Distracting tasks have persisting effects on young and older drivers' braking performance

Multitasking: does task-switching add to the effect of dual-tasking on everyday-like driving behavior?

It is well established that performing multiple tasks simultaneously (dual-tasking) or sequentially (task-switching) degrades performance on one or both tasks. However, it is unknown whether task-switching adds to the effects of dual-tasking in a single setup. We investigated this in a simulated everyday-like car driving scenario. We expected an additive effect of task-switching on dual-tasking, leading to a stronger deterioration of driving performance due to the increased cognitive load required to handle multiple task-sets. Forty-five young adults aged 18 to 30 years (age: 23.62 ± 2.51, 28 females) were instructed to follow a lead car driving at a constant speed of 70 km/h through a rural landscape while concurrently performing additional tasks. The additional tasks were typing and arguing, in response to stimuli presented visually or auditorily. The tasks were presented either in separate blocks or in intermixed order (conditions: repetitive vs. switching). Driving performance was assessed by use of the average velocity and the standard deviation of lateral position, and performance in the additional tasks was assessed by reaction time. Linear-mixed effect models revealed better performance in the repetitive, compared to the switch condition only for the standard deviation of the lateral lane position while performing the additional typing task. This provides limited evidence for the view that task-switching adds to the challenges of dual-tasking. We therefore posit that already dual-tasking alone involves processing demands that are not substantially increased by adding switching demands.

Texting while driving is a visual problem influenced by phone viewing angle and working distance in young individuals

Purpose

To investigate the impact of smartphone viewing distance and angle on reaction times.

Design

A prospective, self-controlled, single-center study.

Methods

Participants engaged in a driving simulation facing a large screen with a simulated brake pedal. They were tasked to stop the simulation once recognizing the deceleration of upcoming traffic. Tests were conducted without distraction and with a standardized distraction simulating texting while driving (TWD). Smartphone positions varied at distances of 30 cm and 60 cm, and at angles parallel to and 30° below the road plane. Reaction times were measured from the onset of simulated closure to detection. Stopping distances were extrapolated using data from the National Highway Traffic Safety Administration.

Results

Ninety-four participants were included with a mean age of 24 ± 2.7 years. The control reaction time was 11.5 ± 4.1 s. Reaction times significantly decreased with smartphone placement at a closer distance of 30 cm parallel (17.0 ± 3.3 s) vs 60 cm parallel (15.4 ± 3.8 s), P < 0.001. A 30-degree downward placement at 30 cm (18.6 ± 4.0 s) and 60 cm (17.9 ± 3.6 s), further decreased reaction time compared to parallel phone positioning, P < 0.001. Extrapolating to stopping distances based on real-world data, smartphone distractions placed at 30 cm 30° below the dashboard had the greatest effect, resulting in a 3 times increase of stopping distance compared to the control, 1201 vs 394 ft respectively, P < 0.001.

Conclusion

TWD significantly delays reaction time in young participants. Both the distance and viewing angle of a smartphone significantly influences reaction times during driving simulations. The greatest delays are observed when the smartphone is positioned closer to the user and at a 30-degree angle which we hypothesize is due to vision blur from increased accommodation, loss of stereopsis, and fixation with the peripheral retina.

Effect of additional tasks on the reaction time of braking responses in simulated car driving: beyond the PRP effect

The presentation of one task increases the reaction time on a subsequent task, if stimulus onset asynchrony (SOA) between tasks is short. This psychological refractory period (PRP) effect is typically leveling off as SOA approaches 1 s, which has been documented both in classical laboratory paradigms and in simulated car driving. Here we report a more persistent effect on the subsequent task that goes well beyond the typical duration of the PRP effect. In a driving simulator, 120 healthy older participants followed a lead car that mostly drove at a constant speed. They had to maintain a regular distance from the lead car and had to brake when the lead car braked. Participants also engaged in several additional tasks during driving (two types of tasks: typing three-digit numbers, stating arguments on public issues). SOA between the braking task and the last preceding additional task was 11.49 s ± 1.99 (mean and standard deviation). In a control condition, the braking task was administered without additional tasks. Main performance outcome was Braking Reaction Time (RT, in s), as the interval between onset of brake lights of the lead car and the moment participants released the gas pedal. Additionally, foot movement time (MT, in s), i.e., the difference between gas pedal release and brake pedal onset, was considered for possible compensation behavior. Inter-vehicle distance to the lead car (in m) was taken into account as a moderator. We found that RT averaged 0.77 s without additional tasks, but averaged 1.45 s with additional tasks. This RT difference was less pronounced at smaller inter-vehicle distances, and was not compensated for by faster MT from the gas pedal to the brake pedal. We conclude that detrimental effects of additional tasks on subsequent braking responses can be more persistent than suggested by the PRP effect, possibly because of maintaining multiple task sets, requiring increased executive control. We further conclude that potential detrimental effects can be ameliorated at small inter-vehicle distances by mobilizing extra cognitive resources when response urgency is higher. As a practical implication of our study, distracting stimuli can have persisting detrimental effects on traffic safety.

CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

Individual differences, ADHD diagnosis, and driving performance: effects of traffic density and distraction type

The present study examined the impact of individual differences, attention, and memory deficits on distracted driving. Drivers with ADHD are more susceptible to distraction which results in more frequent collisions, violations, and licence suspensions. Consequently, the present investigation had 36 participants complete preliminary questionnaires, memory tasks, workload indices, and four, 4-min simulated driving scenarios to evaluate such impact. It was hypothesised ADHD diagnosis, type of cellular distraction, and traffic density would each differentially and substantively impact driving performance. Results indicated traffic density and distraction type significantly affected the objective driving facets measured, as well as subjective and secondary task performance. ADHD diagnosis directly impacted secondary task performance. Results further showed significant interactions between distraction type and traffic density on both brake pressure and steering wheel angle negatively impacting lateral and horizontal vehicle control. Altogether, these findings provide substantial empirical evidence for the deleterious effect of cellphone use on driving performance.Practitioner summary: This study examined how ADHD diagnosis, traffic density, and distraction type affect driver behaviour. Participants completed driving behaviour questionnaires, memory tasks, workload indices, and driving scenarios. Results showed that ADHD diagnosis impacted secondary task performance, while traffic and distractions significantly impacted driving performance as well secondary task performance and workload.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

Lifestyle Matters: Effects of Habitual Physical Activity on Driving Skills in Older Age

Research on multitasking driving has suggested age-related deterioration in driving performance. It has been shown that physical and cognitive functioning, which are related to driving performance and decline with aging, are positively associated with physical activity behavior. This study aimed to explore whether driving performance decline becomes severe with advancing age and whether physical activity behavior modifies age-related deterioration in driving performance. A total of one hundred forty-one healthy adults were categorized into three groups based on their age; old-old (74.21 ± 2.33 years), young-old (66.53 ± 1.50 years), and young adults (23.25 ± 2.82 years). Participants completed a realistic multitasking driving task. Physical activity and cardiorespiratory fitness levels were evaluated. Older groups drove more slowly and laterally than young adults, and old-old adults drove slower than young-old ones across the whole driving course. Physical activity level did not interact with the aging effect on driving performance, whereas cardiovascular fitness interacted. Higher-fitness young-old and young adults drove faster than higher-fitness old-old adults. Higher-fitness old adults drove more laterally than higher-fitness young adults. The present study demonstrated a gradual decline in driving performance in old adults, and cardiorespiratory fitness interacted with the aging effect on driving performance. Future research on the interaction of aging and physical activity behavior on driving performance in different age groups is of great value and may help deepen our knowledge.