| 摘要: |
| 目的 为探索行人与自动驾驶汽车交互时自动驾驶汽车外显界面、道路交通环境，以及个体特征对行人过街决策时间的影响，并确定最优的外显界面呈现形式。方法 利用Unity 3D构建人车交互实验场景，招募志愿者参与模拟实验，收集行人在不同条件下的过街决策时间和行为数据，并利用生存分析模型进行统计分析。结果 多模态(视觉+听觉+物理)eHMI显著减少了行人的过街决策时间，其过街效率比单一物理模态界面提升了3.907倍;行人年龄与决策时间成正比，相较于青年人群，中年人的过街效率为青年人的0.532倍，而老年人的过街效率进一步降至青年人的0.316倍;此外，车辆从近车道驶近时也会显著缩短行人的决策时间，当车辆从远车道驶来时，行人的过街效率仅为车辆从近车道驶入时的70.1%。结论 性别对行人过街决策时间无显著影响;自动驾驶汽车外显界面、行人年龄及车辆驶近车道等因素对行人过街决策时间具有显著影响;行人在面对视觉+听觉+物理信息的外显界面时过街效率最高。 |
| 关键词: 自动驾驶汽车 人车交互 外显界面 过街决策时间 生存分析 |
| DOI：10.19554/j.cnki.1001-3563.2025.10.016 |
| 分类号: |
| 基金项目:浙江省自然科学基金(LY24E080003) |
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| Multi-modal Explicit Interface of Autonomous Vehicle Based on Human-vehicle Interaction Mechanism |
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ZHANG Zhijie, LU Lili, JIANG Jiaxuan, WANG Chengzhang
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(Ningbo University, Zhejiang Ningbo 315211, China)
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| Abstract: |
| The work aims to explore the impact of the eHMI of the autonomous vehicle, the road traffic environment and individual characteristics on the pedestrian's crossing decision time when pedestrians interact with the autonomous vehicle, and determine the optimal form of the eHMI. The experimental scenario of pedestrian-vehicle interaction was constructed by Unity 3D, the volunteers were recruited to participate in the simulation experiment, the crossing decision time and behavior data of pedestrians under different conditions were collected and the statistical analysis was carried out by the survival analysis model. The multi-modal (visual + auditory + physical) eHMI significantly reduced the pedestrian's crossing decision time, and its crossing efficiency was 3.907 times higher than that of the single physical modal interface. The pedestrian's age was proportional to the decision time. Compared with the young people, the crossing efficiency of the middle-aged was 0.532 times that of the young people, while the crossing efficiency of the elderly was further reduced to 0.316 times that of the young people. In addition, the vehicle approaching from the near lane also significantly shortened the pedestrian's decision time. When the vehicle approached from the far lane, the pedestrian's crossing efficiency was only 70.1% of that when the vehicle approached from the near lane. The gender has no significant effect on the pedestrian's crossing decision time. The eHMI of the autonomous vehicles, the pedestrian's age and the vehicle approaching lane all have significant impacts on pedestrians' crossing decision time. The highest crossing efficiency can be achieved when pedestrians face the eHMI of visual + auditory + physical information. |
| Key words: autonomous vehicles human-vehicle interaction explicit interface crossing decision time survival analysis |
