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.

翻译：理解行人与自动驾驶车辆（AVs）交互时如何调整其运动行为，对于提升混合交通环境下的安全性至关重要。本研究基于NuScenes数据集，采用以残差Logit（ResLogit）模型为核心的混合离散选择-机器学习框架，分析了路段中段遭遇场景下的微观行人行为。该模型整合了时间、空间、运动学及感知指标，包括相对速度、视觉迫近效应、剩余距离以及定向碰撞风险邻近度（CRP）度量。结果表明，尽管模型的预测性能处于中等水平，但部分变量可能对运动调整产生显著影响。边际效应与弹性分析揭示了风险感知存在强烈的方向不对称性：前方与后方CRP呈现相反的影响方向。剩余距离则显示出可能存在中途穿越阈值。相对速度线索的影响相对较弱。这些行为模式可能反映了由风险感知与运动效率共同驱动的多重行为倾向。