Biblio

Train operation is highly influenced by the rail track state and the surrounding environment. An abnormal obstacle on the rail track will pose a severe threat to the safe operation of urban rail transit. The existing general obstacle detection approaches do not consider the specific urban rail environment and requirements. In this paper, we propose an edge intelligence (EI)-based obstacle intrusion detection system to detect accurate obstacle intrusion in real-time. A two-stage lightweight deep learning model is designed to detect obstacle intrusion and obtain the distance from the train to the obstacle. Edge computing (EC) and 5G are used to conduct the detection model and improve the real-time detection performance. A multi-agent reinforcement learning-based offloading and service migration model is formulated to optimize the edge computing resource. Experimental results show that the two-stage intrusion detection model with the reinforcement learning (RL)-based edge resource optimization model can achieve higher detection accuracy and real-time performance compared to traditional methods.