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Driving simulators are an increasingly important tool to develop vehicle functionalities and to study driver or passenger responses. A major hindrance to the use and validity of such studies is Simulator Sickness (SS). Several studies have suggested a positive relation between improvements in simulator fidelity and the likelihood of sickness. We hypothesized that this relation only holds true for static (fixed-base) simulators, and that increased fidelity in fact reduces simulator sickness in dynamic (moving-base) simulators. We performed a meta-analysis investigating the relation between sickness and fidelity in static and dynamic systems. A literature search yielded a total of 41 simulator studies that varied aspects of mechanical and/or visual fidelity and assessed SS for the same driving conditions and the same or equivalent participant groups. Evaluation of a model synthesizing the findings of these studies indicates that SS decreases with visual fidelity, and suggests that this effect may be negated for static simulators. The results of the modeling efforts thereby provide some support for the hypothesis that increased fidelity can reduce SS in dynamic simulators. Based on the evaluation of the literature we also note particular shortcomings and gaps in available research. Finally, we make recommendations for specific experiments that may fill these gaps and allow definitive conclusions on the role of simulator fidelity in SS.

The operation of automated vehicles (AVs) on shared roads requires attention concerning their interactions with vulnerable road users (VRUs), such as cyclists. This study investigates the safety of cyclists when they interact with an AV and compares it with their interaction with a conventional vehicle. Overall, 29 cyclists participated in a controlled field experiment consisting of interaction scenarios in which a vehicle approached the cyclist from behind. Four interaction scenarios were included: manual and automated following and manual and automated overtaking of the cyclist. The vehicle operated in all scenarios in a manual mode for safety reasons. However, before each ride, participants received information about the vehicle’s operation mode (automated or manual). The following attributes were considered: overtaking speed, overtaking lateral distance, following distance, and roadside objects. The objective and the subjective risks were evaluated in each scenario. The objective risk was assessed using the probabilistic driving risk field, and the subjective risk was assessed based on the cyclists’ self-reported risk values, cycling behavior, and their trust in AVs. The results show that automated and manual following have similar objective and subjective risks, while automated overtaking has a higher level of objective and subjective risks than manual overtaking. The results also show that a longer interaction time leads to an increase in cycling speed and a decrease in the lateral distance of the cyclist to the curb. Thus, we conclude that automated following is a safer option for short traveling distances, while for longer traveling distances, manual overtaking is preferred. Additionally, a short lateral distance from the cyclist when overtaking increases the subjective and objective risks.

Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust. The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.

A realistic steering feel is one of the key elements to guarantee fidelity on a driving simulator in general, and in particular to replicate on-centre vehicle handling. This requires precise modelling of the steering dynamics, a high bandwidth control loading system, and coupling of virtual and physical components in agreement with computational requirements and hardware limitations. For such a coupling, the common approach position-based control uses the measured signals of steering wheel angle and steering rate as inputs to the steering system model. This paper proposes an alternative torque-based control scheme using steering torque as an input to the steering system with additional compensation. Torque-based control was designed and evaluated in conjunction with detailed electric power steering models including state-of-the-art friction models, a neuromuscular driver model, and two driver-in-the-loop experiments in a high-end driving simulator. The objective analysis performed by means of the neuromuscular driver model reveals that the driver applies less impedance i.e. the driver is less stiff on a driving simulator when steering feedback is provided with the torque-based control compared to the position-based control. The investigations demonstrate that torque-based control reduces haptic response delay and vibrations caused by friction modelling compared to position-based control. The driver-in-the-loop experiments show significant objective effects on steering performance and subjective evaluation of fidelity and effort. We conclude that the proposed approach closes the vehicle-driver loop with more realism in a driving simulator.

The design of take-over requests in automated vehicles traditionally focuses on safety and reaction time. We are interested in how take-over requests can be designed from a broader user experience perspective while ensuring safety. This paper proposes designs for ambient sound (i.e., soundscape) and driving noise to inform the driver of transition situations. Drivers must take-over control within the time budget, the time from the take-over request to the automation system limit. The time required for a safe transition depends on the complexity of the driving environment. In a scheduled take-over, which is not an emergency, there is an opportunity for an interaction that gradually introduces the driver into the transition process. Ambient sound is expected to lead the driver back to the loop with comfort, creating a novel transition experience as well as safety.

Microscopic traffic simulation is a popular tool in traffic research and planning. It enables the evaluation of interventions in a traffic system based on the movement of individual simulated agents. These can be for example infrastructural changes or the introduction of new road users like automated vehicles. To simulate cyclists, traditional simulation techniques have to be adapted. We propose a microscopic model describing bicycle interaction using the social force paradigm. Here we take into account bicycle kinematics, to create realistic bicycle paths. Qualitative evaluation shows simulated interactions with plausible cyclist trajectories.

Augmented Reality (AR) technology could be utilised to assist pedestrians in navigating safely through traffic. However, whether potential users would understand and use such AR solutions is currently unknown. Nine novel AR interfaces for pedestrian-vehicle communication, previously developed using an experience-based design method, were evaluated through an online questionnaire study completed by 992 respondents in Germany, the Netherlands, Norway, Sweden, and the United Kingdom. The AR indicated whether it was safe to cross the road in front of an approaching automated vehicle. Each interface was rated for its intuitiveness and convincingness, aesthetics, and usefulness. Moreover, comments were collected for qualitative analysis. The results indicated that interfaces that employed traditional design elements from existing traffic, and head-up displays, received the highest ratings overall. Statistical results also showed that there were no significant effects of country, age, and gender on interface acceptance. Thematic analysis of the textual comments offered detail on each interface design's stronger and weaker points, and revealed unintended effects of certain designs. In particular, some of the interfaces were commented on as being dangerous or scary, or were criticised that they could be misinterpreted in that they signal that something is wrong with the vehicle, or that they could occlude the view of the vehicle. The current findings highlight the limitations of experience-based design, and the importance of applying legacy design principles and involving target users in design and evaluation. Future research should be conducted in scenarios in which pedestrians actually interact with approaching vehicles.

Increasing levels of vehicle automation are envisioned to allow drivers to engage in other activities but are also likely to increase the incidence of Carsickness or Motion Sickness (MS). Ideally, MS is studied in a safe and controlled environment, such as a driving simulator. However, only few studies address the suitability of driving simulators to assess MS. In this study, we validate a moving base driving simulator for MS research by comparing the symptoms and time course of MS between a real-road driving scenario and a rendition of this scenario in a driving simulator, using a within-subjects design. 25 participants took part as passengers in an experiment with alternating sections (slaloming, stop-and-go) with normal and provocative driving styles. Participants performed Sudoku puzzles (eyes-off-road) during both scenarios and reported MIsery SCale (MISC) scores at 30 s intervals. Motion Sickness Assessment Questionnaire (MSAQ) scores were collected upon completion of either scenario. Overall, the results indicate that MS was more severe in the car than in the simulator. Nevertheless, significant correlations were found between individual MS in the car and simulator for 3 out of 4 MSAQ symptom categories (0.48 < r < 0.73, p < 0.02), with a strong overall correlation (r = 0.57, p = 0.004). MS onset times were similar between the car and the simulator, and sickness fluctuations as a result of driving style showed a similar pattern between scenarios, albeit more pronounced in the car. Based on observed similarities in MS, we conclude these simulator results to have relative validity. We attribute the observed reduction of MS severity in the simulator to the downscaling of the motion by the Motion Cueing Algorithm (MCA). These results suggest that, at least in eyes-off-road conditions, findings on MS from simulator studies may generalize to real vehicles after application of a conversion factor. This conversion factor is likely to depend on simulator and MCA characteristics.

Automated driving needs to be comfortable to encourage the broad acceptance and usage of automated vehicles (AVs). However, current research provides limited knowledge on the descriptions and influencing factors of user comfort in automated driving, especially from the perspective of an AV’s driving styles. This paper presents results from an online workshop, in which nine experts with hands-on experience of AVs, and a long track record of research in this context, brainstormed and discussed a series of topics related to user comfort in automated driving. Results showed that a wide range of terms were used to describe user comfort and discomfort, when the attendees considered being driven by either currently available transport modes (e.g., taxi/bus/train) or higher-level AVs (with neither requiring control of the vehicle by the user). All of the terms used for existing vehicles were found to apply to AVs, but additional aspects of comfort/discomfort were revealed for AVs. This suggests that what makes existing transport modes comfortable is not the same as that for higher-level AVs. Regarding the relationship between comfort and discomfort in automated driving, seven and ten categories of terms were identified for comfort and discomfort, respectively, with both the number and the nature of these categories differing across the two states. This suggests that, to ensure user comfort in automated driving, additional factors that affect comfort must be considered, rather than solely mitigating discomfort. By integrating these multiple aspects that affect user comfort, knowledge from the workshop was used to develop a conceptual framework, to explain how AV driving styles, as well as other, non-driving-related factors, affect user comfort, from both a physical and psychological perspective. This work provides an overview of experts’ insights on the factors that affect comfort in automated driving, and will facilitate a more comprehensive definition, and more accurate measurement, of user comfort, facilitating the design of more comfortable and acceptable automated driving for future vehicles.

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.

Driving simulators are an increasingly important tool to develop vehicle functionalities and to study driver or passenger responses. A major hindrance to the use and validity of such studies is Simulator Sickness (SS). Several studies have suggested a positive relation between improvements in simulator fidelity and the likelihood of sickness. We hypothesized that this relation only holds true for static (fixed-base) simulators, and that increased fidelity in fact reduces simulator sickness in dynamic (moving-base) simulators. We performed a meta-analysis investigating the relation between sickness and fidelity in static and dynamic systems. A literature search yielded a total of 41 simulator studies that varied aspects of mechanical and/or visual fidelity and assessed SS for the same driving conditions and the same or equivalent participant groups. Evaluation of a model synthesizing the findings of these studies indicates that SS decreases with visual fidelity, and suggests that this effect may be negated for static simulators. The results of the modeling efforts thereby provide some support for the hypothesis that increased fidelity can reduce SS in dynamic simulators. Based on the evaluation of the literature we also note particular shortcomings and gaps in available research. Finally, we make recommendations for specific experiments that may fill these gaps and allow definitive conclusions on the role of simulator fidelity in SS.

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.

In future automated vehicles we will often engage in non-driving tasks and will not watch the road. This will affect postural stabilization and may elicit discomfort or even motion sickness in dynamic driving. Future vehicles will accommodate this with properly designed seats and interiors, whereas comfortable vehicle motion will be achieved with smooth driving styles and well-designed (active) suspensions. To support research and development in dynamic comfort, this paper presents the validation of a multi-segment full-body human model, including visuo-vestibular and muscle spindle feedback, for postural stabilization. Dynamic driving is evaluated using a “sickening drive”, including a 0.2 Hz 4 m/s2 slalom. Vibration transmission is evaluated with compliant automotive seats, applying 3D platform motion and evaluating 3D translation and rotation of pelvis, trunk and head. The model matches human motion in dynamic driving and reproduces fore–aft, lateral and vertical oscillations. Visuo-vestibular and muscle spindle feedback are shown to be essential, in particular, for head–neck stabilization. Active leg muscle control at the hips and knees is shown to be essential to stabilize the trunk in the high-amplitude slalom condition but not with low-amplitude horizontal vibrations. However, active leg muscle control can strongly affect 4–6 Hz vertical vibration transmission. Compared to the vibration tests, the dynamic driving tests show enlarged postural control gains to minimize trunk and head roll and pitch and to align head yaw with driving direction. Human modelling can enable the insights required to achieve breakthrough comfort enhancements, while enabling efficient developments for a wide range of driving conditions, body sizes and other factors. Hence, modelling human postural control can accelerate the innovation of seats and vehicle motion-control strategies for (automated) vehicles.

A prime concern for automated vehicles is motion comfort, as an uncomfortable ride may reduce acceptance of the technology amongst the general population. However, it is not clear how transient motions typical for travelling by car affect the experience of comfort. Here, we determine the relation between properties of vehicle motions (i.e., acceleration and jerk) and discomfort empirically, and we evaluate the ability of normative models to account for the data. 23 participants were placed in a moving-base driving simulator and presented sinusoidial and triangular motion pulses with various peak accelerations (Amax0.4 − 2 ms−2) and jerks (Jmax0.5 − 15 ms−3), designed to recreate typical vehicle accelerations. Participants provided discomfort judgments on absolute ‘Verbal Qualifiers’ and relative ‘Magnitude Estimates’ associated with these motions. The data show that discomfort increases with acceleration amplitude, and that the strength of this effect depends on the direction of motion. We furthermore find that higher jerks (shorter duration pulses) are considered more comfortable, and that triangular pulses are more comfortable than sinusoidal pulses. ME responses decrease (i.e., reduced discomfort) with increasing pulse duration. Evaluations of normative models of vibration and shock (ISO 2631), and perceived motion intensity provide mixed results. The vibration model could not account for the data well. Reasonable agreement between predictions and observations were found for the shock model and perceived intensity model, which emphasize the role of acceleration. We present novel statistical models that describe motion comfort as a function of acceleration, jerk, and direction. The present findings are essential to develop motion planning algorithms aimed at maximizing comfort.

We investigated and modeled the temporal evolution of motion sickness in a highly dynamic sickening drive. Slalom maneuvers were performed in a passenger vehicle, resulting in lateral accelerations of 0.4 g at 0.2 Hz, to which participants were subjected as passengers for up to 30 min. Subjective motion sickness was recorded throughout the sickening drive using the MISC scale. In addition, physiological and postural responses were evaluated by recording head roll, galvanic skin response (GSR) and electrocardiography (ECG). Experiment 1 compared external vision (normal view through front and side car windows) to internal vision (obscured view through front and side windows). Experiment 2 tested hypersensitivity with a second exposure a few minutes after the first drive and tested repeatability of individuals’ sickness responses by measuring these two exposures three times in three successive sessions. An adapted form of Oman’s model of nausea was used to quantify sickness development, repeatability, and motion sickness hypersensitivity at an individual level. Internal vision was more sickening compared to external vision with a higher mean MISC (4.2 vs. 2.3), a higher MISC rate (0.59 vs. 0.10 min−1) and more dropouts (66% vs. 33%) for whom the experiment was terminated due to reaching a MISC level of 7 (moderate nausea). The adapted Oman model successfully captured the development of sickness, with a mean model error, including the decay during rest and hypersensitivity upon further exposure, of 11.3%. Importantly, we note that knowledge of an individuals’ previous motion sickness response to sickening stimuli increases individual modeling accuracy by a factor of 2 when compared to group-based modeling, indicating individual repeatability. Head roll did not vary significantly with motion sickness. ECG varied slightly with motion sickness and time. GSR clearly varied with motion sickness, where the tonic and phasic GSR increased 42.5% and 90%, respectively, above baseline at high MISC levels, but GSR also increased in time independent of motion sickness, accompanied with substantial scatter.

Purpose The goal of this study was to assess differences in low back stabilization and underlying mechanisms between patients with low back pain (LBP) and healthy controls. It has been hypothesized that inadequate trunk stabilization could contribute to LBP through high tissue strains and/or impingement. Evidence to support this is inconsistent, and not all methods that have been used to study trunk stabilization are equally suitable. We have recently developed a method to assess intrinsic and reflexive contributions to trunk stabilization, which aims to circumvent the limitations of previous studies. Methods Forty-nine participants suffering from chronic LBP and a control group of fifty healthy subjects participated in this study. Trunk stabilization was measured using force-controlled perturbations directly applied to the trunk. The actuator displacement and contact force between the actuator and subject were measured as well as electromyography (EMG) of the M. Longissimus. Underlying mechanisms were characterized using system identification. Results LBP patients showed lower admittance, i.e., less displacement per unit of force applied, mainly due to higher position, velocity and acceleration feedback gains. Among patients, lower trunk admittance and higher reflex gains were associated with more negative pain-related cognitions. Conclusion Trunk stabilization differs between LBP patients and controls, with the same perturbations causing less trunk movement in patients, due to stronger reflexes. We interpret these changes as reflecting protective behavior. Graphic abstract These slides can be retrieved under Electronic Supplementary Material.

To encourage appropriate use of driving automation, we need to understand and monitor driver's trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events. 30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined. Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving. Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation. At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.

Vibration transmission is essential in the design of comfortable vehicle seats but knowledge is lacking on 3D trunk and head motion and the role of seat back and posture. We hypothesized that head motion is reduced when participants upper back is unsupported, as this stimulates active postural control. We developed an experimental methodology to evaluate 3D vibration transmission from compliant seats to the human body. Wide-band (0.1-12 Hz) motion stimuli were applied in fore-aft, lateral and vertical direction to evaluate the translational and rotational body response in pelvis, trunk and head. A standard car seat was equipped with a configurable and compliant back support to test 3 support heights and 3 sitting postures (erect, slouched, and preferred) where we also tested head down looking at a smartphone. Seat back support height and sitting posture substantially affected vibration transmission and affected low-frequency responses in particular for body segment rotation. According to our hypothesis a low support height proved beneficial in reducing head motion. Relevance to industry: Our methodology effectively evaluates 3D wide-band vibration transmission from compliant seats to the human body. The lowest back support height reduced head motion but was perceived as least comfortable. This calls for seat designs which support but do not so much constrain the upper back. The head down posture enlarged head motion, pleading for computer system integration allowing heads up postures in future automated cars. The biomechanical data will serve to validate human models supporting the design of comfortable (automated) vehicles., submitted to Elsevier

We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the environment and entity-specific context cues of the vehicle and pedestrian (incl. awareness), which affect their future motion and allow to increase the prediction horizon. These sub-graphs share a latent state which models whether vehicle and pedestrian are on collision course; this accounts for a certain degree of motion coupling. The method was validated with real-world data obtained by onboard vehicle sensing (stereo vision, GNSS and proprioceptive). Data consist of 93 vehicle and pedestrian encounters, spanning various awareness conditions and dynamic characteristics of the participants. In ablation studies, we quantify the benefits of various components of our proposed DBN model for path prediction and collision risk estimation. Results show that at a prediction horizon of 1.5 s, context aware models outperform context-agnostic models in path prediction for scenarios with a dynamics change, while performing similarly otherwise. Results further indicate that driver attention aware models improve collision risk estimation compared to driver-agnostic models.

The use of driving simulators for training and for development of new vehicles is widely spread in the automotive industry. In the last decade, a few motorcycle riding simulators have been developed for similar purposes, with focus on maneuvering at high speed. This article presents the subjective and objective evaluation of a motorcycle riding simulator specifically for low speed longitudinal and lateral maneuvering, between 0 and 10 ms–1. An experiment was conducted with and without platform motion, focusing on three maneuvers: acceleration from standstill, braking to standstill and turning at constant speed. Participants briefly evaluated the fidelity of the simulator after each maneuver and more extensively after each motion condition. Behavioral fidelity was evaluated using experimental data measured on an instrumented motorcycle. Overall, the results show that the participants could reproduce the selected maneuvers without falling or losing balance, reporting a sufficient level of simulator realism. In terms of subjective fidelity, platform motion had a positive effect on simulator presence, significantly increasing the feeling of being involved in the virtual environ0ment. In terms of behavioral fidelity, the comparison between the simulator and experimental results shows good agreement, with a limited positive influence of motion for the braking maneuver, which indicates that for this maneuver the use of motion is beneficial to reproduce the real-life experience and performance.

SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver. We investigated the influence of a colour themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in 3 groups with a baseline auditory HMI and two advanced colour themed HMIs consisting of a display and ambient light with the colours red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate. Compared to the baseline HMI, the colour themed HMI is more trustworthy and participants understood their driving tasks better. Results show that the colour themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient light is seen as more urging than blue but HMI colour did not significantly affect the general HMI perception and TOR performance.

High levels of vehicle automation are expected to increase the risk of motion sickness, which is a major detriment to driving comfort. The exact relation between motion sickness and discomfort is a matter of debate, with recent studies suggesting a relief of discomfort at the onset of nausea. In this study, we investigate whether discomfort increases monotonously with motion sickness and how the relation can best be characterized in a semantic experiment (Experiment 1) and a motion sickness experiment (Experiment 2). In Experiment 1, 15 participants performed pairwise comparisons on the subjective discomfort associated with each item on the popular MIsery SCale (MISC) of motion sickness. In Experiment 2, 17 participants rated motion sickness using the MISC during exposures to four sustained motion stimuli, and provided (1) numerical magnitude estimates of the discomfort experienced for each level of the MISC, and (2) verbal magnitude estimates with seven qualifiers, ranging between feeling ‘excellent’ and ‘terrible’. The data of Experiment 1 show that the items of the MISC are ranked in order of appearance, with the exception of 5 (‘severe dizziness, warmth, headache, stomach awareness, and sweating’) and 6 (‘slight nausea’), which are ranked in opposite order. However, in Experiment 2, we find that discomfort associated with each level of the MISC, as it was used to express motion sickness during exposure to a sickening stimulus, increases monotonously; following a power law with an exponent of 1.206. While the results of Experiment 1 replicate the non-linearity found in recent studies, the results of Experiment 2 suggest that the non-linearity is due to the semantic nature of Experiment 1, and that there is a positive monotonous relation between MISC and discomfort in practice. These results support the suitability of MISC to assess motion sickness.

Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.

Vehicles are increasingly equipped with sensors that measure the state of the vehicle and surrounding road users. Although most of these sensor data currently remain local to the vehicle, the data could be shared with the aim to improve road safety. We postulate that there is a range of scenarios regarding data sharing, with two extremes: In scenario 1, the acquired shared data will be analysed regarding the how, where, and who of road traffic errors, violations, and accidents; actions can then be taken to improve automated driving systems, manage accident hotspots, and provide personalised feedback, rewards, or penalties to road users. In scenario 2, the recorded data will not be shared, because of privacy concerns. We conclude that there exists a tension between a position of utilitarian use of data and a position of privacy.

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

The present study investigated the attitudes and acceptance of automated shuttles in public transport among 340 individuals physically experiencing the automated shuttle ‘Emily’ from Easymile in a mixed traffic environment on the semi-public EUREF (Europaisches Energieforum) campus in Berlin. Automated vehicle acceptance was modelled as a function of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs performance expectancy, effort expectancy, social influence, and facilitating conditions, the Diffusion of Innovation Theory (DIT) constructs compatibility and trialability, as well as trust and automated shuttle sharing. The results show that after adding the DIT constructs, automated shuttle sharing, and trust to the model, the effect of performance expectancy on behavioural intention was no longer significant. Instead, compatibility with current travel was the strongest predictor of behavioural intention to use automated shuttles. It was further found that individuals who are willing to share rides in an automated shuttle with fellow travelers (i.e., automated shuttle sharing) and who trust automated shuttles (i.e., trust) are more likely to intend to use automated shuttles (i.e., behavioural intention). The highest mean rating was obtained for believing that automated shuttles are easy to use, while the lowest mean rating was obtained for feeling safe inside the automated shuttle without any type of supervision. The analysis revealed a preference for the supervision of the automated shuttle via an external control room to the supervision by a human steward onboard. We recommend future research to investigate the hypothesis that compatibility could serve as an even stronger predictor of the behavioural intention to use automated shuttles in public transport than performance expectancy.

Percepts of verticality are thought to be constructed as a weighted average of multisensory inputs, but the observed weights differ considerably between studies. In the present study, we evaluate whether this can be explained by differences in how visual, somatosensory and proprioceptive cues contribute to representations of the Head In Space (HIS) and Body In Space (BIS). Participants (10) were standing on a force plate on top of a motion platform while wearing a visualization device that allowed us to artificially tilt their visual surroundings. They were presented with (in)congruent combinations of visual, platform, and head tilt, and performed Rod & Frame Test (RFT) and Subjective Postural Vertical (SPV) tasks. We also recorded postural responses to evaluate the relation between perception and balance. The perception data shows that body tilt, head tilt, and visual tilt affect the HIS and BIS in both experimental tasks. For the RFT task, visual tilt induced considerable biases (≈ 10° for 36° visual tilt) in the direction of the vertical expressed in the visual scene; for the SPV task, participants also adjusted platform tilt to correct for illusory body tilt induced by the visual stimuli, but effects were much smaller (≈ 0.25°). Likewise, postural data from the SPV task indicate participants slightly shifted their weight to counteract visual tilt (0.3° for 36° visual tilt). The data reveal a striking dissociation of visual effects between the two tasks. We find that the data can be explained well using a model where percepts of the HIS and BIS are constructed from direct signals from head and body sensors, respectively, and indirect signals based on body and head signals but corrected for perceived neck tilt. These findings show that perception of the HIS and BIS derive from the same sensory signals, but see profoundly different weighting factors. We conclude that observations of different weightings between studies likely result from querying of distinct latent constructs referenced to the body or head in space.

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

Objective To investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle’s road scene perception. Background An appropriate method is required to identify how driver gaze reveals awareness of other road users. Method We developed a recognition-based method for labeling of driver situation awareness (SA) in a vehicle with road-scene perception and eye tracking. Thirteen drivers performed 91 left turns on complex urban intersections and identified images of encountered road users among distractor images. Results Drivers fixated within 2° for 72.8% of relevant and 27.8% of irrelevant road users and were able to recognize 36.1% of the relevant and 19.4% of irrelevant road users one min after leaving the intersection. Gaze behavior could predict road user relevance but not the outcome of the recognition task. Unexpectedly, 18% of road users observed beyond 10° were recognized. Conclusions Despite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers. Perception occurred at gaze angles well beyond 2°, which means that fixation locations are insufficient for awareness monitoring. Application Findings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.

A necessary condition for the effective integration of automated vehicles in our daily lives is their acceptance by passengers inside and pedestrians and cyclists outside the automated vehicle. 119 respondents experienced an automated shuttle ride with a ‘hidden steward on board’ in a mixed traffic environment in Berlin-Schoneberg. A mixed-method approach was applied gathering qualitative interview data during the ride and quantitative questionnaire data after the ride. Responses were classified into three main categories: (1) Perceived safety, (2) interactions with automated shuttles in crossing situations, and (3) communication with automated shuttles. Respondents associated their perceptions of safety with the low speed, dynamic object and event identification, longitudinal and lateral control, pressing the emergency button inside the shuttle, their general trust in technology, sharing the shuttle with fellow travellers, the operation of the shuttle in a controlled environment, and the behaviour of other road users outside the shuttle. Respondents pressed the emergency button inside the automated shuttle on 28 out of 62 test rides in order to test its behavior. They further expected to be more cautious in crossing the road before an automated shuttle due to the lack of eye contact with the human driver and a lack of trust in the behavior of the automated shuttle, and expected road users testing the automated shuttle due to the conservative driving behavior of automated shuttles. We recommend future research into the hypothesis that the acceptance of automated shuttles will be associated with the perceived safety of and their effective and intuitive interaction and communication with both passengers and other road users.

Objective We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving. Background Humans are likely inadequate for supervising today’s on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized control only if needed. A distraction DMS where perceived false alarms are minimized and the status of the backup is unannounced might reduce problems of distrust and overreliance, respectively. Experimental research is needed to assess the viability of such designs. Methods In a driving simulator, 91 participants either supervised driving automation ( auto-hand-on-wheel vs. auto-hands-off-wheel), drove with different forms of DMS-induced backup control ( eyes-only-backup vs. eyes-plus-context-backup; visible-backup vs. invisible-backup), or drove without any automation. All participants performed a visual N-back task throughout. Results Supervised driving automation increased visual distraction and hazard non-responses compared to backup and conventional driving. Auto-hand-on-wheel improved response generation compared to auto-hands-off-wheel. Across entire driving trials, the backup improved lateral performance compared to conventional driving. Without negatively impacting safety, the eyes-plus-context-backup DMS reduced unnecessary automated control compared to the eyes-only-backup DMS conditions. Eyes-only-backup produced low satisfaction ratings, whereas eyes-plus-context-backup satisfaction was on par with automated driving. There were no appreciable negative consequences attributable to the invisible-backup driving automation. Conclusions We have demonstrated preliminary feasibility of DMS designs that incorporate driving context information for distraction assessment and suppress their status indication. Application An appropriately designed DMS can enable benefits for automated driving as a backup.

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (

We present an approach to assess the risk taken by on-road vehicles within the framework of artificial field theory, envisioned for safety analysis and design of driving support/automation applications. Here, any obstacle (neighboring entity on the road) to the subject vehicle is treated as a finite scalar risk field that is formulated in the predicted configuration space of the subject vehicle. The driving risk estimate is the strength of the risk field at the subject vehicle’s future location. This risk field is formulated as the product of two factors: collision probability and expected crash energy. The collision probability with neighboring vehicles is estimated based on probabilistic motion predictions. The risk can be assessed for a single time step or over multiple future time steps, depending on the required temporal resolution of the estimates. We verified the single step approach in three near-crash situations from a naturalistic dataset and in cut-in and hard-braking scenarios with simulation and showed the application of the multi-step approach in selecting the safest path in a lane-drop section. The risk descriptions from the proposed approach qualitatively reflect the narration of the situation and are in general consistent with Time To Collision. Compared to current surrogate measures of safety, the proposed risk estimate provides a better basis to assess the driving safety of an individual vehicle by considering the uncertainty over the future ambient traffic state and magnitude of expected crash consequences. The proposed driving risk model can be used as a component of intelligent vehicle safety applications and as a comprehensive surrogate measure for assessing traffic safety.

The study investigated the interrelationships between the UTAUT2 (Unified Theory of Acceptance and Use of Technology) constructs predicting intentions to use driverless automated shuttles. Survey data was gathered from individuals physically experiencing an automated shuttle in a mixed traffic environment on public roads in Trikala (Greece) as part of the CityMobil2 project. Structural equation modelling showed that intentions to use automated shuttles were most strongly driven by hedonic motivation, followed by performance expectancy and social influence. Performance expectancy mediated the relationship between effort expectancy and behavioural intention. This means that people's expectations of how difficult automated shuttles will be to use and their intentions to use them can be explained through their expectations around how these vehicles will perform. Technology savviness was a negative moderator of the relationship between social influence and performance expectancy and facilitating conditions, respectively. Tech-savvy individuals rely less on their social networks to nurture their beliefs that automated shuttles are useful and to have the necessary resources to use automated shuttles. Car use was a negative predictor of the intention to use automated shuttles. Future research should revisit the interrelationships between the UTAUT2 constructs and apply (quasi-) experimental studies to unravel the temporal interaction between constructs.Relevance to human factors/Relevance to ergonomics theoryThe study investigated the interrelationships between the UTAUT2 (Unified Theory of Acceptance and Use of Technology) constructs predicting intentions to use driverless automated shuttles. Survey data from individuals physically experiencing an automated shuttle in Trikala (Greece) as part of the CityMobil2 project was analysed by structural equation modeling. Examining the interrelationships between the UTAUT2 constructs enables us to identify underlying beliefs and devise adequate strategies to promote automated vehicle acceptance. Supplemental data for this article is available online at https://doi.org/10.1080/1463922X.2020.1814446.

Evaluating safety risk posed to third parties on the ground due to UAS impact requires a model of probability of fatality (PoF) for human. For quadrotor UAS, the existing impact models predict remarkably different PoFs. The most pessimistic is the impact model adopted by Range Commanders Council (RCC) while the Blunt Criterion model is far more optimistic. The ASSURE study has assessed the third set of PoF values through conducting controlled drop tests of a DJI Phantom III on a crash dummy; these results differ again. To investigate these discrepancies, this paper employs a numerical impact analysis of UAS collisions on humans. The current paper is the third in a series of studies. The first study developed a MultiBody System (MBS) simulation model of a DJI Phantom III impacting the head of a crash dummy; this MBS model has been validated against the experimental drop test results of ASSURE. The second study conducted simulations with the validated MBS model to systematically show the differences in head and neck injuries if the human dummy is replaced by a validated MBS model of a human body. The aim of the current paper is threefold: i) to extend the latter MBS model to assess injury levels for DJI Phantom III impact on thorax and abdomen; ii) to transform the assessed injury levels for head, thorax and abdomen to PoFs; and iii) to compare the MBS obtained PoFs to those from RCC and Blunt Criteria models. The MBS based results show that variations in the scenario of DJI Phantom III impact on the head significantly affect PoF. These variations are not captured by the RCC or BC model, and neither in the ASSURE measurements. Both for head, thorax and abdomen, in case of comparable impact scenarios, the RCC model tends to over-predicts PoF compared to the MBS model, while the BC model tends to under-predict PoF.

This paper is part of the Proceedings of the 2019 Bicycle and Motorcycle Dynamics Conference: A Symposium on the Dynamics and Control of Single Track Vehicles held in Padova, Italy September 9th through 11th in the year 2019.Artifact ID: 7

In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

In highly automated driving, drivers occasionally need to take over control of the car due to limitations of the automated driving system. Research has shown that visually distracted drivers need about 7 s to regain situation awareness (SA). However, it is unknown whether the presence of a hazard affects SA. In the present experiment, 32 participants watched animated video clips from a driver’s perspective while their eyes were recorded using eye-tracking equipment. The videos had lengths between 1 and 20 s and contained either no hazard or an impending crash in the form of a stationary car in the ego lane. After each video, participants had to (1) decide (no need to take over, evade left, evade right, brake only), (2) rate the danger of the situation, (3) rebuild the situation from a top-down perspective, and (4) rate the difficulty of the rebuilding task. The results showed that the hazard situations were experienced as more dangerous than the non-hazard situations, as inferred from self-reported danger and pupil diameter. However, there were no major differences in SA: hazard and non-hazard situations yielded equivalent speed and distance errors in the rebuilding task and equivalent self-reported difficulty scores. An exception occurred for the shortest time budget (1 s) videos, where participants showed impaired SA in the hazard condition, presumably because the threat inhibited participants from looking into the rear-view mirror. Correlations between measures of SA and decision-making accuracy were low to moderate. It is concluded that hazards do not substantially affect the global awareness of the traffic situation, except for short time budgets.

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos (3-second duration) with variations in visible scene components: road curve angle, road surface area, road users, symbols, infrastructure, and vegetation/trees while their eyes were measured for pupil diameter, fixation duration, and saccade amplitude. The subjective measure of effort and the objective measure of saccade amplitude evidenced the highest correlations (r = 0.34 and r = 0.42, respectively) with the scene component of road curve angle. In person-specific regression analyses combining all visual scene components as predictors, average predictive correlations ranged between 0.49 and 0.58 for subjective effort and between 0.36 and 0.49 for saccade amplitude, depending on cross-validation techniques of generalization and repetition. In conclusion, the present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures. In future DMS, such knowledge can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.

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.

Powered two-wheeler riders are frequently involved in crashes at intersections because an approaching car driver fails to give right of way. This simulator study aimed to investigate how riders perform an emergency braking maneuver in response to an oncoming car and, second, whether longitudinal motion cues provided by a motion platform influence riders' braking performance. Twelve riders approached a four-way intersection at the same time as an oncoming car. We manipulated the car's direction of travel, speed profile, and its indicator light. The results showed that the more dangerous the situation (safe, near-miss, impending-crash), the more likely riders were to initiate braking. Although riders braked in the majority of trials when the car crossed their path, they were often unsuccessful in avoiding a collision with the car. No statistically significant differences were found in riders' initiation of braking and braking style between the motion and no-motion simulator configurations.

In future traffic, automated vehicles may be equipped with external human-machine interfaces (eHMIs) that can communicate with pedestrians. Previous research suggests that, during first encounters, pedestrians regard text-based eHMIs as clearer than light-based eHMIs. However, in much of the previous research, pedestrians were asked to imagine crossing the road, and unable or not allowed to do so. We investigated the effects of eHMIs on participants&rsquo, crossing behavior. Twenty-four participants were immersed in a virtual urban environment using a head-mounted display coupled to a motion-tracking suit. We manipulated the approaching vehicles&rsquo, behavior (yielding, nonyielding) and eHMI type (None, Text, Front Brake Lights). Participants could cross the road whenever they felt safe enough to do so. The results showed that forward walking velocities, as recorded at the pelvis, were, on average, higher when an eHMI was present compared to no eHMI when the vehicle yielded. In nonyielding conditions, participants refrained from crossing as indicated by a slight forward and subsequent backward average pelvic motion. An analysis of participants&rsquo, thorax angle indicated rotation towards the approaching vehicles and subsequent rotation towards the crossing path. It is concluded that results obtained via a setup in which participants can cross the road are similar to results from survey studies, with eHMIs yielding a higher crossing intention compared to no eHMI. The motion suit allows investigating pedestrian behaviors related to bodily attention and hesitation.

This driving-simulator study aimed to motivate cooperative lane-change maneuvers in automated freeway driving under human supervision. Two interaction concepts were designed based on game theory. These concepts supported drivers’ cooperation by applying both rewards and sanctions as the proverbial carrot and stick. The social-status interaction rewards gap creation by revealing a driver's prior cooperative behavior to other road users. The trade-off interaction introduces a system in which points compensate time loss and gain. Both concepts were evaluated from the left- and right-lane perspective, framing 39 participants to “be fast.” Drivers in the right lane asked those in the left lane to open a gap to overtake, mediated through a vehicle-to-vehicle connection and an augmented-reality user interface. Only 67% of the merging requests were accepted by left-lane drivers due to time pressure in the baseline condition. The social-status interaction enhanced acceptance to 86% on average and even to 97% for requests made by drivers marked as cooperative. The trade-off interaction enhanced acceptance to 87% as drivers gained a virtual benefit for losing one second. The subjective evaluation was positive for all conditions, and the social concepts were rated significantly higher on items associated with social relationships. Both social interaction concepts motivate cooperation and shape drivers’ behavior even under time pressure. Social mechanisms power maneuver-based local cooperation between traffic participants. It is expected that involving drivers in cooperative maneuvers has a beneficial effect on traffic performance, which microscopic traffic flow modeling should validate next. Gamified interaction and interface elements involve drivers of automated vehicles into strategic decisions and could help to mitigate automation effects. Since they don't “drive” any more, cooperative interaction concepts now make them “play driving” and formulate pleasing strategies.