目的 为提升自动驾驶汽车（AVs）在无信控路段与行人的交互效率,采用眼动、认知与脑电实验相结合的方法优化AVs-eHMI设计。方法 首先,基于AHP-TOPSIS与E-Prime平台评估不同界面信息组合的认知表现;其次,运用眼动追踪分析不同eHMI界面安装位置对视觉注意的影响;最后,通过脑电实验,从神经响应角度验证色彩刺激对注意力与决策效率的影响。结果 车牌上方与挡风玻璃底部为eHMI界面最佳布置信息区域,图文结合的界面反应时间最短（1.08 s）,优于单一形式;黄色刺激诱发最高P300峰值（11.746 μV）,认知表现最佳,且以行人为中心的设计可有效降低认知负荷。结论 研究构建了融合认知优化、视觉引导、与神经机制多维度信息eHMI设计框架,为无信控环境下自动驾驶安全交互及eHMI标准化提供理论依据。
Abstract
The work aims to combine eye tracking, cognition and electroencephalogram (EEG) experiments to optimize the eHMI design to improve the interaction efficiency between autonomous vehicles (AVs) and pedestrians on uncontrolled road sections. Firstly, the cognitive performance of different interface information combinations was evaluated using AHP-TOPSIS and E-Prime. Eye movement experiments were used to analyze the influence of different installation positions of eHMI interfaces on visual attention. Finally, through EEG experiments, the influence of color stimulation on attention and decision-making efficiency was verified from the perspective of neural response. The upper part of the license plate and the bottom of the windshield were the optimal installation areas of the eHMI. The reaction time of the graphic and text combined interface was the shortest (1.08 s), which was superior to the single form. The yellow stimulation triggered the highest peak of P300 (11.746 μV), with the best cognitive performance. Moreover, the pedestrian-centered design could effectively reduce the cognitive load. The research constructs an eHMI design framework integrating visual guidance, cognitive optimization and neural mechanisms, providing theoretical support for the safe interaction and standardization of autonomous driving in an environment without signal control.
关键词
车外人机交互界面（eHMI） /
脑电（EEG） /
无信控路段 /
自动驾驶汽车（AVs） /
眼动实验 /
AHP-TOPSIS
Key words
external human computer interaction interface (eHMI) /
electroencephalogram (EEG) /
road sections without signal control /
autonomous vehicle (AVs) /
eye tracking experiment /
AHP-TOPSIS
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基金
国家自然科学基金项目（52575294）; 中国博士后科学基金（2020M672101）; 中国齐鲁工业大学国际合作基金（QLUTGJHZ2018022）; 齐鲁工业大学创新团队基金和齐鲁工业大学合作创新基金（2021CXY-04）
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