Automated safety diagnosis of vehicle-bicycle interactions using computer vision analysis

This paper demonstrates an automated safety diagnosis approach for evaluating vehicle-bicycle conflicts using video analysis. The use of traffic conflicts for safety diagnosis is gaining acceptance as a surrogate for collision data analysis. Traffic conflicts can provide insight into the failure mechanism that leads to road collisions and do not require long observation periods. In the approach presented in this paper, traffic conflicts are automatically detected and their severity ranked using the Time to collision (TTC) safety indicator. As well, vehicle violations such as failure to respect yield signs are automatically identified. The safety diagnosis is also supplemented with automated classification and count of vehicles and bicycles. A case study is presented for diagnosing safety issues at a busy intersection downtown Vancouver, British Columbia. Vehicle-bicycle conflicts as well as vehicle rear-end and merging conflicts were identified and examined. The results showed a high exposure of cyclists to traffic conflicts and a significant driver non-compliance rate. Several countermeasures to mitigate the safety issues were presented and evaluated.