The increasing demand for autonomous vehicles (AVs) in transportation services has prompted authorities to optimize traffic performance, reduce energy costs, enhance drivers' safety, and detect traffic violations. Although AVs are equipped with various sensors such as Lidar, speed, lights, etc, in order to analyze the interactions in a mixed traffic flow, and to keep track of the unusual patterns observed in received sensor data, analysis solely on the sensor-based dataset is not enough. In this thesis, this challenge is tackled by leveraging fused information coming from two distinct sources: sensors implemented on the shuttles (AVs) and cameras implemented alongside the road.