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Distracted driving continues to be a major contributor to road accidents, highlighting the growing research interest in advanced driver monitoring systems for enhanced safety. This paper seeks to improve the overall performance and effectiveness of such systems by highlighting the importance of recognizing the driver’s activity. This paper introduces a novel methodology for assessing driver attention by using multi-perspective information using videos that capture the full driver body, hands, and face and focusing on three driver tasks: distracted actions, gaze direction, and hands-on-wheel monitoring. The experimental evaluation was conducted in two phases: first, assessing driver distracted activities, gaze direction, and hands-on-wheel using a CNN-based model and videos from three cameras that were placed inside the vehicle, and second, evaluating the multi-task fusion algorithm, considering the aggregated danger score, which was introduced in this paper, as a representation of the driver’s attentiveness based on the multi-task data fusion algorithm. The proposed methodology was built and evaluated using a DMD dataset; additionally, model robustness was tested on the AUC_V2 and SAMDD driver distraction datasets. The proposed algorithm effectively combines multi-task information from different perspectives and evaluates the attention level of the driver.

Under the development of intelligent network technology, drivers can obtain the surrounding traffic situation in real time, which is conducive to improving the stability of traffic flow. Therefore, this paper proposes a new curve-car-following model considering multi-vehicle information of adjacent lanes in connected environment, and conducts linear and nonlinear stability analyses of the model to demonstrate the effectiveness of the proposed model and its ability to improve the stability of traffic system; in addition, numerical simulation experiments of traffic flow convoys are designed to analyze the effects of different parameters in the proposed model on the stability of the traffic flow and test the proposed model’s ability to maintain the following behavior in a convoy. Furthermore, numerical simulation experiments are designed to analyze the effects of different parameters in the proposed model on the stability of traffic flow, and to test the ability of the proposed model to maintain the following behavior in the convoy. The model can provide theoretical guidance to alleviate traffic congestion and improve safety, and extend the application of the following model in curved multi-lane road scenarios.

The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.

Drivers are met with numerous elements requiring their attention while driving. The present research focuses on selected visual and cognitive distractions that the driver is faced with, and on their influence on detecting and perceiving changes in the traffic environment. Driver self evaluation data was used to define which elements attract most visual and cognitive distraction. A constructed conceptual model was subjected to analysis using Exploratory factor analysis (EFA), Confirmatory factor analysis (CFA), and Structural Equation Modelling (SEM). Main findings show that thinking about personal problems, chores and errands as well as roadside advertisements on the cognitive side, and looking at advertisements and the natural environment on the visual side, present the most negative impacts on drivers’ perception of crucial changes in the traffic environment. On the other hand, drivers that visually focus on traffic signals and pedestrians and think about driving speed, driving rules, and other traffic participants, tend to notice crucial changes in the traffic environment more often.

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

As vehicles become more autonomous, there is a greater need for reliable and accurate information about neighbouring vehicles. In the V2V environment, information about nearby vehicles plays a significant role in predicting traffic flow behavior and this information becomes more effective during passing. To better understand how a driver’s attention can impact the average velocity of their vehicle and those around them, an improved car-following model has been developed with a passing effect. The stability condition of the model is obtained via linear stability analysis, moreover, nonlinear analysis is used to determine the mKdV equation, which describes the evolution properties of the traffic density wave in the jammed region. The numerical and analytical results are discussed for smaller as well as larger rates of passing. It is found that for a smaller rate of passing, the phase transition occurs between the kink jam and the free flow. In the kink jam region, the initial perturbations are evolved in the form of a kink-antikink wave which moves in a backward direction and the amplitude of the headway profile decreases with an increase in the value of the driver’s attention coefficient. While for the higher rate of passing, the phase change is between the uniform to the kink jam zone through the chaotic jam zone. In the chaotic region, the behavior of the headway waves is chaotic which band with one another, break up and propagate in the backward direction. Moreover, it has been observed that with the driver’s increased attention on the average speed of any nearby vehicles, the unstable region is reduced for any rate of passing. Also, numerical findings are in accordance with theoretical results and noticed that this model is successful in increasing the efficiency of vehicle movement, reducing congestion, and improving safety on roads. To reduce collision accidents, the improved model can be implemented as active safety technology.

Mental fatigue is one of the main reasons for the decline of response inhibition. This study aimed to explore the impairing influence of mental fatigue on a driver's response inhibition. The effects of mental fatigue on response inhibition were assessed by comparing brain activity and behavioral indices when performing a Go/NoGo task before and after a 90-min fatigue manipulation task. Participants in the driving group performed a simulated driving task, while individuals in the control group spent the same time watching movies. We found that participants in the driving group reported higher levels of mental fatigue and had a higher percentage of eye closure and larger lateral deviations from their lane positions, which indicated there was effective manipulation of mental fatigue through a prolonged simulated driving task. After manipulation of mental fatigue, we observed increased reaction time and miss rates, delayed NoGo-N2 latency and Go-P3 latency, and decreased NoGo-P3 amplitude, which indicated that mental fatigue may slow down the speed of the inhibition process, delay the evaluation of visual stimuli and reduce the availability of attentional resources. These findings revealed the underlying neurological mechanisms of how mental fatigue impaired response inhibition.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

BackgroundA proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers’ capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored.MethodsThis driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers’ gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task.ResultsThe OOTL manipulations influenced drivers’ first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern.ConclusionsAutomated driving systems should be able to direct drivers’ attention to hazards no less than 6 seconds in advance of an adverse outcome.

Background: Older drivers are at a higher risk of being involved in a motor vehicle accident. However, on-road assessments of all older drivers are impractical, highlighting the need to screen for potentially unsafe drivers. This study undertook a meta-analysis of research examining the cognitive predictors of driving ability in older drivers in order to provide an evidence-based method for screening drivers. Methods: Comprehensive searches were undertaken of the PubMed, PsycINFO, CINAHL, and Health-Source Nursing electronic databases between 1980 and 2007 in order to identify studies that examined cognitive differences between drivers aged over 55 years who either passed or failed a driving assessment. Twenty-one studies were eligible for inclusion. Weighted Cohen's d effect sizes, percentage overlap statistics, Fail-safe Ns and 95% CIs were calculated for all cognitive tests. Results: The best predictors of on-road driving were the Ergovision and Useful Field of View (UFOV) tests, a complex RT task, Paper Folding task, Dot Counting, WMS Visual Reproduction, and Computerized Visual Attention Task. Simulator driving performance was best predicted by the Benton Line Orientation Task, Clock Drawing, a Driver Scanning task, the UFOV, WAIS Picture Arrangement and MMSE. Finally, the Trail Making Test, Stroop, UFOV, WAIS Block Design, and Automated Psychophysical Test were good predictors of driving problems. Conclusions: There are a variety of tests that appear suitable for screening older drivers, the exact choice of which depends on the “gold standard” for determining driving ability (on-road driving, driving simulator, driving problems) and whether a computerized or paper-and-pencil task is required.