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Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual’s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant’s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject’s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

To evaluate the crosswind stability (overturning and sideslip) of vehicles driving on the bridge, obtaining critical wind speed is essential. The traditional method is based on the aerodynamic forces of moving vehicles on the bridge and the analysis of force equilibrations. However, various shapes of the bridge make the flow field around the vehicle on the bridge very complicated to obtain. In this paper, a simplified method is introduced to calculate the critical wind speeds of moving vehicles on bridges based on the influence of coefficients of the wind environment on the bridge and the aerodynamic forces of moving vehicles on open fields. The aerodynamic forces of moving vehicles are simulated with dynamic mesh techniques. Besides, the characteristics of the wind environment on the bridge deck are studied to evaluate the driving safety and determine the influence coefficient. To further demonstrate the reliability, critical wind speed in different road conditions of the proposed simplified method shows very good agreement with the traditional method.

Hazard prediction ability refers to a driver’s skill in anticipating and detecting potential road hazards. Drivers with good hazard prediction ability are able to effectively handle various traffic information of the road environment and evaluate predictive cues to help facilitate the early detection of hazards. Insight into the poor areas of hazard prediction ability for specific traffic scenarios provides drivers with valuable information about the kind of measures most urgently needed to improve their driving safety. In this study, a simulated driving experiment is conducted and the multiple layer DEA model is applied to assess drivers’ hazard prediction ability. On the basis of the results, those underperforming drivers are distinguished. Moreover, by analyzing the weights allocated to each indicator from the model, the most problematic scenario and indicator are identified for each driver, which leads up to specific driver improvement recommendations (such as training programs).