Department of Environmental Engineering and Architecture, Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8603, Japan.
Department of Civil Engineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Accid Anal Prev. 2022 Aug;173:106711. doi: 10.1016/j.aap.2022.106711. Epub 2022 May 19.
Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may significantly affect the safety performance of AVs, especially at unsignalized mid-block crosswalks (UMCs). However, there is no available motion-planning model for AVs that considers the effect of pedestrian distraction on UMCs. This study aims to explore innovative approaches for safe and reasonable automated driving in response to distracted pedestrians with various speed profiles at UMCs. Based on two common model design concepts, two new models are established for AVs: a rule-based model that solves motion plans through a fixed calculation procedure incorporating several optimization models, and a learning-based model that replaces the deterministic optimization process with policy-gradient reinforcement learning. The developed models were assessed through simulation experiments in which pedestrian speed profiles were defined using empirical data from field surveys. The results reveal that the learning-based model has outstanding safety performance, whereas the rule-based model leads to remarkable safety problems. For distracted pedestrians with significant crossing-speed changes, rule-based AVs lead to a 5.1% probability of serious conflict and a 1.4% crash probability. The learning-based model is oversensitive to risk and always induces high braking rates, which results in unnecessary efficiency loss. To overcome this, a hybrid model based on the learning-based model was developed, which introduces a rule-based acceleration value to regularize the action space of the proposed learning-based model. The results indicate that the hybrid approach outperforms the other two models in preventing crash hazards from distracted pedestrians by employing appropriate braking behaviors. The high safety performance of the hybrid models can be attributed to the spontaneous slowing down of the vehicle that initiates before detecting pedestrians on UMCs. Although such a cautious driving pattern leads to extra delay, the time cost of the hybrid model is acceptable considering the significant improvements in ensuring pedestrian safety.
行人分心可能会给自动驾驶汽车（AV）的控制带来严重困难，这可能会显著影响 AV 的安全性能，特别是在无信号的中央街区人行横道（UMC）处。然而，目前还没有针对考虑行人分心对 UMC 影响的 AV 运动规划模型。本研究旨在探索创新方法，以实现安全合理的自动驾驶，应对 UMC 处具有各种速度分布的分心行人。基于两种常见的模型设计概念，为 AV 建立了两个新模型：基于规则的模型，通过包含几个优化模型的固定计算程序来解决运动计划；以及基于学习的模型，用策略梯度强化学习替代确定性优化过程。通过使用实地调查的经验数据定义行人速度分布的模拟实验对开发的模型进行了评估。结果表明，基于学习的模型具有出色的安全性能，而基于规则的模型则导致显著的安全问题。对于具有明显穿越速度变化的分心行人，基于规则的 AV 导致严重冲突的概率为 5.1%，碰撞概率为 1.4%。基于学习的模型对风险过于敏感，总是导致高制动率，从而导致不必要的效率损失。为了克服这个问题，基于学习的模型开发了一种混合模型，该模型引入了基于规则的加速度值来规范所提出的基于学习的模型的动作空间。结果表明，混合方法通过采用适当的制动行为来防止分心行人造成的碰撞危险，在防止分心行人造成的碰撞危险方面优于其他两种模型。混合模型的高安全性性能可归因于车辆在 UMC 上检测到行人之前自发减速。虽然这种谨慎的驾驶模式会导致额外的延迟，但考虑到确保行人安全的显著改进，混合模型的时间成本是可以接受的。
