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.

This paper presents a novel data-driven Reference Governor with Model Predictive Control integrating local motion replanning and path following for collision avoidance. Employing a model-free Reference Governor, the proposed solution utilises system knowledge through Bayesian Optimisation to augment predetermined evasive trajectories, minimising path-following errors and simultaneously ensuring obstacle safety margins. A single-track vehicle model in combination with non-linear tyre models is used to capture the vehicle’s dynamics. The optimised control action is the vehicle steering angle, whilst the Reference Governor optimises parameters of a sigmoid reference signal to minimise the tracking error and guarantee safety with respect to obstacles in emergency manoeuvres. The proposed approach is evaluated on a single lane change using a high-fidelity simulation environment and its performance is compared to a baseline controller integrating path following and obstacle avoidance. The results show a 14% reduction of safety critical overshoot, maximising obstacle safety distance and a four times lower controller cycle time compared to the baseline. Furthermore, through a robustness analysis, it is demonstrated that the proposed approach is more robust towards model mismatches and perception-based errors, as seen by average 30% and 40% reductions in near-miss and collision rates.

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.

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 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).

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.

This thesis presents a new MPC controller which integrates path tracking and stability control into one controller. Previously these tasks were done by separate controllers, where one controller handled the path tracking while another controller ensured the vehicle was kept in the stable operating region. A drawback of this method is that the controllers have opposing objectives. The path tracker could require a higher steering wheel angle to follow the path, while the Vehicle Stability Controller (VSC) might require a lower angle to keep the vehicle stable. By integrating these two controllers into one controller, the new controller is able to take both tasks into account and optimise the control output such that both objectives are satisfied. This is achieved by implementing two extra yaw rates into the MPC model. These are the expected yaw rates based on the steering wheel angle and lateral acceleration of the vehicle. By comparing these two yaw rates to the actual yaw rate, the stability of the vehicle can be determined. The MPC controller is then able to prioritise path tracking or vehicle stability. This is achieved by actively varying the weights in the cost function depending on the vehicle state. To compare the new MPC controller, 8 benchmark controllers have been created. These controllers can be divided into two groups of four controllers. The first group is able to use differential braking in the control output, while the second group can only output an equal brake torque for all wheels. The benchmark controllers use different methods for path tracking and stability control, to get an understanding of the performance benefits of each method.
These different methods include: adding an extra target yaw rate based on path curvature and speed for tracking, adding constraints to ensure vehicle stability and using a separate stability controller to stabilise the vehicle. All controllers are evaluated using the industry standard Moose test as well as a double lane change in simulations. These manoeuvres are used in industry to evaluate stability and can also be used to evaluate path tracking. Furthermore the robustness of the controllers was evaluated by changing various parameters. These variations include: changing vehicle speed, adding extra weight to the vehicle, lowering the road 𝜇 level and performing a lane change where each lane has a different 𝜇 level. The results were evaluated using objective Key Performance Indicators regarding tracking performance and vehicle stability. The results show that the new MPC controller with the combined path tracking and stability control improves performance in both objectives. The new controller improves path tracking by 8% compared to the pure path tracking controller. While the stability is improved by 11% compared to the controller with a separate VSC. Furthermore the new controller was able to keep the vehicle stable at higher speeds and was more robust to varying conditions.