Basic Safety Message Generation through a Video-based Analytics for Potential Safety Applications

Abstract

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the traffic and predict potential risks of crashes in real-time. If any risky behavior is observed, then the safety application can send warnings to the vehicles with risky behavior. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle's location, speed, acceleration, heading direction, and so on, in that section. In this study, we develop a video analytics-based basic safety message (BSM) generation method in accordance with the Society of Automotive Engineers standards (SAE J2945 and SAE J2735). Our developed BSM is further evaluated by conducting a field test where the results are compared with the ground truth results and cellular vehicle-to-everything (C-V2X) communication device-generated results. Our results demonstrate that our developed video-based BSM generation method outperforms the C-V2X generated results, and our method's errors are less than the maximum acceptable errors set by SAE J2945. Additionally, we conduct tests to assess the end-to-end latency of our developed method and found that the end-to-end latency is within the maximum allowable range for potential safety applications. We further propose use-case scenarios, illustrating how our developed BSM generation method can be utilized for potential safety applications.