arXiv subjects

Riender Happee

Publications and source records attributed to Riender Happee.

At least 19 recordsLinked to original sources

arXiv2025

Optimal-coupling-observer AV motion control securing comfort in the presence of cyber attacks

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.

eess.SY↗

arXiv2025

Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation

Driving simulators are increasingly used in research and development. However, simulators often cause motion sickness due to downscaled motion and unscaled veridical visuals. In this paper, a motion cueing algorithm is proposed that reduces motion sickness as predicted by the subjective vertical conflict (SVC) model using model predictive control (MPC). Both sensory conflict and specific force errors are penalised in the cost function, allowing the algorithm to jointly optimise fidelity and comfort. Human-in-the-loop experiments were conducted to compare four simulator motion settings: two variations of our MPC-based algorithm, one focused on pure specific force tracking and the second compromising specific force tracking and motion sickness minimisation, as well as reference adaptive washout and no motion cases. The experiments were performed on a hexapod driving simulator with participants exposed to passive driving. Experimental motion sickness results closely matched the sickness model predictions. As predicted by the model, the no motion condition yielded the lowest sickness levels. However, it was rated lowest in terms of fidelity. The compromise solution reduced sickness by over 50% (average MISC level 3 to 1.5) compared to adaptive washout and the algorithm focusing on specific force tracking, without any significant reduction in fidelity rating. The proposed approach for developing MCA that takes into account both the simulator dynamics and time evolution of motion sickness offers a significant advancement in achieving an optimal control of motion sickness and specific force recreation in driving simulators, supporting broader simulator use.

cs.RO↗

arXiv2025

Facial Emotion Recognition does not detect feeling unsafe in automated driving

Trust and perceived safety play a crucial role in the public acceptance of automated vehicles. To understand perceived risk, an experiment was conducted using a driving simulator under two automated driving styles and optionally introducing a crossing pedestrian. Data was collected from 32 participants, consisting of continuous subjective comfort ratings, motion, webcam footage for facial expression, skin conductance, heart rate, and eye tracking. The continuous subjective perceived risk ratings showed significant discomfort associated with perceived risk during cornering and braking followed by relief or even positive comfort on continuing the ride. The dynamic driving style induced a stronger discomfort as compared to the calm driving style. The crossing pedestrian did not affect discomfort with the calm driving style but doubled the comfort decrement with the dynamic driving style. This illustrates the importance of consequences of critical interactions in risk perception. Facial expression was successfully analyzed for 24 participants but most (15/24) did not show any detectable facial reaction to the critical event. Among the 9 participants who did, 8 showed a Happy expression, and only 4 showed a Surprise expression. Fear was never dominant. This indicates that facial expression recognition is not a reliable method for assessing perceived risk in automated vehicles. To predict perceived risk a neural network model was implemented using vehicle motion and skin conductance. The model correlated well with reported perceived risk, demonstrating its potential for objective perceived risk assessment in automated vehicles, reducing subjective bias and highlighting areas for future research.

cs.CV↗

arXiv2025

Perceived risk evolution in automated driving inferred from large-scale discrete ratings

Perceived risk in automated driving is often measured as discrete scores that summarise riding experience but this obscures volatile peaks from sustained elevation. Here we treat discrete clipwise ratings as constraints on an unobserved inferred evolution and apply a kernel constrained inverse model to infer the temporal evolution of perceived risk. Across 2,164 participants and 141,628 discrete clipwise ratings spanning 236 hours of scripted motorway interactions, we infer evolutions under kernel constraints whose shapes follow priors from independent handset-based ratings and whose timing is fixed by scripted manoeuvre markers. The inferred perceived risk evolutions differentiate accumulated perceived risk from within clip concentration, revealing scenario differences that are not identifiable from peak judgements alone. We then map these inferred evolutions from observable vehicle and relative motion cues under strict event level holdout using a deep neural network, enabling interpretable attribution analyses. Attribution shows distinct patterns between risk rising and falling segments, with a shift toward conflict cues in the rising phase, and a rebound toward stability cues in the falling phase. Attribution concentration increases only modestly at high perceived risk levels. These results move beyond treating perceived risk as a single severity score by characterising within episode dynamics and phase dependent cue associations in scripted motorway interactions.

cs.HC↗

arXiv2025

Modeling Head-Neck Dynamics under Lateral Perturbations Using MPC to Mimic CNS postural stabilization strategy

Automated vehicles will allow occupants to engage in non-driving tasks, but limited visual cues will make them vulnerable to unexpected movements. These unpredictable perturbations create a "surprise factor," forcing the central nervous system to rely on compensatory postural adjustments, which are less effective, and are more likely to trigger sensory conflicts. Since the head is a key reference for sensory input (vestibular and vision), models accurately capturing head-neck postural stabilization are essential for assessing AV comfort. This study extends an existing model predictive control-based framework to simulate head-neck postural control under lateral perturbations. Experimental validation against human data demonstrates that the model can accurately reproduce dynamic responses during lateral trunk perturbations. The results show that muscle effort combined with partial somatosensory feedback provides the best overall dynamic fit without requiring corrective relative and global head orientation integrators for posture.

eess.SY↗

arXiv2025

A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation

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.

cs.RO↗

arXiv2024

Efficient Motion Sickness Assessment: Recreation of On-Road Driving on a Compact Test Track

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 efficiently 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 no difference and no effect of the condition on the occurrence of the average motion sickness across the participants. 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.

cs.RO↗

arXiv2024

A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres

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.

cs.RO↗

arXiv2024

Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling Using Torque Vectoring

This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tracking and vehicle stability objectives, prioritising collision avoidance in emergencies. The controller's prediction model is a nonlinear double-track vehicle model based on an extended Fiala tyre to capture the nonlinear coupled longitudinal and lateral dynamics. The controller computes the optimal steering angle and the longitudinal forces per each of the four wheels to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Thanks to the optimisation of the longitudinal tyre forces, the proposed controller can produce an extra yaw moment, increasing the vehicle's lateral agility to avoid obstacles while keeping the vehicle stable. The optimal forces are constrained in the tyre friction circle not to exceed the tyres and vehicle capabilities. In a high-fidelity simulation environment, we demonstrate the benefits of torque vectoring, showing that our proposed approach is capable of successfully avoiding obstacles and keeping the vehicle stable while driving a double-lane change manoeuvre, in comparison to baselines lacking torque vectoring or collision avoidance prioritisation.

cs.RO↗

arXiv2023

Modelling individual motion sickness accumulation in vehicles and driving simulators

Users of automated vehicles will move away from being drivers to passengers, preferably engaged in other activities such as reading or using laptops and smartphones, which will strongly increase susceptibility to motion sickness. Similarly, in driving simulators, the presented visual motion with scaled or even without any physical motion causes an illusion of passive motion, creating a conflict between perceived and expected motion, and eliciting motion sickness. Given the very large differences in sickness susceptibility between individuals, we need to consider sickness at an individual level. This paper combines a group-averaged sensory conflict model with an individualized accumulation model to capture individual differences in motion sickness susceptibility across various vision conditions. The model framework can be used to develop personalized models for users of automated vehicles and improve the design of new motion cueing algorithms for simulators. The feasibility and accuracy of this model framework are verified using two existing datasets with sickening. Both datasets involve passive motion, representative of being driven by an automated vehicle. The model is able to fit an individuals motion sickness responses using only 2 parameters (gain K1 and time constant T1), as opposed to the 5 parameters in the original model. This ensures unique parameters for each individual. Better fits, on average by a factor of 1.7 of an individuals motion sickness levels, are achieved as compared to using only the group-averaged model. Thus, we find that models predicting group-averaged sickness incidence cannot be used to predict sickness at an individual level. On the other hand, the proposed combined model approach predicts individual motion sickness levels and thus can be used to control sickness.

q-bio.NC↗

arXiv2023

Motion Cueing Algorithm for Effective Motion Perception: A frequency-splitting MPC Approach

Model predictive control (MPC) is a promising technique for motion cueing in driving simulators, but its high computation time limits widespread real-time application. This paper proposes a hybrid algorithm that combines filter-based and MPC-based techniques to improve specific force tracking while reducing computation time. The proposed algorithm divides the reference acceleration into low-frequency and high-frequency components. The high-frequency component serves as a reference for translational motion to avoid workspace limit violations, while the low-frequency component is for tilt coordination. The total acceleration serves as a reference for combined specific force with the highest priority to enable compensation of deviations from its reference values. The algorithm uses constraints in the MPC formulation to account for workspace limits and workspace management is applied. The investigated scenarios were a step signal, a multi-sine wave and a recorded real-drive slalom maneuver. Based on the conducted simulations, the algorithm produces approximately 15% smaller root means squared error (RMSE) for the step signal compared to the state-of-the-art. Around 16% improvement is observed when the real-drive scenario is used as the simulation scenario, and for the multi-sine wave, 90% improvement is observed. At higher prediction horizons the algorithm matches the performance of a state-of-the-art MPC-based motion cueing algorithm. Finally, for all prediction horizons, the frequency-splitting algorithm produced faster results. The pre-generated references reduce the required prediction horizon and computational complexity while improving tracking performance. Hence, the proposed frequency-splitting algorithm outperforms state-of-the-art MPC-based algorithm and offers promise for real-time application in driving simulators.

cs.RO↗

arXiv2023

Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling

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.

cs.RO↗

arXiv2023

The impact of body and head dynamics on motion comfort assessment

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%).

cs.RO↗

arXiv2023

Simulating vibration transmission and comfort in automated driving integrating models of seat, body, postural stabilization and motion perception

To enhance motion comfort in (automated) driving we present biomechanical models and demonstrate their ability to capture vibration transmission from seat to trunk and head. A computationally efficient full body model is presented, able to operate in real time while capturing translational and rotational motion of trunk and head with fore-aft, lateral and vertical seat motion. Sensory integration models are presented predicting motion perception and motion sickness accumulation using the head motion as predicted by biomechanical models.

cs.HC↗

arXiv2023

Kinematic body responses and perceived discomfort in a bumpy ride: Effects of sitting posture

The present study investigates perceived comfort and whole-body vibration transmissibility in intensive repetitive pitch exposure representing a bumpy ride. Three sitting strategies (preferred, erect, and slouched) were evaluated for perceived body discomfort and body kinematic responses. Nine male and twelve female participants were seated in a moving-based driving simulator. The slouched posture significantly increased lateral and yaw body motion and induced more discomfort in the seat back area. After three repetitive exposures, participants anticipated the upcoming motion using more-effective postural control strategies to stabilize pelvis, trunk, and head in space.

physics.med-ph↗

arXiv2023

Computationally efficient human body modelling for real time motion comfort assessment

Due to the complexity of the human body and its neuromuscular stabilization, it has been challenging to efficiently and accurately predict human motion and capture posture while being driven. Existing simple models of the seated human body are mostly two-dimensional and developed in the mid-sagittal plane ex-posed to in-plane excitation. Such models capture fore-aft and vertical motion but not the more complex 3D motions due to lateral loading. Advanced 3D full-body active human models (AHMs), such as in MADYMO, can be used for comfort analysis and to investigate how vibrations influence the human body while being driven. However, such AHMs are very time-consuming due to their complexity. To effectively analyze motion comfort, a computationally efficient and accurate three dimensional (3D) human model, which runs faster than real-time, is presented. The model's postural stabilization parameters are tuned using available 3D vibration data for head, trunk and pelvis translation and rotation. A comparison between AHM and EHM is conducted regarding human body kinematics. According to the results, the EHM model configuration with two neck joints, two torso bending joints, and a spinal compression joint accurately predicts body kinematics.

cs.HC↗

arXiv2023

Modelling human seat contact interaction for vibration comfort

The seat to head vibration transmissibility depends on various characteristics of the seat and the human body. One of these, is the contact interaction, which transmits vibrational energy from the seat to the body. To enhance ride comfort, seat designers should be able to accurately simulate seat contact without the need for extensive experiments. Here, the contact area, pressure, friction and seat and body deformation in compression and shear play a significant role. To address these challenges, the aim of this paper is to define appropriate contact models to improve the prediction capabilities of a seated human body model with regards to experimental data. A computationally efficient multibody (MB) model is evaluated interacting with finite element (FE) and MB backrest models, using several contact models. Outcomes are evaluated in the frequency domain for 3D vibration transmission from seat to pelvis, trunk, head and knees. Results illustrate that both FE and MB backrest models allowing compression and shear provide realistic results.

cs.HC↗

arXiv2023

Evaluation of motion comfort using advanced active human body models and efficient simplified models

Active muscles are crucial for maintaining postural stability when seated in a moving vehicle. Advanced active 3D non-linear full body models have been developed for impact and comfort simulation, including large numbers of individual muscle elements, and detailed non-linear models of the joint structures. While such models have an apparent potential to provide insight into postural stabilization, they are computationally demanding, making them less practical in particular for driving comfort where long time periods are to be studied. In vibrational comfort and in general biomechanical research, linearized models are effectively used. This paper evaluates the effectiveness of simplified 3D full-body human models to capture comfort provoked by whole-body vibrations. An efficient seated human body model is developed and validated using experimental data. We evaluate the required complexity in terms of joints and degrees of freedom for the spine, and explore how well linear spring-damper models can approximate reflexive postural stabilization. Results indicate that linear stiffness and damping models can well capture the human response. The results are improved by adding proportional integral derivative (PID) and head-in-space (HIS) controllers to maintain the defined initial body posture. The integrator is shown to be essential to prevent drift from the defined posture. The joint angular relative displacement is used as the input reference to each PID controller. With this model, a faster than real-time solution is obtained when used with a simple seat model. The paper also discusses the advantages and disadvantages of various models and provides insight into which models are more appropriate for motion comfort analysis.

cs.HC↗