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.

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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.

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The paper presents measurements of the lateral force and self-aligning torque from cargo and city bicycle tyres. Based on the experimental data, we have determined the parameters for the Magic Formula model, for lateral force and self-aligning torque. We performed tests with VeTyT, an indoor test rig specific for bicycle tyres, under different vertical loads (ranging from 343 N to 526 N), camber angles (−5, 0, 5) deg and inflation pressure (from 300 kPa to 500 kPa). For each condition, we evaluated the cornering stiffness and found that it generally decreases with the increase in inflation pressure for the tour/city bicycle tyres. However, the cargo tyre tested showed an opposite trend, with an increase in the cornering stiffness as the inflation pressure increased from 300 kPa to 400 kPa.

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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.

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Mode awareness is important for the safe use of automated vehicles, yet drivers' understanding of mode transitions has not been sufficiently investigated. In this study, we administered an online survey to 838 respondents to examine their understanding of control responsibilities in partial and conditional driving automation with four types of interventions (brake pedal, steering wheel, gas pedal, and take-over request). Results show that most drivers understand that they are responsible for speed and distance control after brake pedal interventions and steering control after steering wheel interventions. However, drivers have mixed responses regarding the responsibility for speed and distance control after steering wheel interventions and the responsibility for steering control after gas pedal interventions. With a higher automation level (conditional driving automation), drivers expect automation to remain responsible more often compared to a lower automation level (partial driving automation). Regarding Hands-on requirements, more than 99% of respondents answered that drivers would keep their hands on the steering wheel after all intervention types in partial automation, while 60–95% would place their hands on the wheel after various intervention types in conditional automation. A misalignment between actual logic and drivers' expectations regarding control responsibilities is observed by comparing survey responses to the mode transition logic of commercial partially automated vehicles. To resolve confusion about control responsibilities and ensure consistent expectations, we propose implementing a consistent mode design and providing enhanced information to drivers.

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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.

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This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on a non-linear single-track vehicle model to enhance the estimation accuracy of a Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, and both use the standard inertial measurement unit and the tyre forces measured by load sensing technology. CNN benefits from the UKF the capacity to leverage the laws of physics. Concurrently, the UKF uses the CNN outputs as sideslip angle pseudo-measurement and adaptive process noise parameters. The back-propagation through time algorithm is applied end-to-end to the CNN and the UKF to employ the mutualistic property. Using a large-scale experimental dataset of 216 manoeuvres containing a great diversity of vehicle behaviours, we demonstrate a significant improvement in the accuracy of the proposed architecture over the current state-of-art hybrid approach combined with model-based and data-driven techniques. In the case that a limited dataset is provided for the training phase, the proposed hybrid approach still guarantees estimation robustness.

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The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV's driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV's capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.

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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.

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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.

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Existing models of vibration transmission through the seated human body are primarily two-dimensional, focusing on the mid-sagittal plane and in-plane excitation. However, these models have limitations when the human body is subjected to vibrations in the mid-coronal plane. Three-dimensional (3D) human models have been primarily developed for impact analysis. Recently, we showed that such a 3D active human model can also predict vibration transmission. However, existing 3D body models suffer from excessive computational time requirements due to their complexity. To effectively analyze motion comfort, this research presents a 3D computationally efficient human model (EHM), running faster than real-time, with scope for real-time vehicle and seat motion control to enhance comfort. The EHM is developed by considering various combinations of body segments and joint degrees of freedom, interacting with multibody (MB) and finite element (FE) seat compliance models. Postural stabilization parameters are estimated using an optimization process based on experimental frequency-dependent gain responses for different postures (erect/slouched) and backrest support (low/high) conditions. The model combines two postural control mechanisms: 1) joint angle control capturing reflexive and intrinsic stabilization for each degree of freedom with PID controllers, including integration to eliminate drift, and 2) head-in-space control minimizing 3D head rotation. Interaction with a compliant seat was modeled using deformable finite elements and multibody contact models. Results showed the importance of modeling both compressive and shear deformation of the seat and the human body. Traditional stick-slip multibody contact failed to reproduce seat-to-human vibration transmission. Combining efficient body modeling principles, innovative postural adaptation techniques, and advanced seat contact strategies, this study lays a robust foundation for predicting and optimizing motion comfort.

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In conditionally automated driving (SAE level 3), drivers may take their eyes off the road but will still need to be ready to take control and will, therefore, benefit from information on automation. This study aims to investigate the effectiveness of automation manoeuvre information provided through spatial sound, traditional notification sound (beep), and a visual interface. Spatial sounds were designed differentiating four distinct driving manoeuvres: overtaking a leading car, slowing down, turning right, and passing a roundabout. The notification sound consisted of one beep being identical for all manoeuvres. The visual interface showed the automation mode with an image and manoeuvre information with text and images. The impact of these interfaces on trust, workload, acceptance, situation awareness, and sense of control was evaluated with questionnaires and visual attention was evaluated with eye tracking while participants engaged in a visual-motor secondary task in a driving simulator. The results indicate that, with all interfaces tested, manoeuvre information enhances trust, acceptance, situation awareness, and sense of control, without significantly affecting the overall workload. These benefits were more profound, adding auditory information and differed marginally between the traditional notification and the spatial sound, as the effectiveness of the different auditory interface types varied depending on the specific manoeuvre. Findings highlight the importance of designing user interfaces for automation manoeuvre information using auditory cues to improve the user experience in automated driving.@en

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.

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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.

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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%).

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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.

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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.@en

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.

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The use of bicycles as a cheap and healthy way to travel the “last mile” is spreading widely in the cities. This new way of dealing with short trips, known as “micro-mobility”, is also fostered by the new awareness of the global impact of ICE vehicles and rising fuel costs. In recent years, also cargo bikes are knowing a large use, both for families with children and for delivery purposes. They are featured by a long frame that can carry loads usually placed in between the rider and the front wheel. This requires fairly skilled riders to deal with driving dynamics, different from the common bicycle we are used to (Miller, M., 2023). They can easily reach a speed of 25 km/h (according to the regulations in most EU countries) being usually pedal-assisted. Tyre characteristics may strongly affect bicycle dynamics (Bulsink, V., 2015). This applies even more for cargo bikes as they are featured by remarkable load variation (load/unload configuration), relatively high speed and torque applied to the tyres, both during acceleration and braking phases. In this context, it is important to have a good understanding of tyre characteristics. With the aim of designing safer and more performant bicycles, numerical models are required. Furthermore, existing mechanical models of bicycles mostly ignore tyre dynamics and need to be updated with realistic tyre models (Dell’Orto, G., 2022). Measurements were performed with VeTyT, an indoor test-rig specific for bicycle tyres, designed at the Department of Mechanical engineering of Politecnico di Milano (Figure 1) (Dell’Orto, G., 2022). It is the only test-rig for bicycle tyres complying to the standard ISO 9001-2015. We can measure lateral force and self-aligning torque, as tyre parameters vary. The tyre 20”x2,15 was mounted on a standard aluminum rim and tested on flat track. The specific dimensions of the cargo bicycle wheel forced us to update the test-rig, designing a new steel fork to ensure sufficient stiffness and new steel plates to carry the wheel on flat track (Figure 2). Inflation pressure was set to 400 kPa, as recommended by the manufacturer. Tests were performed applying a vertical load of 411 N and 526 N, according to the technical limits of the test-rig. The camber was set to 0 degree, as first stage of the study. The lateral force and self-aligning torque as function of the slip angle are shown in Figure 3 and Figure 4, respectively. It is clear the difference in outcomes adjusting the vertical load. As the vertical load increases, both the lateral force and the self-aligning torque increase in magnitude as well. As expected, the tyre can generate higher forces with higher vertical load. It is worth noticing that the peak value of lateral force will be reached for very large slip angles (> |6| degrees, as maximum value tested in this study). Tyres for cargo bicycles are designed to carry large loads, therefore we expect to reach saturation conditions for higher vertical forces or, conversely, large slip angles. The cornering stiffnesses are reported in Table 1: for vertical load 526 N it is 23% higher than that found at 411 N.@en

The goal of this paper is to contribute to the accurate prediction of human body motion by proposing a novel head-neck model for dynamic driving scenarios with complex vehicle motions. While automated vehicles are considered a potential solution to several transportation issues, there are still significant challenges that need to be addressed, including fundamental questions regarding motion comfort and postural stability. Existing standards fail to accurately describe motion comfort, and current head-neck models have limitations, such as their inability to accurately capture human head responses to dynamic perturbations and lack of adaptability to different perturbations, amplitudes, and individual characteristics. To address these challenges, the authors propose a 3D double inverted pendulum model (DIPM) with a total of 6 degrees of freedom (DoF) as an approximation of head-neck system. The proposed model uses Model Predictive Control (MPC) to derive optimal control inputs for head-neck stabilization. The study validates the proposed model against experimental data of anterior-posterior seat translation and rotation from the literature. The results indicate that the model fitted the experimental data with a variance accounted for 82.80 % in translation and 73.15 % in rotation (pitch). The proposed model paves the path for the accurate assessment of occupants’ postural stability in automated vehicles.

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