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.

@en

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.

@en

The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.@en

Introduction: 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. Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate. Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance. Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.@en

The role of prior austenite grain size (PAGS) on the passive layer properties of martensitic steels is studied. Electron backscatter diffraction analysis shows that PAGS between 5 and 66 µm were obtained after applying different heat treatments. The barrier properties of passive film deteriorate with grain refinement up to 28 µm, attributed to increased donor density and a decrease in the fraction of γ-Fe2O3 in passive films. However, the further refinement of PAGS to 5 µm leads to improvement in the barrier properties due to the changes within the martensite structure. This improvement stems from the increase in γ-Fe2O3 fraction. @en

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.

@en

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.

@en

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.

@en

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.

@en