This paper presents the ACC system for autonomous trams, including three core processes: object detection, collision assessment and control. The system utilizes the YOLOX deep learning model for camera-based object detection and a clustering approach for LiDAR point clouds, integrating both outputs through a late fusion method for enhanced accuracy. The positions of objects and the tram are projected onto a data map to assess collision risk. This data map, which is a virtual 2D space configured with the track’s UTM coordinates and ROI lines, is used to determine whether objects are located within the ROI. The ACC system adjusts the tram’s speed to control acceleration or deceleration based on the relative distances of objects within the ROI. Experiments on a test track demonstrate effective collision avoidance and reliable ACC system operation under predefined scenarios.
