Department of Environmental Engineering and Architecture, Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8603, Japan.
Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, P.O.Box 2713, Doha, Qatar; Department of Civil & Architectural Engineering, College of Engineering, Qatar University, P.O.Box 2713, Doha, Qatar.
Accid Anal Prev. 2021 Dec;163:106468. doi: 10.1016/j.aap.2021.106468. Epub 2021 Nov 10.
Visibility can be identified as one of the critical determinants for the safety performance of autonomous vehicles (AVs) on unsignalized mid-block crosswalks (UMC), which may be significantly influenced by build-up environment and surrounding vehicles. This study investigates the safety performance when AVs interact with pedestrians approaching from far-side sidewalks to UMCs considering the visual occlusion of opposing vehicles. A mathematical model is proposed for judging the visibilities of objects from observers' location under the impact of visual obstacles and is embedded into an agent-based pedestrian-vehicle interaction framework. Two yielding decision modules is assumed for AVs: The normal decision module implements the pedestrian priority rule simply based on the current detectable information, whereas the memory aid decision module extends AVs' detection abilities by incorporating the memory data. Through simulation experiments, it is found that the percentages of short post encroachment time (%SPET) between AVs and far-side pedestrians reach peaks when the pedestrian flow rate is 300-400 ped/h. When opposing vehicles are in stationary queue conditions, %SPETs are only sensitive to the net distance between the last opposing vehicles in the queue and crosswalks (D). As the D decreases to lower than 15 m, %SPETs start to increase drastically. However, when opposing vehicles are in free flow conditions, %SPETs are influenced by multiple factors such as pedestrians' crossing decisions, sizes and flow rates of opposing vehicles. Furthermore, only when opposing vehicles are in free flow conditions, memory aid AVs can significantly eliminate the impacts of opposite vehicles. Finally, several countermeasures are developed to enhance the visibility and safety at UMCs based on the findings of this study.
可及性可以被视为自动驾驶车辆（AV）在无信号中央街区交叉口（UMC）安全性能的关键决定因素之一，其可能会受到环境和周围车辆的显著影响。本研究考虑到对面车辆的视觉遮挡，调查了 AV 与从远侧人行道接近 UMC 的行人交互时的安全性能。提出了一种用于判断观察者位置处物体可见性的数学模型，该模型受到视觉障碍物的影响，并嵌入基于代理的行人和车辆交互框架中。为 AV 假设了两个让行决策模块：正常决策模块简单地根据当前可检测信息实现行人优先规则，而记忆辅助决策模块通过结合记忆数据扩展 AV 的检测能力。通过仿真实验，发现当行人流率为 300-400 ped/h 时，AV 和远侧行人之间的短后侵入时间百分比（%SPET）达到峰值。当对面车辆处于静止排队状态时，%SPET 仅对队列中最后一辆对面车辆和交叉口之间的净距离（D）敏感。当 D 减小到低于 15 m 时，%SPET 开始急剧增加。然而，当对面车辆处于自由流动状态时，%SPET 会受到多个因素的影响，例如行人的穿越决策、对面车辆的大小和流率。此外，只有在对面车辆处于自由流动状态时，记忆辅助 AV 才能显著消除对面车辆的影响。最后，根据本研究的结果，提出了几种增强 UMC 可见性和安全性的对策。
