This paper presents a novel approach integrating motion replanning, path tracking and vehicle stability for collision avoidance using nonlinear Model Predictive Contouring Control. Employing torque vectoring capabilities, the proposed controller is able to stabilise the vehicle in evasive manoeuvres at the limit of handling. A nonlinear double-track vehicle model, together with an extended Fiala tyre model, is used to capture the nonlinear coupled longitudinal and lateral dynamics. The optimised control inputs are the steering angle and the four longitudinal wheel forces to minimise the tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergency manoeuvres. These optimised longitudinal forces generate an additional direct yaw moment, enhancing the vehicle’s lateral agility and aiding in obstacle avoidance and stability maintenance. The longitudinal tyre forces are constrained using the tyre friction cycle. The proposed controller has been tested on rapid prototyping hardware to prove real-time capability. In a high-fidelity simulation environment validated with experimental data, our proposed approach successfully avoids obstacles and maintains vehicle stability. It outperforms two baseline controllers: one without torque vectoring and another one without collision avoidance prioritisation. Furthermore, we demonstrate the robustness of the proposed approach to vehicle parameter variations, road friction, perception, and localisation errors. The influence of each variation is statistically assessed to evaluate its impact on the performance, providing guidelines for future controller design.

Driving simulators aim to replicate real-world vehicle experiences by recreating accelerations acting on occupants using a combination of translational accelerations and tilt-coordination. Due to space constraints, translational accelerations alone are insufficient, and platform tilting generates additional gravitational forces to enhance realism. However, ensuring the tilt motion remains imperceptible is critical to maintaining immersion.
Model Predictive Control-based motion cueing algorithms demonstrate superior specific force tracking and platform workspace utilization. Despite these benefits, MPC algorithms can exhibit pre-positioning, a phenomenon where the platform tilts prematurely in anticipation of future motion, causing perceptible false cues that disrupt immersion. This phenomenon is particularly noticeable in tilt-coordination due to sustained specific forces.
This work proposes a solution to mitigate pre-positioning by introducing a dynamic scaling factor for tilt-coordination. By scaling down the reference signal for tilt coordination, it stays within the simulator’s tilt angle and tilt-rate capabilities, and platform tilt rates are kept below human perception thresholds. The scaling factor is derived from two key parameters: the maximum specific force generated by platform tilt and the tilt rate perception threshold. The reference for specific force is unscaled to optimally use the translational workspace.
This approach enhances driving simulator realism by minimizing the perceptibility of pre-positioning while optimizing specific force recreation. Subjective evaluations also indicate improved immersion, illustrating the effectiveness of the scenario-adaptive Autoscaling MCA.

A plausible explanation about the acquisition and realization of beliefs by the central nervous system (CNS) when issuing control actions to counteract external perturbations, is to employ mechanisms aiming to minimize sensory conflict and muscle effort while maintaining biomechanical stability. However, existing head–neck postural control models fail to explicitly integrate this plausible CNS objective within their stabilization mechanisms. This study proposes a novel Model Predictive Control (MPC)-based framework to replicate CNS postural stabilization by incorporating the minimization of sensory conflict as a primary control objective through the MPC cost function. The MPC is integrated in a simplified biomechanical head–neck structure, using a prediction model and sensory feedback to optimize control actions over a finite time horizon within biomechanical constraints. Two human experiments measuring head motion with unpredictable seat and trunk perturbations were used to evaluate and validate different configurations of sensory feedback pathways. During anterior–posterior translational trunk perturbations, the results illustrated that the configuration with vestibular feedback improved head position prediction while muscle effort and partial somatosensory feedback alone, achieved superior results in head pitch prediction. Meanwhile, muscle effort and partial somatosensory feedback were sufficient to stabilize the head during trunk rotational (pitch) perturbations. Finally, a multi-scenario optimization demonstrated that a single set of MPC weights could generalize stabilization across both perturbation types. The results demonstrate the effectiveness of MPC in replicating CNS-inspired postural adjustments, indicating that controlling a simplified biomechanical head–neck model provides a computationally efficient and accurate alternative to complex multi-segment approaches.

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design.

Transportation and mobility are experiencing a significant transformation the recent years, which is evident in road (vehicles and bicycles) and rail vehicles. This transformation includes the introduction of automated vehicles (AVs), the increase of active transportation modes (e.g. cycling and walking) and the extended use of trains for commuting to work or travelling. However, despite this great transition, there are significant challenges that can hamper the wide use of these transport means, with comfort being one of them. In this paper, we explore physical comfort in these transport modes, examining ride comfort and motion sickness definitions and assessment, environmental influences, occupant postures, human body dynamics, and postural control strategies for adapting to motion. We conclude that while established comfort guidelines exist for conventional vehicles, substantial gaps persist in understanding and evaluating comfort in emerging modes like bicycles and automated vehicles with varied seating. Further research into modelling human body dynamics and the central nervous system's role in postural control, especially for cyclists and non-conventional postures, is essential for designing future transportation systems that prioritise comfort and health.

We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ('edge cases') that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.

In automated vehicles, beeps are widely used as alarms and feedback. However, as automation advances, there is a need to explore subtler, contextually sound-based notifications for non-urgent situations. While auditory interfaces for take-over requests have been studied, limited attention has been given to using soundscapes for such alerts. This paper designed and evaluated soundscapes using existing driving-related sounds–amplified road noise and/or dimmed background music–for scheduled take-over situations. A driving simulator study showed that these soundscapes enhanced reaction time, situation awareness, and acceptance without causing annoyance. Particularly, the combined condition (music dimming and road noise amplifying) supported higher driver awareness and responsiveness. These findings suggest that soundscapes can offer safer, more intuitive take-over alerts by embedding information into familiar audio cues. This study contributes to developing soundscapes as novel alert mechanisms that integrate seamlessly with the driving environment to enhance both safety and user experience in automated vehicles.

Vulnerable road user safety is paramount for increasing shares of active travel modes and introducing automated vehicles. Microscopic traffic simulation is a prevalent method in research and practice with a growing focus on safety and cyclists. Its practical benefits make it an essential tool for developing safe future transportation. We review the methodology of simulation studies and the validation of their microscopic models to evaluate cycling safety assessment in microscopic simulations. We find that current work relies predominantly on the lane-based models of established traffic flow simulation packages that separate longitudinal and lateral dynamics. These models do not sufficiently capture diverse behaviors and conflict causality to predict cycling safety. In contrast, new models with successful calibrations and validations advance simulated interactions towards capturing conflict causality. Of 42 reviewed studies, six calibrate, and three validate models for safety prediction. Other studies disregard calibration and validation, posing a threat of unfounded safety predictions and unsafe design recommendations. We present a methodological framework conceptualizing best practices for reliable assessment. It calls for the identification of safety-relevant behaviors of cyclists and other road users in conflicts. Specialized behavioral models must be developed, calibrated, and validated. The selected safety indicators must enable capturing the expected unsafe events. To create these tools, improved models of cycling behavior must be transferred to established simulation packages. Following the framework, researchers and practitioners can use simulation as a practical and ethical means to assess the cycling safety impact of innovations ranging from infrastructure to automation and connectivity.

As vehicles transition between driving automation levels, drivers need to be continually aware of the automation mode and the resulting driver responsibilities. This study investigates the impact of visual user interfaces (UIs) on drivers’ mode awareness in SAE Level 2 automated vehicles. It focuses on their understanding of speed and distance control, steering control, and the hands-on steering wheel requirement presented through UIs. Forty-five UIs were generated, presenting the activation of Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC) and the hands-on steering wheel requirement. Through an online questionnaire with 1080 respondents with experience of SAE Level 2, the study evaluated how these visual UIs influenced users’ understanding of control responsibilities, information usability, and trust in automated vehicles. The results show a limited role of UI in shaping users’ understanding of control. ACC UIs and LKA UIs had no significant effects, and apparently, the understanding of speed and distance control and steering control was independent of the ACC UI and LKA UI. A large variance in responses regarding the understanding of steering control and speed and distance control indicates confusion caused by mode ambiguity, suggesting that drivers do not well understand how the speed and distance control and steering control task is shared between the driver and the automation. However, the hands-on steering wheel UIs significantly improved the understanding of the hands-on steering wheel requirement. The hands-on steering wheel UI combining the hands on the wheel icon and the text “Keep hands on steering wheel” yielded 94.4% correct understanding and outperformed the UI with hands but without text (87.8% correct) or no UI (82.5% correct). In addition, the variation of visual UI did not affect trust. This study contributes to the understanding and design of visual UIs for effective communication of driver responsibilities in automated vehicles.

As users transition from drivers to passengers in automated vehicles, they often take their eyes off the road to engage in non-driving activities. In driving simulators, visual motion is presented with scaled or without physical motion, leading to a mismatch between expected and perceived motion. Both conditions elicit motion sickness, calling for enhanced vehicle and simulator motion control strategies. Given the large differences in sickness susceptibility between individuals, effective countermeasures must address this at a personal level. This paper combines a group-averaged sensory conflict model with an individualized Accumulation Model (AM) to capture individual differences in motion sickness susceptibility across various conditions. The feasibility of this framework is verified using three datasets involving sickening conditions: (1) vehicle experiments with and without outside vision, (2) corresponding vehicle and driving simulator experiments, and (3) vehicle experiments with various non-driving-related tasks. All datasets involve passive motion, mirroring experience in automated vehicles. The preferred model (AM2) can fit individual motion sickness responses across conditions using only two individualized parameters (gain K1 and time constant T1) instead of the original five, ensuring unique parameters for each participant and generalisability across conditions. An average improvement factor of 1.7 in fitting individual motion sickness responses is achieved with the AM2 model compared to the group-averaged AM0 model. This framework demonstrates robustness by accurately modeling distinct motion and vision conditions. A Gaussian mixture model of the parameter distribution across a population is developed, which predicts motion sickness in an unseen dataset with an average RMSE of 0.47. This model reduces the need for large-scale population experiments, accelerating research and development

Previous literature suggests that the motion sickness susceptibility questionnaire (MSSQ) is inadequate for prediction of motion sickness under naturalistic driving conditions. In this study, we investigated whether visually induced motion sickness using a virtual reality headset could be used as a quick and reliable way to predict participant susceptibility. We recruited 22 participants to complete a two-part experiment. In randomised order, we determined their susceptibility to visual motion sickness and their susceptibility to car sickness. To determine visual susceptibility, the visual scene was sequentially rotated at constant velocity around an earth-vertical yaw axis and rolled about the nasiooccipital axis, in 30 s intervals. Car sickness, on the other hand, was elicited under completely naturalistic conditions, being driven in the backseat of a car in the city of Delft, performing a visual task on a laptop. Sickness ratings were collected at regular intervals in both parts of the experiment. We found that the frequencies excited by naturalistic driving are very low, which has important consequences for motion sickness modelling and mitigation in automated vehicles. We found that individual car sickness correlated positively with visual motion sickness. This indicates that both are influenced by a common sickness susceptibility factor. Car sickness correlated similarly with visual motion sickness and MSSQ. Overall, our results indicate that combining measurements of sickness responses to a visual stimulus and MSSQ can yield a reliable method for determining individual sickness susceptibility. To this end the visual stimulus and the weighting with MSSQ responses can be refined using a much larger sample and considering additional visual conditions in driving.

Introduction: Augmented reality (AR) has been increasingly studied in transportation, particularly for drivers and pedestrians interacting with automated vehicles (AVs). Previous research evaluated AR interfaces using online video-based questionnaires but lacked human-subject research in immersive environments. This study examined if prior online evaluations of nine AR interfaces could be replicated in an immersive virtual environment and if AR interface effectiveness depends on pedestrian attention allocation. 

Methods: Thirty participants completed 120 trials in a CAVE-based simulator with yielding and non-yielding AVs, rating the interface’s intuitiveness and crossing the road when they felt safe. To emulate visual distraction, participants had to look into an attention-attractor circle that disappeared 1 s after the interface appeared. 

Results: The results showed that intuitiveness ratings from the current CAVE-based study and the previous online study correlated strongly (r ≈ 0.90). Head-locked interfaces and familiar designs (augmented traffic lights, zebra crossing) yielded higher intuitiveness ratings and quicker crossing initiations than vehicle-locked interfaces. Vehicle-locked interfaces were less effective when the attention-attractor was on the environment’s opposite side, while head-locked interfaces were relatively unaffected by attention-attractor position. 

Discussion: In conclusion, this ‘AR in VR’ study shows strong congruence between intuitiveness ratings in a CAVE-based study and online research, and demonstrates the importance of interface placement in relation to user gaze direction.

This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC’s cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle’s manoeuvrability compared to an L-MPCC with a Gaussian Process.

Head motion is a key determinant of motion comfort and differs substantially from seat motion due to seat and body compliance and dynamic postural stabilization. This paper compares different human body model fidelities to transmit seat accelerations to the head for the assessment of motion comfort through simulations. Six-degree of freedom dynamics were analyzed using frequency response functions derived from an advanced human model (AHM), a computationally efficient human model (EHM) and experimental studies. Simulations of dynamic driving show that human models strongly affected the predicted ride comfort (increased up to a factor 3). Furthermore, they modestly affected sickness using the available filters from the literature and ISO-2631 (increased up to 30%), but more strongly affected sickness predicted by the subjective vertical conflict (SVC) model (increased up to 70%).

Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of perceived risk dynamics remains limited, and corresponding computational models are scarce. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE Level 2 automated vehicles. PCAD quantifies task difficulty using the gap between the current velocity and the safe velocity region in 2D, and accounts for the minimal control effort (braking and/or steering) needed to avoid a potential collision, based on visual looming, behavioural uncertainties of neighbouring vehicles, imprecise control of the subject vehicle, and collision severity. The PCAD model predicts both continuous-time perceived risk and peak perceived risk per event. We analyse model properties both theoretically and empirically with two unique datasets: Datasets Merging and Obstacle Avoidance. The PCAD model generally outperforms three state-of-the-art models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers’ perceived risk, albeit at the cost of longer computation time. Our findings reveal that perceived risk varies with the position, velocity, and acceleration of the subject and neighbouring vehicles, and is influenced by uncertainties in their velocities.

Microscopic simulation is an established tool in traffic engineering and research, where aggregated traffic performance measures are inferred from the simulation of individual agents. Additionally, measures describing the safety and efficiency of road user interactions gain importance for recent developments such as automated vehicles and urban cycling. However, current simulation frameworks model interactions including cyclists only with limited realism. To address this issue, we propose to bring bicycle dynamics to traffic simulation. We demonstrate that a novel reformulation of the social force framework can create input signals for a controlled inverted pendulum bicycle model and thereby enable a fully two-dimensional open space simulation of cyclist interactions. The inverted pendulum model introduces the need to stabilize the bicycle as a constraint to the reactive behavior of simulated cyclists. Furthermore, it enables the simulation of countersteering and weaving for stabilization. Our cyclist social forces have anisotropic force fields with respect to relative interaction position and orientation to describe the varying interaction constellations in open space. With these models, we simulate five single- and multi-cyclist test cases and show that the generated trajectories notably differ from results obtained from a 2D bicycle model without lean angle simulation. Measurements of the maximum lateral path deviation and post-encroachment time show that these differences are relevant for typical applications. Our work demonstrates the potential of introducing physics-based realistic bicycle dynamics to the microscopic simulation of individual road user interactions and the fundamental capability of our reformulated cyclist social forces to do so. Going further, we plan to calibrate and validate our model based on naturalistic cycling data to support the initial results of this work.

As automated vehicles require human drivers to resume control in critical situations, predicting driver takeover behaviour could be beneficial for safe transitions of control. While previous research has explored predicting takeover behaviour in relation to driver state and traits, little work has examined the predictive value of manual driving style. We hypothesised that drivers’ behaviour during manual driving is predictive of their takeover behaviour when resuming control from an automated vehicle. We assessed 38 drivers with varying experience in a high-fidelity driving simulator. After completing manual driving sessions to assess their driving style, participants performed an automated driving task, typically on a subsequent date. Measures of driving style from manual driving sessions, including headway and lane change speed, were found to be predictive of takeover behaviour. The level of driving experience was associated with the behavioural measures, but correlations between measures of manual driving style and takeover behaviour remained after controlling for driver experience. Our findings demonstrate that how drivers reclaim control from their automated vehicle is not an isolated phenomenon but is associated with manual driving behaviour and driving experience. Strategies to improve takeover safety and comfort could be based on driving style measures, for example by the automated vehicle adapting its behaviour to match a driver's driving style.

This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising obstacle avoidance in emergencies. The controller’s prediction model is a non-linear single-track vehicle model with the Fiala tyre to capture the vehicle’s non-linear behaviour. The MPCC computes the optimal steering angle and brake torques to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Furthermore, the MPCC is extended with the tyre friction circle to fully exploit the vehicle’s manoeuvrability and stability. The MPCC controller is tested using real-time rapid prototyping hardware to prove its real-time capability. The performance is compared with a state-of-the-art Model Predictive Control (MPC) in a high-fidelity simulation environment. The double lane change scenario results demonstrate a significant improvement in successfully avoiding obstacles and maintaining vehicle stability.

Teleoperation is considered as a viable option to control fully automated vehicles (AVs) of Level 4 and 5 in special conditions. However, by bringing the remote drivers in the loop, their driving experience should be realistic to secure safe and comfortable remote control. Therefore, the remote control tower should be designed such that remote drivers receive high quality cues regarding the vehicle state and the driving environment. In this direction, the steering feedback could be manipulated to provide feedback to the remote drivers regarding how the vehicle reacts to their commands. However, until now, it is unclear how the remote drivers' steering feel could impact occupant's motion comfort. This paper focuses on exploring how the driver feel in remote (RD) and normal driving (ND) are related with occupants' motion comfort. More specifically, different types of steering feedback controllers are applied in (a) the steering system of a Research Concept Vehicle-model E (RCV-E) and (b) the steering system of a remote control tower. An experiment was performed to assess driver feel when the RCV-E is normally and remotely driven. Subjective assessment and objective metrics are employed to assess drivers' feel and occupants' motion comfort in both remote and normal driving scenarios. The results illustrate that motion sickness and ride comfort are dominated by steering velocity variations in remote driving, while throttle input variations dominate in normal driving. The results demonstrate that motion sickness and steering velocity increase both around 25% from normal to remote driving.

We evaluated the impact of Model Predictive Control (MPC) robotic-assisted versus unassisted training on motor learning of a complex bicycle steering task. Ten participants were divided into two groups, alternating between MPC-assisted and unassisted training to ride a steer-by-wire bicycle on a treadmill to collect virtual stars. At Baseline, Mid-Training, and Post-Training, motor skills were assessed by the average and standard deviation (SD) of distance to stars, while performance was measured by the mean absolute and SD of the steering rate. We found significant improvements in task skill and steering performance, with notable benefits observed in the performance of the group initially trained unassisted. Our findings suggest that starting the training unassisted could stimulate an internal focus (concentrating on one's own body movements) and intrinsic skill perception. This foundation may then form a basis for later integration of MPC assistance to refine further the gained motor skills. Such a sequential training approach may benefit motor skill acquisition of complex dynamics tasks. Further research is necessary to validate and apply these findings to enhance training methods.