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 investiga...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.展开更多
Drivers are the center of vehicles and transportation systems.Because of the rapid development of advanced technologies,artificial drivers have been developed as key elements in vehicles and transportation systems.The...Drivers are the center of vehicles and transportation systems.Because of the rapid development of advanced technologies,artificial drivers have been developed as key elements in vehicles and transportation systems.The inconsistency between human drivers and artificial drivers will lead to accidents and congestion.To make future vehicles and transportation systems trustworthy in driving safety and acceptable in travel efficiency,developing technologies based on human drivers’reliable knowledge and cognitive intelligence together with smart operations is an essential and promising solution.However,there are many challenges to be addressed including the learning of smart human perception,reliable smart inference strategies in decision-making,adaptive correction of inappropriate driving operation,knowledge mapping and enhancement of smart human driving in various scenarios,etc.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52272421).
文摘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.
文摘Drivers are the center of vehicles and transportation systems.Because of the rapid development of advanced technologies,artificial drivers have been developed as key elements in vehicles and transportation systems.The inconsistency between human drivers and artificial drivers will lead to accidents and congestion.To make future vehicles and transportation systems trustworthy in driving safety and acceptable in travel efficiency,developing technologies based on human drivers’reliable knowledge and cognitive intelligence together with smart operations is an essential and promising solution.However,there are many challenges to be addressed including the learning of smart human perception,reliable smart inference strategies in decision-making,adaptive correction of inappropriate driving operation,knowledge mapping and enhancement of smart human driving in various scenarios,etc.
