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Introduction: Highly automated driving is expected to reduce the accident risk occurrence by human errors, but it can also increase driver distraction. Previous evidence shows that auditory signals can help drivers take over in critical situations. However, it is still uncertain whether the potential benefit of verbal auditory signals could be generalized to driving situations where drivers are visually and auditorily distracted., Method: Our first objective was to compare the effectiveness of complementary audio messages (audio + visual condition) and visual only (visual condition) variable message signs (VMS) messages. The second objective was to explore the potential use of oral messages with traffic information to help highly-automated vehicle drivers identify critical situations. Eye-tracking data were also registered. Twenty-four volunteers participated in a driving simulator study, completing two tasks: (a) a TV series task, where they had to pay attention to an episode of a TV series while traveling along the route; and (b) a VMS task, where they had to recover the manual control of the car if the VMS message was a 'critical message.', Results: General results showed that, when the audio was available, the participants: (a) had a higher ability to discriminate the VMS messages, (b) were less conservative, (c) responded earlier, and (d) their pattern of fixations was more efficient. A complementary analysis showed that the counterbalance order was a moderating factor for the discrimination ability and the response distance measures. This evidence suggests a potential learning effect, not cancelled by counterbalancing the order of the conditions., Conclusion: The processing of traffic messages may improve when provided as oral and visual messages., Practical Applications: These results would be of special interest for engineers designing highly automated cars, considering that the design of automated systems must ensure that the driver's attention is sufficient to take over control., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers' naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

Purpose: The aim of this study was to investigate the driving performance of drivers with autism spectrum disorders under complex driving conditions., Method: Seventeen drivers with autism spectrum disorders and 18 typically developed drivers participated in a driving simulator trial. Prior to the assessment, participants completed the Driving Behaviour Questionnaire and measurements of cognitive and visual-motor ability. The driving simulation involved driving in an urban area with dense traffic and unpredictable events., Results: In comparison with the typically developed group, drivers with autism spectrum disorders reported significantly more lapses in driving, committed more mistakes on the driving simulator, and were slower to react in challenging situations, such as driving through intersections with abrupt changes in traffic lights. However, they were also less likely to tailgate other vehicles, as measured by time-to-collision between vehicles, on the driving simulator., Conclusions: The performances of licensed drivers with autism spectrum disorders appeared to be safer in respect to car-following distance but were poorer in their response to challenging traffic situations. Driver education for individuals with autism spectrum disorders should focus on quick identification of hazards, prompt execution of responses, and effective allocation of attention to reduce lapses in driving. Implications for rehabilitation Drivers with autism spectrum disorders reported significantly more lapses during driving. Drivers with autism spectrum disorders were observed to be poorer in traffic scenarios requiring critical response. Driver education for individuals with autism spectrum disorders should focus on managing anxiety and effective attention allocation while driving. Driving simulators can be used as a safe means for training critical response to challenging traffic scenarios.

Driver distraction is one of the main causes of motor-vehicle accidents. However, the impact on traffic safety of tasks that impose cognitive (non-visual) distraction remains debated. One particularly intriguing finding is that cognitive load seems to improve lane keeping performance, most often quantified as reduced standard deviation of lateral position (SDLP). The main competing hypotheses, supported by current empirical evidence, suggest that cognitive load improves lane keeping via either increased physical arousal, or higher gaze concentration toward the road center, but views are mixed regarding if, and how, these possible mediators influence lane keeping performance. Hence, a simulator study was conducted, with participants driving on a straight city road section whilst completing a cognitive task at different levels of difficulty. In line with previous studies, cognitive load led to increased physical arousal, higher gaze concentration toward the road center, and higher levels of micro-steering activity, accompanied by improved lane keeping performance. More importantly, during the high cognitive task, both physical arousal and gaze concentration changed earlier in time than micro-steering activity, which in turn changed earlier than lane keeping performance. In addition, our results did not show a significant correlation between gaze concentration and physical arousal on the level of individual task recordings. Based on these findings, various multilevel models for micro-steering activity and lane keeping performance were conducted and compared, and the results suggest that all of the mechanisms proposed by existing hypotheses could be simultaneously involved. In other words, it is suggested that cognitive load leads to: (i) an increase in arousal, causing increased micro-steering activity, which in turn improves lane keeping performance, and (ii) an increase in gaze concentration, causing lane keeping improvement through both (a) further increased micro-steering activity and (b) a tendency to steer toward the gaze target., (Copyright © 2018 Elsevier Ltd. All rights reserved.)