Study of Driver's Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement

IF 6.6 1区 计算机科学 Q1 Multidisciplinary Tsinghua Science and Technology Pub Date : 2024-12-09 DOI:10.26599/TST.2024.9010002
Bilu Li;Xin Pei;Dan Zhang;Xinmiao Zhang;Zhuoran Li;Duanrui Yu;Shifei Shen
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Abstract

Understanding how drivers perceive and respond to external stimuli in driving tasks is important for the development of advanced driving technologies and human-computer interaction. In this paper, we conducted a temporal response analysis between driving data and cortical activation data measured by functional near-infrared spectroscopy (fNIRS), based on a naturalistic driving experiment. Temporal response function analysis indicates that stimuli, which elicit significant responses of drivers include distance, acceleration, time headway, and the velocity of the preceding vehicle. For these stimuli, the time lags and response patterns were further discussed. The influencing factors on drivers' perception were also studied based on various driver characteristics. These conclusions can provide guidance for the construction of carfollowing models, the safety assessment of drivers and the improvement of advanced driving technologies.
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基于自然驾驶实验和 fNIRS 测量的驾驶员驾驶任务感知研究
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来源期刊
Tsinghua Science and Technology
Tsinghua Science and Technology COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
10.20
自引率
10.60%
发文量
2340
期刊介绍: Tsinghua Science and Technology (Tsinghua Sci Technol) started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.
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Front Cover Contents Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems Study of Driver's Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement Deep Time-Frequency Denoising Transform Defense for Spectrum Monitoring in Integrated Networks
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