Traffic safety is one of the biggest challenges of connected and autonomous vehicles (CAVs). The safety of CAVs relies largely on the awareness of surroundings provided by the surveillance systems and motion detection in the ego vehicle. Many factors, such as weather, interferences, and sensibilities, contribute to the effectiveness of real world detection. We propose a system that mitigates CAV safety by sending collision warnings to the vehicles around. The system analyzes the trajectories of the vehicles embedded in the basic safety massages (BSMs) generated in the CAVs. Our system overcomes the obstacles in previous research studies when the time to perform a chain of tasks to avoid a crash after the vehicles come into the effective vehicle-to-vehicle (V2V) range may not be adequate. This paper focuses on the conflict detections using the real time BSMs with the following steps: (1) redefining the conflict, a key concept of surrogate safety analysis, originated from traffic simulation and improved for our system; (2) developing the mathematical algorithms to identify the conflicts; and (3) implementing the algorithms in the in-vehicle subsystem. The algorithm is tested on the SHARPII crash data, which contain data similar to BSMs. This paper is part of our ongoing project of Automatic Safety Diagnosis in Connected Vehicle Environment.
Conflict Identification Using Speed Distance Profile on Basic Safety Messages
International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington
2022-08-31
Conference paper
Electronic Resource
English
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