Drivers’ EEG Responses to Different Distraction Tasks

Driver distraction has been deemed a major cause of traffic accidents.However,drivers’brain response activities to different distraction types have not been well investigated.The purpose of this study is to investigate the response of electroencephalography(EEG)activities to different distraction tasks.In the conducted simulation tests,three secondary tasks(i.e.,a clock task,a 2-back task,and a navigation task)are designed to induce different types of driver distractions.Twenty-four participants are recruited for the designed tests,and differences in drivers’brain response activities concerning distraction types are investigated.The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction.Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions.The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving,whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions.These results provide theoretical references for the development of distraction detection systems based on EEG signals.

Monitoring driver attention distraction with binocular vision

Driver attention distraction(DAD)is a typical artificial factor traffic accident,and DAD monitoring can improve driving security.In this study,a method was developed for accurate DAD monitoring based on binocular vision.A binocular vision system was built,and camera parameters of the system were calibrated based on Open CV.In the method,the driver’s facial image is obtained by using active infrared imaging technology and preprocessed to locate the eye positions.The connected component labeling algorithm for binary images is used to pinpoint the eye locations.The characteristic information of the eye pupils is extracted with the least-squares ellipse fitting algorithm,and the characteristic information of the Purkinje image is obtained with the Harris corner detection algorithm.A DAD warning model based on the binocular vision system was established to evaluate the attention state of the driver.

Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments

Purpose–The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.Design/methodology/approach–Field tests with 17 participants were conducted in the connected and automated vehicle test field.All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed.Demographic data,vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit,such as electrocardiograph for heart rate,electromyography for muscle strength,electrodermal activity for skin conductance and force-sensing resistor for braking pressure.Findings–This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs.The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.Originality/value–The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time.Therefore,the driver states in distracted driving are significantly different than in regular driving,the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics.To the best of the authors’knowledge,this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.