Adverse Event Prevention on The Road System with Collaborative MEC

The localization of Road Users (RUs) is an important issue in adverse event prevention due to the unreliable nature of GPS and the high cost of high-precision location acquisition sensors. In addition, previous research on adverse event prevention on roads has not taken into account RUs in blind spots at the same time. To address these issues, we investigate a collaboration system for heterogeneous RUs and Mobile Edge Computing (MEC) servers, called Collaborative Adverse Event Prevention system (CAEP) to efficiently alert RUs to potential adverse events and perceive the blind spot of the RUs. CAEP includes two AI-based functional modules, a localization module and a blind spot detection module, and an adverse event prevention algorithm. The localization module localizes each RU and the blind spot detection module detects the other RUs in the blind spot. The adverse event prevention algorithm jointly considers general road collision events and the event of a difference in radius between the inner wheels of a vehicle to completely include adverse events on the road. We implement CAEP in a real-world traffic environment with Jetson AGX Xavier devices and cameras to evaluate the performance. Our evaluation shows that CAEP provides RUs with sufficient preparation time to prevent adverse events and correctly detects the RUs in blind spots.