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.

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.

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.

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.

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.

In the transition from partial to high automation, occupants will no longer be actively involved in driving. This will allow the use of travel time for work or leisure, where high comfort levels preventing motion sickness are required. In this paper, an optimal trajectory planning algorithm is presented in order to minimise motion sickness in automated vehicles. A predefined path is provided as an input to the algorithm, to generate an optimal path with limited lateral deviation and the corresponding optimal velocity profile, for the minimisation of motion sickness. An optimal control problem is formulated with a cost function combining both motion sickness and travel time. For a sickening curvy road, the algorithm reduced the motion sickness dose value (MSDV) up to 52% depending on the allowed lateral deviation and the weighting on travel time. The efficacy of the proposed algorithm has been evaluated via human-in-the-loop experiments using a moving-base driving simulator. Motion cueing parameters were selected to optimally transmit the sickening stimuli resulting in close to full vibration transmission above 0.2 Hz. During the experiment, the participants were asked to rate their experience based on the standard MIsery SCore ratings. According to these, sickness levels were reduced on average by 65% with reduced motion sickness in all 16 participants.

This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of a UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by the average sum of the states’ and measurement’ estimation error for various vehicle manoeuvres covering a wide range of vehicle behaviour. The predefined cost function is minimised through a TSBO which aims to find a location in the feasible region that maximises the probability of improving the current best solution. Results on an experimental dataset show the capability to tune the UKF in 79.9% less time than using a genetic algorithm (GA) and the overall capacity to improve the estimation performance in an experimental test dataset of 9.9% to the current state-of-the-art GA.

We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate > 10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.

The future urban environment may consist of mixed traffic in which pedestrians interact with automated vehicles (AVs). However, it is still unclear how AVs should communicate their intentions to pedestrians. Augmented reality (AR) technology could transform the future of interactions between pedestrians and AVs by offering targeted and individualized communication. This paper presents nine prototypes of AR concepts for pedestrian-AV interaction that are implemented and demonstrated in a real crossing environment. Each concept was based on expert perspectives and designed using theoretically-informed brainstorming sessions. Prototypes were implemented in Unity MARS and subsequently tested on an unmarked road using a standalone iPad Pro with LiDAR functionality. Despite the limitations of the technology, this paper offers an indication of how future AR systems may support future pedestrian-AV interactions.

Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

Lower levels of automation are designed to work in specific conditions referred to as the Operational Design Domain (ODD). Beyond these conditions, the human driver is expected to take control. A mismatch between a driver's understanding and expectations of the automated vehicle capabilities and its actual capabilities as prescribed in the Original Equipment Manufacturers (OEMs) manual, could affect their safety and trust in automation. The main aim of this study is to develop a method for assessing the ODD of lane keeping system equipped vehicles. The analysis method is composed of an objective driving risk measure based on the Probabilistic Driving Risk Field (PDRF), and a subjective risk measure based on driver behavior, trust and situation awareness. We demonstrate the method applicability using the Automated Lane Keeping system of the Tesla Model S. A field test was conducted with 19 participants on public roads in the Netherlands including situations within and outside the defined ODD by the OEM. Across all test situations, a mismatch was observed between the ODD specified by the OEM and by the driver. Situations outside the ODD (i.e. no-lane markings and on/off-ramp) were often regarded as within the ODD by the participants. Situations inside the ODD (i.e. tunnel and curve) were mostly correctly classified by the participants. This analysis method has the potential to aid OEMs and road operators in defining more clearly the ODD while taking into account the driver's safety and awareness of the system capabilities.

This paper presents our research platform Safe VRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs.

Safety is one of the major areas of concerns today in the field of automotive development. Different safety measures have and are being introduced in order to improve driver/passenger and pedestrian safety. Advanced driver assist systems (ADAS) are therefore becoming increasingly important in their role of reducing driver crash risk. A shortcoming of the ADAS systems is that the variability in drivers based on skill and experience is not taken into account and the system is often designed for average or worst case driver performance thereby compromising on the dynamic behaviour of the vehicle. This study focuses on understanding and quantifying the differences in drivers. This knowledge of driver differences can be helpful in designing an adaptive ADAS by introducing the driver into the control loop. The study investigates differences between race-car drivers and normal (experienced) drivers in a high-speed driving task. The study analyses simulator data for 17 drivers on the Mallory Park test circuit. The driving task required the participants to drive around the circuit to achieve the fastest lap times. Analysis showed that higher steering activity and differences in path strategy were the main reasons for lower lap-times shown by the expert race drivers compared to the non-expert drivers. Steering metrics like average steering rate, steering jerk showed higher values for the expert group and distance traveled around the corner showed a different path strategy adopted by the experts. Both groups showed improvement in performance based on lap-times across the different sessions. Thus the study shows that expert and non-expert drivers have different steering behaviour and path strategy, which can be attributed to differences in driving experience, vehicle dynamics knowledge and vehicle control skills.

This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.

This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the proposed robust multi-sensor bootstrap filter is able to robustly estimate the quality of service of the sensors. Furthermore, the filter outperforms tracking filters that assume a perfect detection profile. In addition, real world experiments in a fog simulator show that the proposed multi-sensor local-bootstrap-LMB filter outperforms all other filters in foggy conditions.

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle's motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this paper is to develop and evaluate a haptic steering support when driving near the vehicle's handling limits [Haptic Support near the Limits (HSNL)]. The support aims to promote the driver's perception of the vehicle's behavior and handling capacity (the vehicle's internal model) by providing haptic cues on the steering wheel. The HSNL has been evaluated in a test track where 17 test subjects drove around a narrow-twisting tarmac circuit, a vehicle (Opel Astra G/B) equipped with a steering system able to provide variable steering feedback torque. The drivers were instructed to achieve maximum velocity through corners while receiving haptic steering feedback cues related to the vehicle's cornering potentials. The test-track tests led to the conclusion that haptic support reduced drivers' mental and physical demand without affecting their driving performance.

This paper presents a driving simulator experiment, which evaluates a road-departure prevention (RDP) system in an emergency situation. Two levels of automation are evaluated: 1) haptic feedback (HF) where the RDP provides advisory steering torque such that the human and the machine carry out the maneuver cooperatively, and 2) drive by wire (DBW) where the RDP automatically corrects the front-wheels angle, overriding the steering-wheel input provided by the human. Thirty participants are instructed to avoid a pylon-confined area while keeping the vehicle on the road. The results show that HF has a significant impact on the measured steering wheel torque, but no significant effect on steering-wheel angle or vehicle path. DBW prevents road departure and tends to reduce self-reported workload, but leads to inadvertent human-initiated steering resulting in pylon collisions. It is concluded that a low level of automation, in the form of HF, does not prevent road departures in an emergency situation. A high level of automation, on the other hand, is effective in preventing road departures. However, more research may have to be done on the human response while driving with systems that alter the relationship between steering-wheel angle and front-wheels angle.

A car-following assisting system named the rear window notification display (RWND) was developed, with the aim of improving a driver's manual car-following performance. The RWND presented lead-car acceleration and time headway (THW) (i.e., intervehicle distance divided by the speed of the following car) on the rear window of a lead car, which was driven automatically. A simulator-based experiment with 22 participants showed that the RWND reduced both the mean and standard deviation of THW but did not increase the occurrence of potentially unsafe headways of less than 1 s. The parameter estimation of a common linear car-following model showed that drivers accomplished the performance improvements by adopting higher control gains with respect to intervehicle distance, relative speed, and acceleration. A postexperiment questionnaire revealed that the display was generally not regarded as a distraction nor did participants think that it provided too much information, with means of 4.0 and 2.9, respectively, on a scale from one (completely disagree) to ten (completely agree). The results of this study suggest that the RWND can be used along with Cooperative Adaptive Cruise Control to increase traffic flow without degrading safety.

Drivers usually maintain an error-neglecting control strategy (passive phase) in keeping their vehicle on the road, only to change to an error-correcting approach (active phase) when the vehicle state becomes inadequate. We developed an algorithm that is capable of detecting whether the driver is currently error-neglecting or error-correcting in straight lane keeping tasks. The development of this algorithm was part of a larger research project, DrivObs, that aims at creating an advanced driver observation tool. Performance of the algorithm in a straight lane driving task with lateral vehicle position perturbations was tested in a Monte Carlo simulation using Matlab/Simulink. Results show that the algorithm is capable of correctly detecting active or passive phase 90–95% of the time, depending on vehicle speed and algorithm settings.

A car following assisting system named as Rear Window Notification Display (RWND) is developed in order to assist drivers to interact effectively with vehicles equipped with Cooperative Adaptive Cruise Control (CACC) systems. The interface quantifies the acceleration of the instrumented lead car and the following distance in an intuitive way for the human driver on the rear window of the leader. Results of tests with human subjects in a driving simulator indicate that this interface reduces time headway and decreases the variance in time headway that drivers adopt, especially in manoeuvres that involve short term speed changes of the leading car. This system can accelerate the introduction of cooperative driving due to its effectiveness with low penetration rates.

A driving simulator experiment is presented investigating different road departure prevention (RDP) setups. To induce the risk of road departure, thirty test drivers were asked to avoid a pylon-confined area (obstacle) while keeping the vehicle within the road limits. The RDP system intervened by applying a haptic-feedback (i.e., haptic shared control) and/or correcting the steering angle (i.e., drive-by-wire (DBW) input-mixing shared control) in the event that a vehicle road departure was likely to occur. The system that determines the correcting steering input is a RDP controller based on the driver's inputs. The results showed that DBW effectively helped drivers to stay within road limits and reduced workload. The haptic shared control had a significant influence on the measured steering torque, but limited effect on the steering wheel angle and the vehicle path. The DBW system resulted in drivers making counter-corrections demoting their performance. In conclusion, shared control for RDP is effective, although more research needs to be conducted regarding the human response in situations where the relationship between the steering wheel angle and the front wheels' steering angle is altered while driving.

The aim of this study was to find a realistic control-theoretic visual driver model for curve driving that does not only show simular performance as actual drivers but also applies the same inputs and uses the same information. The model structure must enable system identification and parameter estimation of the model parameters. A large number of existing and adapted models have been evaluated and simulated, and when possible, frequency response functions have been identified using two system identification methods. A significant part of the paper is devoted to review these models. The evaluation shows that two-point models comply best with all system identification requirements while still governing realistic driving behavior. It is recommended to investigate further the positioning and perception part of the two-point models using eye-tracking in driving experiments with real human drivers.

In this article the effect of adding motion cues to a car simulation on human operator behavior and simulator acceptance was researched. This was done by performing a car driving experiment, in which three different settings of the classical linear washout algorithm were used. These settings were: no motion, no tilt-coordination and full motion. Objective measures on human control input and driving performance showed that the addition of motion cues positively affected the driving behavior and improved performance. Subjects also subjectively preferred the conditions where all motion cues were present over the conditions without the presence of motion cues.