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AuthorHong, Zhu
AuthorHan, Tianyang
AuthorAlhajyaseen, Wael K.M.
AuthorIryo-Asano, Miho
AuthorNakamura, Hideki
Available date2022-10-10T06:05:04Z
Publication Date2022-08-31
Publication NameAccident Analysis & Prevention
Identifierhttp://dx.doi.org/10.1016/j.aap.2022.106711
CitationZhu, H., Han, T., Alhajyaseen, W. K., Iryo-Asano, M., & Nakamura, H. (2022). Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks. Accident Analysis & Prevention, 173, 106711.
ISSN00014575
URIhttps://www.sciencedirect.com/science/article/pii/S0001457522001476
URIhttp://hdl.handle.net/10576/34972
AbstractPedestrian 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.
SponsorThis publication was made possible by the Qatar–Japan Research Collaboration Application Award [M-QJRC-2020-8] from Qatar University. The statements made herein are the sole responsibility of the authors. This research was also partially supported by Kurata Grants No. 1397 of the Hitachi Global Foundation.
Languageen
PublisherElsevier
SubjectUnsignalized mid-block crosswalks
Automated vehicle
Hybrid model
Distracted pedestrians
Crossing speed profile
Reinforcement learning
TitleCan automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks
TypeArticle
Volume Number173
dc.accessType Full Text


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