A Secure Trajectory Planning Method for Connected Autonomous Vehicles at Mining Site

Abstract

Recently, with the assistance of 5G networks and the Internet of Things, specialized applications of autonomous driving to mining sites have been explored, with the goal of realizing the unmanned operation of mining systems and enhancing the safety of the mining industry. After receiving the loading task, the autonomous driving system will generate a feasible trajectory for the mining truck. It requires that the trajectory be generated in advanced within a limited-time high-latency network. In addition, the secure trajectory planning for mining sites involves factors in the complex environment and an unstable network. Thus, a secure trajectory planning method for autonomous trucks at mining sites is proposed. It simplifies the planning by decoupling the planning into front-end path searching and back-end trajectory generation. First, the planner enhances the Hybrid A* search algorithm to find the hauling path within the boundary of the mining site, and then, it post-processes the path with a well-designed symmetric optimization-based method. Then, considering the interaction with other autonomous trucks, a topology-guided search method for secure decision making is proposed, considering the possibility of cybersecurity. The proposed method was validated in real scenarios of the mining environment. The results verify that the planner can generate the secure trajectory under network delay 2.0 s conditions.