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.     

Comparative assessment of the behaviour of drivers with Mild Cognitive Impairment or Alzheimer’s disease in different road and traffic conditions

The objective of this research was the analysis of the driving performance of drivers with Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD), in different road and traffic conditions, on the basis of a driving simulator experiment. In this experiment, healthy “control” drivers, patients with MCI, and patients with AD, drove at several scenarios at the simulator, after a thorough neurological and neuropsychological assessment. The scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of healthy and impaired drivers was analysed and compared by means of Repeated Measures General Linear Modelling techniques. A sample of 75 participants was analysed, out of which 23 were MCI patients and 14 were AD patients. Various driving performance measures were examined, including longitudinal and lateral control measures. The results suggest that the two examined cerebral diseases do affect driving performance, and there were common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, cognitively impaired drivers drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. The group of patients had difficulties in all road and traffic environments, and especially when traffic volume was high. Most importantly, both cerebral diseases appear to significantly impair reaction times at incidents. The results of this research suggest that compensatory behaviours developed by impaired drivers are not adequate to counterbalance the direct effects of these cerebral diseases on driving skills. They also demonstrate that driving impairments increase as cognitive impairments become more severe (from MCI to AD).     

Drivers’ driving style and their take-over-control judgment

Advanced driver assistance systems (ADASs), which help a driver drive a car safely and easily (e.g., warning alerts, steering control, and brake/acceleration pedal operation), have increased in popularity. However, such systems have not yet been perfected. Sometimes, humans must take over control from the systems; otherwise, they can cause an accident. In this study, we focused on one of the ADASs, adaptive cruise control (ACC), which automatically maintains a selected distance from the preceding car, and investigated individual differences in take-over-control judgment and related factors. The candidate factors included driver’s manual driving style, driving performance without the ACC, and the usability evaluation of ACC. Ten participants repeated the short, strictly controlled trials in a driving simulator (DS), with a varying value of only one parameter (deceleration of the preceding car) affecting the need for intervention. First, we confirmed that the participants made the judgment based on the dangerousness of the situation and that there were individual differences in the take-over-control judgments. Some participants intervened in the ACC control in less dangerous trials, whereas other participants did not, even if their own car got very close to the preceding car. We conducted a correlation analysis and confirmed the results with the estimation of the confidence interval using a bootstrap method. As a result, we found that driving style and driving performance without ACC had a stronger relationship to the number of interventions, rather than the usability evaluation. In particular, methodical drivers, who obeyed traffic rules and manners, began to intervene in less dangerous situations. The tendency to avoid utilizing brake operations was also related to take-over-control judgment. This might be because the participants intervened by pressing the brake pedal. Our study showed that drivers’ driving style could affect the usage of ACC independently from the performance of the ACC.     

Driving examiners’ views on data-driven assessment of test candidates: An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about data-driven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behavior, and color-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were skeptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test.     

Influence of pedestrian-to-vehicle technology on drivers’ response and safety benefits considering pre-crash conditions

Pedestrian-to-vehicle (P2V) technology may offer a promising approach to reducing pedestrian crashes. However, its influences on both driver response and safety benefits have been little studied in previous research, particularly in regard to the variation of influences between different pre-crash scenarios. To investigate these influences, this study designed three pre-crash scenarios based on pedestrian crash contributing factors identified from crash reports, and collected 44 drivers’ driving simulator experiments’ data. The results clarified how using P2V technology to warn drivers of an impending collision improves safety by causing a series of changes for both brake operation and braking profile. These series of changes were further demonstrated to vary between scenarios. The study showed that P2V technology may be particularly useful in scenarios in which a pedestrian’s crossing intention is unclear; specifically, in this type of scenario, the P2V warning had changed the braking process from a panic brake of “slow reaction-hard brake” to a comfortable brake of “quick reaction-gentle brake.” In addition, the P2V warning may be less effective in “low-risk” level scenarios where a driver is confident that he/she can handle the situation through a more conservative evasive action and don’t need to react strongly to a warning. Moreover, depending on the pre-crash scenario, the P2V warning may be mostly beneficial for drivers who had a crash/citation in the past five years and working-aged drivers.     

Roadside digital billboard advertisements: Effects of static,transitioning, and animated designs on drivers’ performance and attention

The aim of this study was to analyze and compare the effects of different types of digital billboard advertisements (DBAs) on drivers’ performance and attention allocation. Driver distraction is a major threat to driver safety. DBAs are one form of distraction in drivers’ outside environment. There are many different types of DBAs, such as static images, changing images, or videos. However, it is not clear to what extent each of these contributes to driver distraction. A total of 100 students participated in a controlled driving simulator experiment in an urban environment. Measures of driving performance were collected, as well as eye tracking and EEG as windows into attention allocation. The different types of DBAs investigated were static (a single image), transitioning (one static DBA replaces another), and animated (short videos). The statistical analysis demonstrated that there were significant differences in the effect of each type of DBA on drivers' performance (deviation from the center of the lane and reaction time), visual attention to the road (percent of fixations on the road, percent of fixations on DBAs, fixation duration on DBAs, and number of gazes on DBAs), and the EEG theta band and beta band. These results show that driving performance and attention to the road were both more negatively affected when drivers were exposed to transitioning and animated DBAs as compared to static DBAs. The results of this study provide guidance for the better design and regulation of DBAs in order to minimize driver distraction.     

Attentional processes and meditation

Visual attentional processing was examined in adult meditators and non-meditators on behavioral measures of change blindness, concentration, perspective-shifting, selective attention, and sustained inattentional blindness. Results showed that meditators (1) noticed more changes in flickering scenes and noticed them more quickly, (2) counted more accurately in a challenging concentration task, (3) identified a greater number of alternative perspectives in multiple perspectives images, and (4) showed less interference from invalid cues in a visual selective attention task, but (5) did not differ on a measure of sustained inattentional blindness. Together, results show that regular meditation is associated with more accurate, efficient, and flexible visual attentional processing across diverse tasks that have high face validity outside of the laboratory. Furthermore, effects were assessed in a context separate from actual meditation practice, suggesting that meditators’ better visual attention is not just immediate, but extends to contexts separate from meditation practice.