研究自动驾驶车与行人相遇时的微行为，发现前后方向风险感知差异大。
Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset
- 用混合模型分析行人与自动驾驶车交互时的移动决策机制。
- 前后方碰撞风险感知差异显著，剩余距离存在中段过街阈值。
- 适合交通规划、自动驾驶安全设计人员阅读。
理解行人与自动驾驶车辆（AVs）在混合交通中的互动行为对提升安全性至关重要。本研究基于NuScenes数据集，采用融合离散选择与机器学习的混合框架（基于残差逻辑回归模型），分析路口中段行人与车辆相遇时的微观行为。模型纳入时间、空间、运动学及感知指标，包括相对速度、视觉逼近度、剩余距离和方向性碰撞风险接近度（CRP）。结果显示，部分变量显著影响行人移动调整，但预测性能中等。边际效应与弹性分析表明风险感知存在强烈方向不对称性：前方与后方CRP的影响相反；剩余距离可能在中段过街处存在阈值效应。相对速度线索影响较小。这些模式可能反映由风险感知与移动效率共同驱动的多种行为倾向。
原文摘要 · Abstract (English)
Understanding how pedestrians adjust their movement when interacting with autonomous vehicles (AVs) is essential for improving safety in mixed traffic. This study examines micro-level pedestrian behaviour during midblock encounters in the NuScenes dataset using a hybrid discrete choice-machine learning framework based on the Residual Logit (ResLogit) model. The model incorporates temporal, spatial, kinematic, and perceptual indicators. These include relative speed, visual looming, remaining distance, and directional collision risk proximity (CRP) measures. Results suggest that some of these variables may meaningfully influence movement adjustments, although predictive performance remains moderate. Marginal effects and elasticities indicate strong directional asymmetries in risk perception, with frontal and rear CRP showing opposite influences. The remaining distance exhibits a possible mid-crossing threshold. Relative speed cues appear to have a comparatively less effect. These patterns may reflect multiple behavioural tendencies driven by both risk perception and movement efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。