Investigating driving instructors: The mediating roles of driving skills in the relationship between organizational safety strategies and driver behaviours

The aim of the present study is to investigate the mediating roles of driving skills in relationship between organizational safety strategies and driver behaviours among driving instructors. Driving skills consist of perceptual-motor skills and safety skills. Driver behaviours are investigated under four factors: violations, errors, lapses, and positive driver behaviours. Participants were 132 driving instructors (108 male and 24 female). In order to measure organizational safety strategies, Organizational Safety Strategies Scale (OSSS) was developed for driving schools. Results of the principal component analyses yielded one-factor solution for OSSS. In order to test the indirect effects of organizational safety strategies on driver behaviours through driving skills, multiple mediation analyses were conducted by entering age and annual mileage as the control variables. As organizational safety strategies were stronger, driving instructors had higher levels of perceptual-motor skills, which resulted in higher violations. On the other hand, as organizational safety strategies were stronger, driving instructors had higher levels of safety skills, which resulted in less violations and lapses. It can be inferred that; organizational stronger safety strategies might have negative influences on road safety through higher perceptual-motor skills; whereas there can be positive influences on road safety through higher safety skills. In addition, both skills are related to organizational safety strategies. Hence, driving schools should consider the asymmetric relationship between perceptual-motor skills and safety skills while improving their safety strategies to decrease violations and lapses. Organizations might also develop interventions to balance the stated skills to increase road safety.     

Driving without awareness: The effects of practice and automaticity on attention and driving

This research examined the development of proceduralised “driving without awareness” in a driving simulator by paying participants to drive a simulated road regularly over 12 weeks of testing. This longitudinal research paradigm is a significant departure from previous studies which have examined drivers in a conscious attentional mode using short experimental sessions or cross-sectional designs comparing expert and novice performance. During each session, participants took two “trips” on the simulated road; sometimes travelling on a “to and from” journey on one half of the road, sometimes traversing the entire road in one direction. A range of measures, including driving performance, vehicle detection, perceptual speed regulation, and hazard reactions were collected. The results showed the development of driving patterns and changes in object detection performance indicative of proceduralised driving. Speed and lane position variability quickly decreased with practice, as did participants’ subjective experiences of driving difficulty. Performance on an embedded detection task appeared to become a proceduralised part of the driving task, becoming highly efficient in later stages of the experiment. The changes in attentional focus and driving performance over time provide new light on previous research findings and allow us to critically re-examine several established models of driver behaviour.     

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.     

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.     

Evaluation of a dynamic blocking concept to mitigate driver distraction: Three simulator studies

In recent years, the number and complexity of in-vehicle infotainment systems has been steadily increasing. While these systems certainly improve the driving experience, they also increase the risk for driver distraction. International standards and guidelines provide methods of measuring this distraction along with test criteria that help automakers decide whether an interface task is too distracting to be used while driving. Any specific function failing this test should therefore be locked out for use by the driver. This study implemented and tested a dynamic approach to this blocking by algorithmically reacting to driver inputs and the pace of the interaction in order to prevent drivers from having prolonged or too intense sequences of in-vehicle interactions not directly related to driving. Three simulated driving experiments in Germany and the United States were conducted to evaluate this dynamic function blocking concept and also cater for differences in the status quo of either no blocking or static blocking. The experiments consisted of a car following scenario with various secondary interface tasks and always included a baseline condition where no blocking occurred as well as an implementation of the dynamic function blocking. While Experiments 1 and 3 were aimed at collecting and analyzing gaze and driving data from more than 20 participants, Experiment 2 focused on the user experience evaluation of different visual feedback implementations from 13 participants. The user experience as rated by these participants increased throughout the course of all three studies and helped further improve both the concept and feedback design. In the experiments the total glance time towards the road was significantly higher in the dynamic function blocking condition compared to the baseline, already accounting for the increase in total task time inherent to the dynamic condition. Participants developed two strategies of interacting with the dynamic function blocking. They either operated at their normal baseline speed and incurred task blockings or operated slower to avoid the blockings. In the latter strategy, participants chunked their interactions into smaller steps with the present data suggesting that they used the pauses in between chunks to look back onto the road ahead. Theoretical and practical implications of this first evaluation of a dynamic function blocking concept are discussed.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

Effect of verbal messages with reminders to communicate driving situations to alter driver behavior in conditional driving automation

In conditionally automated driving, drivers are relieved of steering (hands-off), accelerating, and braking actions as well as of continuous monitoring of driving situations and the system operation status (eyes off). This enables continuously engagement in non-driving-related activities. Managing the allocation of a driver’s attention to the surrounding environment and automation status presents a major challenge in human–machine system design. In this study, we propose a verbal message with a reminder (monitoring request) to divert the driver’s attention from non-driving-related activities to peripheral monitoring under conditionally automated driving. When the system encounters events related to weather, traffic, and road geometry, it provides a verbal message pertaining to the road surroundings (e.g., “It is foggy outside”) to the driver. After three seconds, the system provides a reminder message (i.e., “Did you confirm it?”) to the driver. We explore two questions: (1) how does the message with the reminder affect the driver’s attention allocation, and (2) how does the message with the reminder affect the driver behavior in response to a request to intervene (RTI). With a driving simulator, we designed a repeated measures mixed design with a between-participant factor of “Driving condition” and within-participant factors of “Event type” and “Measurement time”. Three driving conditions were established as follows: no messages, messages without reminders, and messages with reminders. Twenty-seven drivers participated as participants in the driving simulator experiment. Results showed that the reminder message was effective in allocating the participants’ attention to the surrounding environment, and the participants took over the driving task after spending more time understanding the take-over situation in the condition of messages with reminders compared to those in the condition of no messages. We conclude that the proposed reminder message can direct drivers’ attention to the road surroundings during conditionally automated driving. In the future, we plan to design adaptive verbal monitoring requests to adjust the reminder message according to the situation.     

The influence of music genres on the driving behaviour of young drivers and their visual scanning of the environment

Recently, distractions have been recognized as a significant risk factor in road traffic. This simulator study aims to investigate how different music genres affect the driving behaviour of young drivers and their visual scanning of the environment in urban settings. The genres considered were Croatian pop, foreign pop, classical music, metal, and Balkan folk music, while on one road section there was no music. The research sample consisted of 61 participants (44 males and 17 females) with a mean age of 24.58 years and a mean driving experience of 5.25 years. The influence of music on the drivers’ behaviour was analysed on the basis of the data collected from the driving simulator, eye tracking glasses, and structured observation during driving sessions. It was found that the highest average speed (around 60 km/h) was recorded while the participants were listening to Balkan folk and metal music, while other music genres, as well as the “no music” condition, influenced driving speed in a similar way to one another and the participants drove at 50 km/h on average. Furthermore, the results suggest that the music genre also affects how drivers visually scan the environment (the number of gazes classified as fixations and number of road signs looked at). The findings obtained may be used in road safety work and practical recommendations and further research are discussed.     

An evaluation of the effectiveness of countermeasures for improving the safety of dilemma zones: A driving simulator study

The main objective of this driving simulator study is to analyze the behavior of the driver at the start of the yellow signal of a signalized rural intersection and identify the most effective countermeasures for tackling the dilemma zone, namely an area on the intersection approach where vehicles at the start of the yellow phase can neither safely stop before the stop line nor cross the intersection. The following countermeasures were tested in the study on a sample of 48 drivers: green signal countdown timers, GSCT (C₁); a new pattern of vertical and horizontal warning signs (C₂); and an advanced on-board driver assistance system based on augmented reality (AR) and connected vehicle technologies (C₃). These countermeasures were tested and compared to a baseline condition (B) where no countermeasures were applied. Based on the results of this study, the C₂ and C₃ countermeasures have proven to be valid tools for reducing driver indecision when approaching signalized intersections at the start of the yellow signal. In fact, using C₂ and C₃, the length of the dilemma zone was equal to 30 m and 36 m, respectively, with a reduction of about 50%, as compared to the baseline condition (B). Moreover, a reduced number of false behaviors was recorded, as well as a greater consistency in driver decision-making behaviors. Conversely, the C₁ countermeasure did not lead to a significant improvement in the dilemma zone: an unnecessary increase in early stop rates was recorded, resulting in reduced intersection efficiency and operations.     

Evaluating the impact of adaptive signal control technology on driver stress and behavior using real-world experimental data

While the operational and crash reduction benefits of adaptive signal control technology (ASCT) have long been investigated, the impact of this technology on driver behavior and stress is still uncertain. This study evaluated the impact of ASCT on driver behavior and stress in a real-world environment. Participants travelled through two arterial corridors, one equipped with ASCT and the other having traditional time-of-day coordinated signals. Driver stress was measured using a heart rate detector and a perceived stress scale while driver behavior was examined using vehicular trajectory data. Overall, driving behavior improved on the ASCT as compared to the non-ASCT corridor, as indicated by higher speeds and a fewer number of stops on the ASCT corridor relative to the non-ASCT corridor. Repeated measures ANOVA showed a statistically significant reduction in driver heart rate by −10 beats per minute over the ASCT corridor. A similar trend was observed for drivers’ perceived stress, analyzed by Wilcoxon sign ranked test. Driving behavior also showed significant improvement with ASCT presence, and speed was found to be negatively correlated with stress. Furthermore, the participants’ speed was controlled by the two systems i.e. ASCT and non-ASCT as expected. This study provides a significant proof of concept that ASCT can create positive improvements in driver stress and behavior that can be further investigated in the future.     

Novel use of a virtual driving assessment to classify driver skill at the time of licensure

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary “Driving Classes”; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.     

Attention during visual secondary tasks in driving: Adaptation to the demands of the driving task

Visual attention in driving with visual secondary task is compared for two visual secondary tasks. N = 40 subjects completed a 1 h test drive in a motion-base driving simulator. During the drive, participants either solved an externally paced, highly demanding visual task or a self paced menu system task. The secondary tasks were offered in defined critical and non-critical driving situations. Eye movement behavior was analyzed and compared for both tasks. Before starting the secondary tasks, eye movement behavior shows a smaller standard deviation of gaze as well as longer fixation durations for both tasks. The comparison between the two tasks indicates that drivers use the possibilities the self paced task offers: during the secondary task, they monitor the driving scene with longer fixations and show a greater standard deviation of gaze position. Furthermore, independently of the type of secondary task, drivers adapt their eye movement behavior to the demands of the situation. In critical driving situations they direct a larger proportion of glance time to the driving task. Last, the relation between glance behavior and collisions is analyzed. Results indicate that collisions go together with an inadequate distribution of attention during distraction. The results are interpreted regarding the attentional processes involved in driving with visual secondary tasks. Based on the similarities and differences between the two secondary tasks, a cognitive approach is developed which assumes that the control of attention during distraction is based on a mental situational model of the driving situation.