Abstract
Hazard Perception can be considered to be situation cognition for dangerous situations in the traffic environment. Enough cognition could ensure drivers’ safety especially when they are facing emergent situations, which can ensure that drivers have full time to make timely response. Maintaining constant attention is necessary for drivers which could help them to better control vehicles and then avoid conflicts effectively. Drivers’ abilities to concentrate, visual search and distraction will affect brain waves, and drivers’ attention requires coordination between brain waves in different brain regions. Some researches extracted drivers’ electroencephalography (EEG) to explore the changes of their brain waves, and previous studies indicated that different frequency bands within the normal EEG frequency range reflected quite different cognitive processes. Moreover, some researches always associate with different brain areas to explore wave activity in different frequency bands. However, there are limited studies explore how could drivers’ EEG signals influence traffic safety and which EEG variables could measure drivers’ attention. The purpose of the study is to examine which EEG variables could be taken as measurement indexes to evaluate drivers’ attention level, furthermore we compare the differences of these EEG variables under different collision avoidance results. The experimental results of this study would lead to a better understanding of choosing which EEG variables could be used to measure drivers’ attention during the emergent collision avoidance process, and how drivers’ EEG variables changed could avoid the happening of conflicting.
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The work described in this paper was supported by the National Natural Science Foundation of China (No. 71621001 and No. 71771014) and BJTU Basic Scientific Research (2017YJS108).
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Zhang, X., Yan, X. (2020). Comparing the Differences of EEG Signals Based on Collision and Non-collision Cases. In: Stanton, N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham. https://doi.org/10.1007/978-3-030-20503-4_33
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DOI: https://doi.org/10.1007/978-3-030-20503-4_33
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