The security of Automated Vehicles (AVs) is an important emerging area of research in traffic safety. Methods have been published and evaluated in experimental vehicles to secure safe AV control in the presence of attacks, but human motion comfort is rarely investigated in such studies. In this paper, we present an innovative optimal-coupling-observer-based framework that rejects the impact of bounded sensor attacks in a network of connected and automated vehicles from safety and comfort point of view. We demonstrate its performance in car following with cooperative adaptive cruise control for platoons with redundant distance and velocity sensors. The error dynamics are formulated as a Linear Time Variant (LTV) system, resulting in complex stability conditions that are investigated using a Linear Matrix Inequality (LMI) approach guaranteeing global asymptotic stability. We prove the capability of the framework to secure occupants’ safety and comfort in the presence of bounded attacks. In the onset of attack, the framework rapidly detects attacked sensors and switches to the most reliable observer eliminating attacked sensors, even with modest attack magnitudes. Without our proposed method, severe (but bounded) attacks result in collisions and major discomfort. With our method, attacks had negligible effects on motion comfort evaluated using ISO-2631 Ride Comfort and Motion Sickness indexes. The results pave the path to bring comfort to the forefront of AVs security.

The objective of this study was to comprehensively evaluate vibrations with dummies representing infants aged 0, 3, and 9 months lying or sitting in five strollers and two cargo bicycles with dedicated baby seats on six common road surfaces using the ISO standard for whole-body vibration. Strollers induced on average 0.4 ms (Formula presented.) on tarmac and up to 5.0 ms (Formula presented.) on cobblestones at a mean walking speed of 5.3 km h (Formula presented.). Cargo bicycles induced on average 0.6 ms (Formula presented.) on tarmac and up to 10.7 ms (Formula presented.) at 25 km h (Formula presented.) on paver bricks. The standard suggests the highest accelerations for strollers and cargo bicycles are extremely uncomfortable and continuous exposure should be limited to less than 10 min. Vintage strollers have reduced vibrations compared to modern strollers, indicating benefits of compliant suspensions. We recommend that designers systematically consider vibration, users avoid prolonged exposure to surfaces rougher than tarmac, and researchers pursue scientifically founded test procedures and standards for infant vibration.

Prolonged exposure to whole-body vibration (WBV) is a key contributor to motion discomfort in vehicles, including motion sickness and ride comfort. This issue becomes more compelling in automated vehicles, where occupants are expected to frequently engage in non-driving-related activities and will expect high comfort levels. Hence, enhancing seat design to mitigate WBV is essential for improving ride comfort across vehicle types. Therefore, this study, which primarily addresses vertical accelerations, optimized an existing seat suspension (K-Seat) and subjectively assessed discomfort using 24 participants (13 males and 11 females) exposed to a 29-minute driving session. The experiment was conducted with a conventional Toyota Yaris seat in a driving simulator, where a K-Seat model was used to emulate the effect of the seat suspension. Thus we evaluated the K-Seat, which has shown great promise for attenuating low-frequency vibrations; however, it had never been tested on human participants. The results show an overall reduction of 50% in reported motion sickness using the motion illness symptoms classification scale (MISC). Subjective discomfort was also alleviated for head and upper back. In addition, perceived discomfort was analyzed based on gender, illustrating a greater effectiveness of the K-Seat in enhancing lower neck comfort for females than for males.

The ability to engage in other activities during the ride is considered by consumers as one of the key reasons for the adoption of automated vehicles. However, engagement in non-driving activities will provoke occupants’ motion sickness, deteriorating their overall comfort and thereby risking acceptance of automated driving. Therefore, it is critical to extend our understanding of motion sickness and unravel the modulating factors that affect it through experiments with participants. Currently, most experiments are conducted on public roads (realistic but not reproducible) or test tracks (feasible with prototype automated vehicles). This research study develops a method to design an optimal path and speed reference to accurately replicate on-road motion sickness exposure on a small test track. The method uses model predictive control to replicate the longitudinal and lateral accelerations collected from on-road drives on a test track of 70 m by 175 m. A within-subject experiment (47 participants) was conducted comparing the occupants’ motion sickness occurrence in test-track and on-road conditions, with the conditions being cross-randomized. The results illustrate that the subjective (reported) motion sickness is well reproduced with an insignificant reduction on the track. Meanwhile, there is an overall correspondence of individual sickness levels between on-road and test-track. This paves the path for the employment of our method for a simpler, safer and more replicable assessment of motion sickness.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

Users of automated vehicles will engage in other activities and take their eyes off the road, making them prone to motion sickness. To resolve this, the current paper validates models predicting sickness in response to motion and visual conditions. We validate published models of vestibular and visual sensory integration that have been used for predicting motion sickness through sensory conflict. We use naturalistic driving data and laboratory motion (and vection) paradigms, such as sinusoidal translation and rotation at different frequencies, Earth-Vertical Axis Rotation, Off-Vertical Axis Rotation, Centrifugation, Somatogravic Illusion, and Pseudo-Coriolis, to evaluate different models for both motion perception and motion sickness. We investigate the effects of visual motion perception in terms of rotational velocity (visual flow) and verticality. According to our findings, the SVC_I model, a 6DOF model based on the Subjective Vertical Conflict (SVC) theory, with visual rotational velocity input is effective at estimating motion sickness. However, it does not correctly replicate motion perception in paradigms such as roll-tilt perception during centrifuge, pitch perception during somatogravic illusion, and pitch perception during pseudo-Coriolis motions. On the other hand, the Multi-Sensory Observer Model (MSOM) accurately models motion perception in all considered paradigms, but does not effectively capture the frequency sensitivity of motion sickness, and the effects of vision on sickness. For both models (SVC_I and MSOM), the visual perception of rotational velocity strongly affects sickness and perception. Visual verticality perception does not (yet) contribute to sickness prediction, and contributes to perception prediction only for the somatogravic illusion. In conclusion, the SVC_I model with visual rotation velocity feedback is the current preferred option to design vehicle control algorithms for motion sickness reduction, while the MSOM best predicts perception. A unified model that jointly captures perception and motion sickness remains to be developed.

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%).

Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s²) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities, was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities.

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.

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.