利用自然数据集建模行人与自动驾驶车辆相遇时的行为
Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset
AI总结：
本研究利用NuScenes数据集，通过混合模型分析行人与自动驾驶车辆相遇时的行为，发现风险感知和运动效率影响行人运动调整。
摘要：
理解行人在与自动驾驶车辆（AVs）互动时如何调整运动对于提高混合交通中的安全性至关重要。本研究利用NuScenes数据集，采用基于残差Logit（ResLogit）模型的混合离散选择-机器学习框架，分析街区相遇中的微观层面行人行为。该模型整合了时间、空间、运动学和感知指标，包括相对速度、视觉逼近、剩余距离和方向碰撞风险接近（CRP）测量。结果显示，某些变量可能对运动调整有显著影响，尽管预测性能仍较为温和。边际效应和弹性系数表明，风险感知存在强烈的方向不对称性，前向和后向CRP表现出相反的影响。剩余距离可能具有中间穿越阈值。相对速度提示似乎影响较小。这些模式可能反映了由风险感知和运动效率驱动的多种行为倾向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
