A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Prediction of effort and eye movement measures from driving scene components

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.     

Evaluating the effectiveness of a young driver-education intervention: Learn2Live

Road traffic collisions are the leading cause of death among young adults, and behaviour change interventions play a key role in battling this public health concern. Road safety interventions are often educational and have traditionally relied on fear appeals to alter risky driving behaviour - yet there is a paucity of data regarding their effectiveness. Peer-education has been championed as an additional route to promoting safe driving behaviour. To examine these issues, this study evaluated the effectiveness of a fear appeal intervention in improving young drivers’ attitudes towards risky driving behaviour. A total sample of 800 high school and college students (16–20 years old) completed a similar set of questionnaires pre- and post-intervention and at a 3-month follow-up. Two different types of follow-up interventions were also compared: a peer-led and an expert-led road safety educational event. Measures evaluating attitudes towards risky driving behaviour were completed at all 3 time frames, and questions regarding the participants' perception of the follow-up event were asked. Overall, our data showed an improvement in attitudes towards risky driving behaviours both immediately after the intervention and three months later. These changes were especially evident among females. With regard to the follow-ups, both were effective in improving the attitudes towards risky driving. However, the peer-led event was preferred by the participants compared to the expert-led follow-up.     

Using a multi-user driving simulator system to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits

Vehicle fleet rear-end collisions (FRECs) are an extremely fatal type of traffic collisions on freeway and they usually occur in foggy weather. This study aimed to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits on freeway. A multi-user driving simulator system was used to conduct the experiment and the driving behavior data were collected from eight participants. The experimental results showed that as the fog density increased, the length of vehicle fleet decreased significantly, and drivers tended to keep a more stable car-following distance. The fog weather and short vehicle gap prompted drivers to react faster and brake harder in respond to the leading vehicle’s brake. In spite of the compensational behaviors, more FRECs were observed under heavy fog condition. Lowering speed limit can significantly reduce the FRECs under foggy conditions. As the speed limits reduced, drivers’ brake response time and speed variance significantly reduced. The study also found that drivers’ brake response time was negatively correlated with their positions in the fleet. Drivers in the front positions of the fleet had a longer response time than drivers in the back positions and thus were more likely to encounter collisions. The study generated a better understanding of drivers’ behavioral pattern in a vehicle fleet and the patterns of vehicle fleet rear-end collisions occurrence. The findings also shed lights on the design of driver assistance system for complex driving situations such as freeway driving under adverse weather.     

A one-boundary drift-diffusion model for time to collision estimation in a simple driving task

ABSTRACT

Driving is a complex everyday task. Every year a huge number of driving accidents around the world causes serious physical and mental injuries and deaths. The correct estimation of the remaining time to reach the other vehicles on the road, known as time to collision (TTC), is an important factor to avoid accidents. In this study, we aimed to use a drift-diffusion model (DDM) to better understand the participants’ estimation of TTC in two driving experiments. Both experiments were the same, except that in one of them participants were asked to finish the experiment as fast as they could, while in the other experiment there was no time constraint. DDM fitted the data from all participants well in both experiments according to the chi-square goodness of fit criterion. Also, results showed that time pressure increases subjects’ estimated TTC, the rate of accumulation of sensory information and the response threshold.     

Relations Among Personality Traits, Mood States, and Driving Behaviors

One hundred sixty-three individuals participated in this study, which assessed relations among personality traits, mood states, and driving behaviors. Each participant underwent a standard driving evaluation on the road and completed the NEO PI-R Personality Inventory and the Profile of Mood States (POMS). Results indicated that the mood states of depression-dejection, anger-hostility, fatigue-inertia, vigor activity, and tension-anxiety were related to Cautiousness while driving for young adults, while personality traits were not found to be related to driving. Implications of these findings are discussed.     

Look: No hands! Driving on the motorway

How drivers hold their steering wheels when they are driving has been studied in restricted ways for many years. Here I describe the advantages (and disadvantages) of a more naturalistic way of studying this phenomenon, and present the results from two studies that observed the hand positions of motorway drivers from an elevated vehicle. In the first study I tape-recorded the data as we passed traffic on the left, and in the second I used paper and pencil to record the data as traffic passed us on the right. In both studies few drivers kept to the recommended 10-2 (or more generous 9-3) position for holding the steering wheel, and approximately one-third of them were driving with one hand on the wheel. Very few of the drivers observed (some 2%) were using mobile phones, but two were driving with no hands on the wheel. The paper concludes with some discussion of the value of this methodology for obtaining realistic data.     

Cognitive biases in aggressive drivers: Does illusion of control drive us off the road?

Anger has been shown to be a motivating factor in aggression and it is widely accepted that driving anger may lead to aggressive driving. However, the link between anger and aggressive driving is likely to be mediated by drivers’ pre-existing cognitive biases and the subsequent situational evaluations made. This study investigated the extent to which optimism bias, illusion of control beliefs and driver anger predict self-reported hostile driving behaviours. A total of 220 licensed drivers (106 men; 114 women) completed a self-report questionnaire measuring trait driving anger, optimism bias, illusion of control and driving behaviour. Structural Equation Modelling showed that trait driving anger and illusion of control beliefs account for 37% of the variance in hostile driving behaviour scores. Optimism biases were unrelated to hostile driving behaviours. Thus, driving anger propensities and feelings of control over the situation, but not a general tendency to underestimate the likelihood of adverse outcomes, predict aggressive driving.     

Investigating the individual and organisational predictors of work-related driving crash involvement in Ethiopia

The rate of road traffic injury and death in Ethiopia is at a critical level when compared to rates in high-income countries. Considering the enormity of this issue, research is to identify groups of high-risk road users and the factors contributing to their crash involvement. This study focuses on work-related drivers. This study explores driving behaviour as a mediator of the relationship between organisational and individual attribute factors and self-reported crashes in a sample of 213 work-related drivers in Addis Ababa, Ethiopia. The hypothesised framework identifies driving behaviour as the most proximal determinant of self-reported crashes, and safety values, role overload and self-efficacy as antecedents of driving behaviour. With the exception of the relationship between self-efficacy and driving behaviour, all the hypothesised relationships were supported. We make recommendations for intervention approaches that are theoretically focused and sensitive to the cultural context.