Distraction in the Driving Simulator: An Event-Related Potential (ERP) Study with Young, Middle-Aged, and Older Drivers

A multimodal physiological dataset for driving behaviour analysis

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Assessing drivers' cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers' natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

Cognitive aging at work and in daily life-a narrative review on challenges due to age-related changes in central cognitive functions

Demographic change is leading to an increasing proportion of older employees in the labor market. At the same time, work activities are becoming more and more complex and require a high degree of flexibility, adaptability, and cognitive performance. Cognitive control mechanism, which is subject to age-related changes and is important in numerous everyday and work activities, plays a special role. Executive functions with its core functions updating, shifting, and inhibition comprises cognitive control mechanisms that serve to plan, coordinate, and achieve higher-level goals especially in inexperienced and conflicting actions. In this review, influences of age-related changes in cognitive control are demonstrated with reference to work and real-life activities, in which the selection of an information or response in the presence of competing but task-irrelevant stimuli or responses is particularly required. These activities comprise the understanding of spoken language under difficult listening conditions, dual-task walking, car driving in critical traffic situations, and coping with work interruptions. Mechanisms for compensating age-related limitations in cognitive control and their neurophysiological correlates are discussed with a focus on EEG measures. The examples illustrate how to access influences of age and cognitive control on and in everyday and work activities, focusing on its functional role for the work ability and well-being of older people.

Tracking drivers' minds: Continuous evaluation of mental load and cognitive processing in a realistic driving simulator scenario by means of the EEG

Driving safety strongly depends on the driver's mental states and attention to the driving situation. Previous studies demonstrate a clear relationship between EEG measures and mental states, such as alertness and drowsiness, but often only map their mental state for a longer period of time. In this driving simulation study, we exploit the high temporal resolution of the EEG to capture fine-grained modulations in cognitive processes occurring before and after eye activity in the form of saccades, fixations, and eye blinks. A total of 15 subjects drove through an approximately 50-km course consisting of highway, country road, and urban passages. Based on the ratio of brain oscillatory alpha and theta activity, the total distance was classified into 10-m-long sections with low, medium, and high task loads. Blink-evoked and fixation-evoked event-related potentials, spectral perturbations, and lateralizations were analyzed as neuro-cognitive correlates of cognition and attention. Depending on EEG-based estimation of task load, these measures showed distinct patterns associated with driving behavior parameters such as speed and steering acceleration and represent a temporally highly resolved image of specific cognitive processes during driving. In future applications, combinations of these EEG measures could form the basis for driver warning systems which increase overall driving safety by considering rapid fluctuations in driver's attention and mental states.

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

Modelling braking behaviour of distracted young drivers in car-following interactions: A grouped random parameters duration model with heterogeneity-in-means

Braking is an important characteristic of driving behaviour that has a direct relationship with rear-end collisions in a car-following task. Braking becomes more crucial when drivers' cognitive workload increases because of using mobile phones whilst driving. This study, therefore, investigates and compares the effects of using mobile phones whilst driving on braking behaviour. Thirty-two young licenced drivers, evenly split by gender, faced a safety-critical event, that is, leader's hard braking, in a car-following situation. Each participant drove the CARRS-Q Advanced Driving Simulator and was required to respond to a braking event in the simulated environment in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A random parameters duration modelling approach is employed to (i) model drivers' braking (or deceleration) times using a parametric survival model, (ii) capture unobserved heterogeneity associated with braking times, and (iii) account for repeated experiment design. The model identifies the handheld phone condition as a random parameter whilst vehicle dynamics variables, hands-free phone condition, and driver-specific variables are found as fixed parameters. The model suggests that most distracted drivers (in the handheld condition) reduce their initial speeds more slowly than undistracted drivers, reflecting their delayed initial braking that may lead to abrupt braking to avoid a rear-end collision. Further, another group of distracted drivers exhibits faster braking (in the handheld condition), recognising the risk associated with mobile phone usage and delayed initial braking. Provisional licence holders are found to be slower in reducing their initial speeds than open licence holders, indicating their risk-taking behaviour because of their less experience and more sensitivity to mobile phone distraction. Overall, mobile phone distraction appears to impair the braking behaviour of young drivers, which poses significant safety concerns for traffic streams.

Distracted driving behavior in patients with insomnia

Insomnia is one of the most common sleep disorders and is characterized by a subjective perception of difficulty falling asleep. Drivers with insomnia are vulnerable to distraction and exhibit higher levels of risk while driving. This study investigated the effect of two sources of in-vehicle distractions on the driving performance of drivers with insomnia and good sleepers by analyzing different driving behavior measures. Twenty-one drivers with insomnia and twenty-one healthy volunteers were recruited to complete simulated driving dual tasks. The primary task required the participants to perform: (a) a lane-keeping task, and (b) a lane-change task. The secondary task required the participants to deal with: (a) baseline (non-task), (b) internal distraction task, and (c) external distraction task. The internal distraction task required participants to complete quantitative reasoning tasks, while the external distraction task was a 0-back test. The relationship between distracted driving ability and cognitive function was also investigated. The results demonstrate that for lane-keeping tasks, drivers with insomnia had significantly higher standard deviations (SD) for speed, throttle position, acceleration, and lateral position than healthy drivers under internal distraction, but the driving performance did not differ significantly between groups under internal distraction or baseline. In the lane-change task, drivers with insomnia had higher SDs for steering wheel angle, steer angular velocity, lateral acceleration, and lateral speed than healthy drivers under external distraction. Moreover, external distraction impaired driving behavior in the healthy group, while internal distraction impaired driving ability in both groups. Healthy drivers with cognitive impairment displayed impaired lane-keeping abilities under internal distractions and impaired lane-changing abilities under external distractions. Driving performance in the insomnia group was not significantly associated with cognitive function. The results demonstrate that insomnia and distraction impair driving ability, and driver performance is affected differently by the distraction source (internal or external). The driving ability of healthy drivers with decreased cognition was impaired, but not that of insomniacs.The findings of this study provide new insights for preventing and estimating the potential influence of distracted driving behavior in individuals with insomnia.

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations

The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.