predictive control (NMPC) formulation will be used to perform both torque vectoring and path following.
In order to test the new MPC controller, benchmark controllers have been created for comparison. The benchmark controllers are two controllers that are able to perform torque vectoring by generating a corrective yaw moment to follow the yaw rate reference. The torques are then allocated based on the size and direction of the corrective yaw moment. Then, the NMPC controller that performs path following by using both the steering angle and torques as an input will be compared to a controller that is able to perform path following by using the steering angle as an input.
The controllers are compared by using the sine with dwell test and the double lane change manoeuvre. These manoeuvres are used to test the lateral performance, vehicle handling, responsiveness and tracking performance of the vehicles. Key performance indicators (KPI) are used in order to evaluate the results regarding tracking performance and the vehicle handling.
The results show that the NMPC controller has an increase in performance regarding both path following and vehicle handling. When compared to the benchmark torque vectoring controller, the vehicle handling is increased by 5% and the lateral performance is increased by 6 %. Additionally, compared to the path following controller, by adding torque vectoring, the NMPC controller has improved the path
following by 4 %, the vehicle handling has been improved by 5 % and the responsiveness has been improved by 11 %.
Comparing automated vehicles with human drivers
Improving motion comfort with motion planning and suspension control
This dissertation is dedicated to understanding the potential of improving the motion comfort of automated vehicles and explores multiple options that serve this purpose. Comfort is usually prioritized behind factors such as safety and efficiency but is nevertheless influential to the acceptance of automated vehicles. The goal of enhancing motion comfort overlaps with the need to overcome challenges brought by the motion sickness phenomenon. Motion sickness is found to impact a significant portion of travelers in all types of transport. It tends to develop faster among occupants who are not engaged in the driving task. Its symptoms can cause difficulties for non-driving-related tasks (NDRTs) to be performed effectively by the passengers. Therefore, a part of the research in this dissertation is directed specifically toward mitigating motion sickness in automated vehicles...
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This dissertation is dedicated to understanding the potential of improving the motion comfort of automated vehicles and explores multiple options that serve this purpose. Comfort is usually prioritized behind factors such as safety and efficiency but is nevertheless influential to the acceptance of automated vehicles. The goal of enhancing motion comfort overlaps with the need to overcome challenges brought by the motion sickness phenomenon. Motion sickness is found to impact a significant portion of travelers in all types of transport. It tends to develop faster among occupants who are not engaged in the driving task. Its symptoms can cause difficulties for non-driving-related tasks (NDRTs) to be performed effectively by the passengers. Therefore, a part of the research in this dissertation is directed specifically toward mitigating motion sickness in automated vehicles...
This thesis describes the concept of a reconfigurable wheelbase for a car as a way to improve agility at higher velocities. Altering the longitudinal position of each wheel with respect to the centre of gravity, leads to a change in lateral wheel forces. This affects the car's yaw rate, therefore making it possible to influence its agility. Available literature has shown that this subject is still quite weakly investigated, which makes it an interesting topic of research. To investigate this influence of actively reconfiguring the wheelbase, simulations were done using IPG Carmaker together with MATLAB/Simulink, during which a simplified vehicle model of a Toyota Camry was used. A proportional derivative controller combined with control allocation was designed to regulate the reconfigurable wheelbase system. It features a reference generator that returns a desired yaw rate and activation logic which ensures that the controller is only activated in a velocity range of 60 km/h to 110 km/h and when a steering input is given. Furthermore, a benchmark study is designed, using a fuzzy logic controller that is based on a simplified version of one presented by Soltani et al. (2017). This controller uses the same reference generator and activation logic for consistency. The rules and membership functions of the fuzzy logic block are adopted from the mentioned paper. The performance of both these controllers and a baseline vehicle without any control, are evaluated for a step steer manoeuvre, an increasing circle manoeuvre and a double lane change, at different velocities and/or steering amplitudes. Based on the results of these tests, it is shown that both controllers are able to influence the lateral dynamics positively during most of these manoeuvres and are therefore able to improve the vehicle's agility in the velocity range in which the controller is active.
...
This thesis describes the concept of a reconfigurable wheelbase for a car as a way to improve agility at higher velocities. Altering the longitudinal position of each wheel with respect to the centre of gravity, leads to a change in lateral wheel forces. This affects the car's yaw rate, therefore making it possible to influence its agility. Available literature has shown that this subject is still quite weakly investigated, which makes it an interesting topic of research. To investigate this influence of actively reconfiguring the wheelbase, simulations were done using IPG Carmaker together with MATLAB/Simulink, during which a simplified vehicle model of a Toyota Camry was used. A proportional derivative controller combined with control allocation was designed to regulate the reconfigurable wheelbase system. It features a reference generator that returns a desired yaw rate and activation logic which ensures that the controller is only activated in a velocity range of 60 km/h to 110 km/h and when a steering input is given. Furthermore, a benchmark study is designed, using a fuzzy logic controller that is based on a simplified version of one presented by Soltani et al. (2017). This controller uses the same reference generator and activation logic for consistency. The rules and membership functions of the fuzzy logic block are adopted from the mentioned paper. The performance of both these controllers and a baseline vehicle without any control, are evaluated for a step steer manoeuvre, an increasing circle manoeuvre and a double lane change, at different velocities and/or steering amplitudes. Based on the results of these tests, it is shown that both controllers are able to influence the lateral dynamics positively during most of these manoeuvres and are therefore able to improve the vehicle's agility in the velocity range in which the controller is active.
Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host of possible benefits that automated vehicles propose to offer, and therefore affect their acceptance among potential users.
It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.
Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.
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It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.
Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.
...
Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host of possible benefits that automated vehicles propose to offer, and therefore affect their acceptance among potential users.
It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.
Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.
It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.
Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.
A vehicle that is travelling at high sideslip angles can still be controlled by drifting. Implemented into a vehicle, this phenomenon could lead to increased vehicle safety and performance. Additionally, it could lead to higher acceptance rates of autonomous driving.
In this work a three state vehicle model is used. Using this model, simulations are performed with varying vehicle parameters and tyre models. First, the mathematical descriptions of all used models are stated, after which phase plane representations for the identification of drift equilibria are elaborated on. Next, different steering characteristics and their influence on the drift equilibrium points are considered. These characteristics are achieved by varying the lateral rear tyre stiffness. The effect of
three different types of tyre models on the drift equilibria is discussed. The models that are considered are the linear tyre model, Dugoff tyre model and Magic Formula model.
In addition to this, the effects of the location of the centre of gravity, the vehicle mass and the cornering stiffness are investigated. Finally the outcome of the Dugoff tyre model and the Magic Formula model for construction of drift equilibria is shown and discussed.
Contrary to the Dugoff and Magic Formula model, no drift equilibria were found using the linear tyre model. Using the Dugoff model, drift equilibrium points were found for the understeering and neutral steering vehicle, whereas the oversteering vehicle showed drift equilibrium ranges. Finally, drift equilibria depend on vehicle parameters, showing change in behaviour when the latter are varied.
...
In this work a three state vehicle model is used. Using this model, simulations are performed with varying vehicle parameters and tyre models. First, the mathematical descriptions of all used models are stated, after which phase plane representations for the identification of drift equilibria are elaborated on. Next, different steering characteristics and their influence on the drift equilibrium points are considered. These characteristics are achieved by varying the lateral rear tyre stiffness. The effect of
three different types of tyre models on the drift equilibria is discussed. The models that are considered are the linear tyre model, Dugoff tyre model and Magic Formula model.
In addition to this, the effects of the location of the centre of gravity, the vehicle mass and the cornering stiffness are investigated. Finally the outcome of the Dugoff tyre model and the Magic Formula model for construction of drift equilibria is shown and discussed.
Contrary to the Dugoff and Magic Formula model, no drift equilibria were found using the linear tyre model. Using the Dugoff model, drift equilibrium points were found for the understeering and neutral steering vehicle, whereas the oversteering vehicle showed drift equilibrium ranges. Finally, drift equilibria depend on vehicle parameters, showing change in behaviour when the latter are varied.
...
A vehicle that is travelling at high sideslip angles can still be controlled by drifting. Implemented into a vehicle, this phenomenon could lead to increased vehicle safety and performance. Additionally, it could lead to higher acceptance rates of autonomous driving.
In this work a three state vehicle model is used. Using this model, simulations are performed with varying vehicle parameters and tyre models. First, the mathematical descriptions of all used models are stated, after which phase plane representations for the identification of drift equilibria are elaborated on. Next, different steering characteristics and their influence on the drift equilibrium points are considered. These characteristics are achieved by varying the lateral rear tyre stiffness. The effect of
three different types of tyre models on the drift equilibria is discussed. The models that are considered are the linear tyre model, Dugoff tyre model and Magic Formula model.
In addition to this, the effects of the location of the centre of gravity, the vehicle mass and the cornering stiffness are investigated. Finally the outcome of the Dugoff tyre model and the Magic Formula model for construction of drift equilibria is shown and discussed.
Contrary to the Dugoff and Magic Formula model, no drift equilibria were found using the linear tyre model. Using the Dugoff model, drift equilibrium points were found for the understeering and neutral steering vehicle, whereas the oversteering vehicle showed drift equilibrium ranges. Finally, drift equilibria depend on vehicle parameters, showing change in behaviour when the latter are varied.
In this work a three state vehicle model is used. Using this model, simulations are performed with varying vehicle parameters and tyre models. First, the mathematical descriptions of all used models are stated, after which phase plane representations for the identification of drift equilibria are elaborated on. Next, different steering characteristics and their influence on the drift equilibrium points are considered. These characteristics are achieved by varying the lateral rear tyre stiffness. The effect of
three different types of tyre models on the drift equilibria is discussed. The models that are considered are the linear tyre model, Dugoff tyre model and Magic Formula model.
In addition to this, the effects of the location of the centre of gravity, the vehicle mass and the cornering stiffness are investigated. Finally the outcome of the Dugoff tyre model and the Magic Formula model for construction of drift equilibria is shown and discussed.
Contrary to the Dugoff and Magic Formula model, no drift equilibria were found using the linear tyre model. Using the Dugoff model, drift equilibrium points were found for the understeering and neutral steering vehicle, whereas the oversteering vehicle showed drift equilibrium ranges. Finally, drift equilibria depend on vehicle parameters, showing change in behaviour when the latter are varied.
Driving simulators have been used in the automotive industry for many years now. They have been vastly employed for conducting tests in a safe, reproducible and controlled immersive virtual environment. The ability of the simulator to recreate the in-vehicle experience for the occupant is established through motion cueing algorithms. Such algorithms have consistently been developed with model predictive control (MPC) acting as the main control technique. Currently available MPC based methods either compute the optimal controller online or derive an explicit control law in an offline setting. These approaches limit the applicability of MPC for real-time applications due to online computational expense and offline memory storage issues.
This thesis report presents a solution to deal with issues of offline and online solving through a combined/hybrid approach. For this, explicit MPC is used to provide an initial guess as warm start for the implicit MPC based motion cueing algorithm. From the simulations, it was observed that the presented hybrid approach was able to reduce online computational load by shifting it offline using the explicit MPC. Further, braking constraints and adaptive washout weights were implemented in the hybrid motion cueing algorithm, to improve the specific force tracking performance and reduce any false cue occurrences. Finally, emulator studies were performed to realize driving simulator performance with the hybrid MPC approach. The thesis concludes by showing the improvements made with the developed algorithm and proposes recommendations for future work. ...
This thesis report presents a solution to deal with issues of offline and online solving through a combined/hybrid approach. For this, explicit MPC is used to provide an initial guess as warm start for the implicit MPC based motion cueing algorithm. From the simulations, it was observed that the presented hybrid approach was able to reduce online computational load by shifting it offline using the explicit MPC. Further, braking constraints and adaptive washout weights were implemented in the hybrid motion cueing algorithm, to improve the specific force tracking performance and reduce any false cue occurrences. Finally, emulator studies were performed to realize driving simulator performance with the hybrid MPC approach. The thesis concludes by showing the improvements made with the developed algorithm and proposes recommendations for future work. ...
Driving simulators have been used in the automotive industry for many years now. They have been vastly employed for conducting tests in a safe, reproducible and controlled immersive virtual environment. The ability of the simulator to recreate the in-vehicle experience for the occupant is established through motion cueing algorithms. Such algorithms have consistently been developed with model predictive control (MPC) acting as the main control technique. Currently available MPC based methods either compute the optimal controller online or derive an explicit control law in an offline setting. These approaches limit the applicability of MPC for real-time applications due to online computational expense and offline memory storage issues.
This thesis report presents a solution to deal with issues of offline and online solving through a combined/hybrid approach. For this, explicit MPC is used to provide an initial guess as warm start for the implicit MPC based motion cueing algorithm. From the simulations, it was observed that the presented hybrid approach was able to reduce online computational load by shifting it offline using the explicit MPC. Further, braking constraints and adaptive washout weights were implemented in the hybrid motion cueing algorithm, to improve the specific force tracking performance and reduce any false cue occurrences. Finally, emulator studies were performed to realize driving simulator performance with the hybrid MPC approach. The thesis concludes by showing the improvements made with the developed algorithm and proposes recommendations for future work.
This thesis report presents a solution to deal with issues of offline and online solving through a combined/hybrid approach. For this, explicit MPC is used to provide an initial guess as warm start for the implicit MPC based motion cueing algorithm. From the simulations, it was observed that the presented hybrid approach was able to reduce online computational load by shifting it offline using the explicit MPC. Further, braking constraints and adaptive washout weights were implemented in the hybrid motion cueing algorithm, to improve the specific force tracking performance and reduce any false cue occurrences. Finally, emulator studies were performed to realize driving simulator performance with the hybrid MPC approach. The thesis concludes by showing the improvements made with the developed algorithm and proposes recommendations for future work.