Blockchain-Empowered Edge Intelligence for TACS Obstacle Detection: System Design and Performance Optimization

With the significant advantages of system complexity and operating costs, train autonomous circumambulate system (TACS) is gradually replacing the traditional communication-based train control system as the next-generation train operation control system development direction. As train operation and control become more decentralized and autonomous, real-time and accurate obstacle detection, apart from route-level protection, is quite desirable in TACS. Most of the existing researches about obstacle detection focus on detection algorithm optimization based on the once-deployed lifelong use principle, whereas model reoptimization based on the actual operating environment under unexpected situations and model sharing among multiusers are largely ignored. In this article, we design a novel obstacle detection system in TACS based on blockchain-empowered edge intelligence (EI). To make full use of the massive raw unannotated data collected online, we first propose an semisupervised learning-based TACS obstacle detection model. Considering the resource-hungry model training, we introduce EI into TACS and propose a multiagent reinforcement learning-based task offloading algorithm for secure and efficient computation offloading coordination. Furthermore, we propose a blockchain-based model sharing scheme to facilitate the multimodel parameter exchange and improve the obstacle detection accuracy. Extensive simulation results show that the designed obstacle detection system can effectively improve the TACS obstacle detection performance.