Anti-rollover Artificial Potential Field Motion Planning Method of an Autonomous Heavy Truck Optimised by Game Theory

Abstract Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks. This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk. The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion, lateral motion, yaw motion, sprung mass roll motion, unsprung mass roll motion, and an anti-rollover artificial potential field (APF) is proposed based on this. The motion planning method, which is based on model predictive control (MPC), combines trajectory tracking, anti-rollover APF, and the improved obstacle avoidance APF and considers the truck dynamics constraints, obstacle avoidance, and anti-rollover. Furthermore, by using game theory, the coefficients of the two APF functions are optimised, and an optimal path is planned. The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios. The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.