On-road trust and perceived risk in Level 2 automation

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.     

Behavioural adaptation or constraint? The effect of curve proximity on driving behaviour

Horizontal curves are typically associated with increased crash risk when compared with straight roads, but recent analyses have suggested that having more frequent sharp curves decreases the relative crash risk posed by each curve. Here, 90 drivers completed a simulated rural drive with either high proximity (160 m straight tangent between curves) or low proximity (1200 m tangent) curves. Curve proximity had a significant effect on approach speeds, with drivers in the high proximity curve drive showing significantly lower mean and maximum approach speeds before entering the curve. However, they also showed an unexpected tendency to higher speeds while negotiating the curve itself. The current study provides direct empirical evidence that driving behaviour on approach to a given curve is significantly affected by the proximity of other curves, and therefore highlights the need to factor in the characteristics of the road on approach to the curve, as well as the features of the curve itself when assessing risk.     

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.     

Driver social desirability scale: A Turkish adaptation and examination in the driving context

Self-report data collection methods are widely used techniques to gather information in studies related to road safety. One of the most considerable limitations of self-reports is social desirability bias. One way to overcome the possible detrimental effects of socially desirable responding is to control it by using social desirability scales. With respect to that, the present study aims to adapt the Driver Social Desirability Scale into Turkish, examine its construct validity, and investigate the relationship between social desirability and driving-related measures. A total of 351 drivers between the ages of 19 and 59 completed a questionnaire including a demographic information form, the Driver Behavior Questionnaire (DBQ), the Driver Skill Inventory (DSI), the Two-Dimensional Social Desirability Scale (SDS), and the Driver Social Desirability Scale (DSDS). Factor analysis supported the two-factor structure of the DSDS in the Turkish sample. Social desirability correlated positively with age and driving experience. Female drivers reported higher levels of driver impression management, while male drivers scored higher on self-deception. Driver impression management was associated negatively with violations and perceptual-motor skills and positively with safety skills. Lastly, driver self-deception was positively related to violations, positive driver behaviors, perceptual-motor skills, and safety skills. The study shows that the Turkish version of the DSDS is a reliable and structurally valid instrument with incremental validity compared to the general social desirability measure in predicting driving-related outcomes.     

What happens when drivers of automated vehicles take over control in critical lane change situations?

According to legislation, take-overs initiated by the driver must always be possible during automated driving. For example, when drivers mistrust the automation to handle a critical and hazardous lane change, they might intervene and take over control while the automation is performing the maneuver. In these situations, drivers may have little time to avoid an accident and can be exposed to high lateral forces. Due to lacking research, it is yet unknown if they recognize the criticality of the situation and how they behave and perform to manage it. In a driving simulator study, participants (N = 60) accomplished eight double lane changes to evade obstacles in their lane. Time-to-collision and traction usage were varied to establish different degrees of objective criticality. To manipulate these parameters as required, participants were triggered to take over control by an acoustic cue. This setting shows what might happen if drivers disable the automation and complete the maneuver themselves. The results of the experiment demonstrate that drivers rated objectively more critical driving situations as more critical and responded to the hazard very fast over all experimental conditions. However, their behavior was more extreme with respect to decelerating and steering than necessary. This impaired driving performance and increased the risk of lane departures and collisions. The results of the experiment can be used to develop an assistance system that supports driver-initiated take-overs.     

Assessing the potential impacts of connected vehicle systems on Driver’s situation awareness and driving performance

Vehicles equipped with connected vehicle technologies are able to communicate with each other and with infrastructures. Compared to Advanced Driving Assistance Systems (ADAS) using camera systems and sensor technologies, the Connected Vehicle Systems (CVS) leverage the wireless communication networks to detect hazards with a greater range, alert drivers of hazards much earlier, and therefore enhance driving safety. However, drivers’ reliance on the CVS to detect critical situations could negatively affect them maintaining situation awareness (SA) in noncritical situations when no warning is issued by the CVS. The present study conducted a driving simulator experiment with 40 participants to investigate the effect of connected vehicle systems on driver SA in normal, noncritical driving scenarios after they were exposed to the CVS with different designs of collision warning lead time (3 s, 6 s, and no warnings). After drivers experienced the CVS-supported warnings with the assigned design of lead time in critical situations, driver SA was measured in normal driving conditions using the freeze probe technique. Results revealed that drivers who experienced the CVS with early warnings (6 s) showed lower SA for normal driving events compared to those who experienced the CVS with late warnings (3 s) or no warnings. Although early warnings of CVS brought more safety benefits to drivers in critical situations, the degraded driver SA due to drivers’ reliance on such warning systems could endanger drivers when a system failure occurred. These findings highlight the importance of balancing the effects of warning lead time on driver SA and driving performance in designing connected vehicle systems.     

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.     

Towards autonomous artificial agents with an active self: Modeling sense of control in situated action

In this paper we present a computational modeling account of an active self in artificial agents. In particular we focus on how an agent can be equipped with a sense of control and how it arises in autonomous situated action and, in turn, influences action control. We argue that this requires laying out an embodied cognitive model that combines bottom-up processes (sensorimotor learning and fine-grained adaptation of control) with top-down processes (cognitive processes for strategy selection and decision-making). We present such a conceptual computational architecture based on principles of predictive processing and free energy minimization. Using this general model, we describe how a sense of control can form across the levels of a control hierarchy and how this can support action control in an unpredictable environment. We present an implementation of this model as well as first evaluations in a simulated task scenario, in which an autonomous agent has to cope with un-/predictable situations and experiences corresponding sense of control. We explore different model parameter settings that lead to different ways of combining low-level and high-level action control. The results show the importance of appropriately weighting information in situations where the need for low/high-level action control varies and they demonstrate how the sense of control can facilitate this.     

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.