Citation

Alozi AR, Hussein M. Transp. Res. Rec. 2023; 2677(7): 74-89.

Copyright

(Copyright © 2023, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981221151032

PMID

unavailable

Abstract

This study aims to conduct a thorough assessment of pedestrian and cyclist safety in autonomous vehicle (AV) environments. To that end, the study utilized AV sensor data of over 1,500 driving hours from five sources in Canada, the United States, and Singapore. The sensor data were used to extract conflicts between AVs and active road users. The conflicts were then processed to develop accurate estimates of AV collisions involving pedestrians and cyclists based on the extreme value theory. Further in-depth assessments were conducted on the identified conflicts, by type and location, to highlight potential issues leading to risky conflicts. The results showed that the total number of predicted AV collisions involving active road users was 2.17 collisions per million AV kilometers travelled. Collisions involving pedestrians were slightly higher than those involving cyclists. Also, collisions in clear weather conditions slightly exceeded collisions in adverse weather conditions, although the difference was not statistically significant. The relative risk of collisions was developed for both pedestrian and cyclist conflicts per AV movement type. The results showed that for pedestrians, interactions with right-turning AVs are the riskiest, while interactions with left-turning AVs are the riskiest for cyclists. A thorough analysis of conflicts revealed many issues, including a higher tendency for pedestrian violations when interacting with AVs, aggressive AV behavior (particularly when interacting with pedestrians while making a right turn), AVs struggling to predict the path of cyclists (mainly because of cyclist violations), and AVs failing to interpret pedestrian intentions in some cases.

Language: en