Handing control back to drivers: Exploring the effects of handover procedure during transitions from Highly Automated Driving

The operational capabilities of automated driving features are limited and sometimes require drivers’ intervention through a transition of control. Assistance at an operational level might be extremely beneficial during transitions but the literature lacks evidence on the topic. A simulator study was conducted to investigate the potential impacts that lateral assistance systems might have while the Automated Driving System (ADS) hands back control to the driver. Results showed that drivers benefitted from a strong Lane Keeping Assist during the first phase of the transfer, helping them to keep the lane centre. However, assisting the drivers at an operational level did not enhance their capability of addressing a more complex task, presented as a lane change. In fact, it was more task-specific assistance (Blind-spot assist) that allowed drivers to better cope with the tactical decision that the lane change required. Moreover, longer exposure to lane-keeping assist systems helped them in gaining awareness of the surrounding traffic and improved the way drivers interacted with the Blind-spot assist.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.     

Effect of multimodal takeover request issued through A-pillar LED light,earcon, speech message,and haptic seat in conditionally automated driving

The driver of a conditionally automated vehicle equivalent to level 3 of the SAE is obligated to accept a takeover request (TOR) issued by the vehicle. Considerable research has been conducted on the TOR, especially in terms of the effectiveness of multimodal methods. Therefore, in this study, the effectiveness of various multimodalities was compared and analyzed. Thirty-six volunteers were recruited to compare the effects of the multimodalities, and vehicle and physiological data were obtained using a driving simulator. Eight combinations of TOR warnings, including those implemented through LED lights on the A-pillar, earcon, speech message, or vibrations in the back support and seat pan, were analyzed to clarify the corresponding effects. When the LED lights were implemented on the A-pillar, the driver reaction was faster (p = 0.022) and steering deviation was larger (p = 0.024) than those in the case in which no LED lights were implemented. The speech message resulted in a larger steering deviation than that in the case of the earcon (p = 0.044). When vibrations were provided through the haptic seat, the reaction time (p < 0.001) was faster, and the steering deviation (p = 0.001) was larger in the presence of vibrations in the haptic seat than no vibration. An interaction effect was noted between the visual and auditory modalities; notably, the earcon resulted in a small steering deviation and skin conductance response amplitude (SCR amplitude) when implemented with LED lights on the A-pillar, whereas the speech message led to a small steering deviation and SCR amplitude without the LED lights. In the design of a multimodal warning to be used to issue a TOR, the effects of each individual modality and corresponding interaction effects must be considered. These effects must be evaluated through application to various takeover situations.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment

The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.     

Personality and demographic differences in the perceived risks of potentially timid driving behaviours

Timid driving behaviours can be described as overly cautious and hesitant driving behaviours. Little research has examined behaviours that potentially resemble timid driving and how these behaviours are perceived by other drivers. This is despite the potential for these behaviours to be perceived in a way that leads to angry and aggressive retaliatory behaviours in some drivers (e.g., in anger-prone drivers). We conducted an online survey examining the perceived road safety risks of several behaviours that could potentially result from timid driving and their relationships with driver personality (trait anxiety, trait driving anger), behaviour (anxious driving, angry driving), and demographic (age, gender, annual mileage) background. Drivers (N = 439, M_age = 49.41 ± 5.59 years, aged 18–89) perceived excessively cautious and unpredictable braking behaviours as posing moderate levels of risk. Multiple linear regression analyses also indicated higher perceived risks of slow and excessively cautious behaviours in older, male, and anger prone drivers. No meaningful associations were found between driver characteristics and the risks of unpredictable braking behaviours. These results suggest that safety campaigns to reduce aggressive behaviour may benefit from targeting the perceptions of other drivers’ behaviours.     

Driving self-regulation and risky driving outcomes

Self-regulation has been associated with risky driving outcomes in the past but there are no available measures to assess driving-specific self-regulatory capacity. The present study assessed the association of a newly developed driving self-regulation measure with driving violations, errors, and lapses. Overall, 330 UK drivers completed measures of risky driving outcomes, driving anger, trait impulsivity, sensation seeking, normlessness, domain-general trait self-regulation plus a new unidimensional measure of Driving Self-Regulation Questionnaire (DSRQ-16). Bivariate correlation analysis indicated that the DSRQ-16 showed expected associations with both driving-related outcomes and factors, as well as with impulsivity traits and general self-regulation. Bootstrapped hierarchical linear regression models showed that the DSRQ-16 was significantly associated with driving violations, errors, and lapses after controlling for the effects of other relevant predictors. This is the first study to demonstrate the association of driving-specific self-regulation with risky driving behaviour, driving anger, impulsivity and related personality traits. Driving-specific self-regulation may present a novel target for road safety interventions, as well as a theoretically relevant component of models of risky driving behaviour.     

Cognitive load while driving impairs memory of moving but not stationary elements within the environment

The negative impact of cognitive load, such as cell phone conversations, while driving is well established, but understanding the nature of this performance deficit is still being developed. To test the impact of load on awareness of different elements in a driving scene, memory for items within the environment was examined under load and no load conditions. Participants drove through two different scenarios in a driving simulator, were periodically interrupted by a pause in the driving during, and were asked questions regarding moving and stationary objects in the environment. Participants in the load condition drove while concurrently counting backwards by sevens. Results indicate that driving under load conditions led to diminished knowledge of moving, but not stationary, objects in the scene. This result suggests not all types of knowledge are equally impaired. Potential implications for current theories of cell phone use while driving and applied attention theory are discussed.     

Virtual Reality and Cognitive-Behavioral Therapy for Driving Anxiety and Aggression in Veterans: A Pilot Study

Within the U.S. military, motor vehicle accidents (MVAs) are the leading cause of preventable morbidity and mortality. Prior combat exposure and anxiety symptoms are associated with risky and aggressive driving, which is responsible for over half of MVA fatalities. Therefore, interventions are needed to reduce driving anxiety and aggression in veterans in order to mitigate the public health impact of MVAs. Virtual reality exposure therapy (VRET) offers safe, controlled exposure to distressing stimuli. The current study piloted a novel virtual reality and cognitive behavioral intervention (VRET + CBT) for veterans that integrated both anxiety and anger management components. Virtual reality driving scenarios were delivered in a driving simulator and tailored for the military population. Six previously deployed veterans completed eight intervention sessions, as well as pre/post, one month follow-up and six to nine month follow-up assessments. Repeated measures ANOVAs demonstrated significant decline and large effect sizes for PTSD symptoms, driving phobia, hyperarousal in driving situations, anxiety/anger-related thoughts and behaviors, and risky driving. Hyperarousal in driving situations declined by 69%, aggressive driving declined by 29%, and risky driving declined by 21%. Treatment gains were maintained at follow-up. Recruitment, retention, immersion, simulator sickness scores, and qualitative feedback demonstrated feasibility of the intervention. Implications for future research and adaptation are discussed.     

Driving speed choice: The role of conscious monitoring and control (reinvestment) when driving

This study aimed to examine the role of reinvestment - the propensity to consciously monitor and control actions (movement specific reinvestment) and to consciously monitor and evaluate decision making processes (Decision specific reinvestment) while driving in everyday risky scenarios. The study also aimed to evaluate the association between reinvestment and previously validated driver attitude measures. Fifty one participants completed a series of questionnaires (Driving Self-Efficacy Scale, Driver Attitude Questionnaire, Movement Specific Reinvestment Scale, Decision Specific Reinvestment Scale) after which they completed a test phase in a driving simulator. In the test phase, driving scenarios included roads with different markings (i.e., double yellow, wide centrelines, wire rope barriers, Audio Tactile Profiled markings) and alerting scenarios (i.e., police car present, high crash risk area sign, reduced speed zone). Results revealed that on risky roads (wide centrelines), participants with a high propensity for decision specific reinvestment drove slower than those with a low propensity. Driver experience, attitudes towards speeding and scores on the Decision Reinvestment subscale of the Decision Specific Reinvestment Scale significantly predicted speed choice. More experienced participants with higher scores on the Decision Reinvestment subscale were more likely to drive slower and participants with worse attitudes towards speeding were likely to drive faster. Participants with a low propensity for movement specific reinvestment (specifically, Movement Self-Consciousness) reduced their speed to a greater extent than those with a high propensity when driving in the police car scenario. There was some evidence to suggest that high decision specific and movement specific reinvesters were more likely to be involved in crashes and receive driving infringements. The current study is the first to demonstrate a significant relationship between reinvestment and driving. The implications of these findings for road safety are discussed.