Noonan T Zach, Gershon Pnina, Domeyer Josh, Mehler Bruce, Reimer Bryan
MIT Center for Transportation & Logistics, AgeLab, Cambridge, Massachusetts.
Toyota Collaborative Safety Research Center, Toyota Motor North America, Ann Arbor, Michigan.
Traffic Inj Prev. 2022;23(sup1):S62-S67. doi: 10.1080/15389588.2022.2108023. Epub 2022 Aug 26.
This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction.
A 228 epoch vehicle-pedestrian interaction dataset was extracted from a large naturalistic driving data collection effort, which included vehicle, pedestrian, and contextual information (e.g., intersection type, jaywalking, vehicle maneuver, and lead vehicle presence). An expanded Actor-Partner Interdependence Model (APIM) was used to analyze driver-pedestrian dyads using driver and pedestrian standard deviations of velocity as the independent variables and wait times as dependent variables. APIM structural equation models were augmented to include driver effects (i.e., lead vehicle and maneuver type) and pedestrian effects (i.e., lead pedestrian, crossing group size, crossing direction).
The level of protection afforded by an intersection had an effect on the extent of driver-pedestrian dyadic behavior. Interactions in undesignated crossings (i.e., jaywalking) were associated with interdependent behavior whereas interactions in designated crossings (i.e., crosswalks and parking lots) showed a partner effect on the driver's wait time but no significant corresponding partner effect on the pedestrian. Finally, protected intersection interactions (i.e., traffic lights and stop signs) demonstrated no significant partner effects.
The difference in behavior patterns associated with the intersection type and level of protection shows that context can mediate the level of negotiation required between drivers and pedestrians. These findings inform how context and driver-pedestrian interactions should be incorporated in future modeling efforts which may, ultimately, support design of automated systems that are able to interact more safely, efficiently, and socially.
本文通过研究行人与驾驶员二元组在不同交叉路口类型（如斑马线、停车标志）间的相互依存关系，来描述其行为特征。此外，对相互依存关系的分析还涵盖了其他可能影响交互的外部因素，如其他车辆或行人。
从一项大型自然驾驶数据收集工作中提取了一个包含228个时段的车辆 - 行人交互数据集，其中包括车辆、行人及情境信息（如交叉路口类型、乱穿马路、车辆操纵动作以及前车的存在情况）。使用扩展的actor - partner相互依存模型（APIM），以驾驶员和行人速度的标准差作为自变量，等待时间作为因变量，来分析驾驶员 - 行人二元组。对APIM结构方程模型进行了扩充，纳入了驾驶员效应（即前车和操纵类型）和行人效应（即带头行人、过街群体规模、过街方向）。
交叉路口提供的保护水平对驾驶员 - 行人二元组行为的程度有影响。在未指定的交叉路口（即乱穿马路）的交互与相互依存行为相关，而在指定的交叉路口（即人行横道和停车场）的交互对驾驶员的等待时间显示出伙伴效应，但对行人没有显著的相应伙伴效应。最后，受保护的交叉路口交互（即交通信号灯和停车标志）未显示出显著的伙伴效应。
与交叉路口类型和保护水平相关的行为模式差异表明，情境可以调节驾驶员与行人之间所需的协商程度。这些发现为未来建模工作中如何纳入情境和驾驶员 - 行人交互提供了参考，这最终可能支持设计出能够更安全、高效且符合社交规范进行交互 的自动化系统。
