Motion Control of Autonomous Vehicle with Domain-Centralized Electronic and Electrical Architecture based on Predictive Reinforcement Learning Control Method

High-level autonomous vehicles and domain-based electronic and electrical (E/E) architectures are important development directions of the intelligent automobile industry. The domain-centralized E/E architecture has become the potential upgrade to the autonomous vehicle benefitting from its powerful software updates, cabling reduction, and functional integration. Aiming at the efficient motion control of the autonomous vehicle equipped with domain-centralized E/E architecture, a novel control framework with algorithms improvement is proposed in this paper, which contains the multi-hops loop delay analysis to solve the control stability problem caused by the heterogeneous topology loop delay of domain-centralized E/E architecture. In this framework, the motion controller is generated through the combination of modified double reinforcement learning algorithm and multi-steps predictive control method, and the loop delay is integrated into the controller optimization. Through the virtual driving environment simulation and real world scenario, the results show that the proposed framework achieves better performance in terms of path tracking and obstacles avoidance, and the stability of control strategies to loop delay is also guaranteed.