Poster: Enhancing Autonomous Vehicles Safety through Edge-based Anomaly Detection
Qiren Wang, Ruijie Feng, Weisong Shi · 2023
With the growth of vehicular computing capacity, there is an increasing demand for real-time data processing. However, data is sometimes not optimal for purposes such as storage or training. To address this issue, we propose a solution to enhance vehicle safety by generating abnormal image data. We then leverage machine learning algorithms to detect and classify these anomalies while vehicles are in operation. An edge-based anomaly detection approach will be applied to prevent accidents and enhance the safety of connected vehicles.