Energy-Efficient Trajectory Optimization of Autonomous Driving Systems With Personalized Driving Style

Abstract

This article proposes a three-layer framework for autonomous driving systems with optimized trajectory generation and tracking, ensuring optimal energy efficiency during the whole process. The third-order minimize curvature method is built in the first layer, which generates the personalized reference path by smoothing the human driving path to reduce its curvature. The energy-efficient strategy in the second layer is mainly through adopting the motor efficiency map to achieve the best efficiency interval of the operation motor's speed and acceleration, resulting in an optimized trajectory with motor efficiency consideration (OTHDM). The third layer integrates the OTHDM with the model predictive control module built based on vehicle dynamics to generate the optimal steering angle and achieve accurate tracking. The performance is validated through detailed numerical analysis and real human driving data collected by Honda Research Institute, including dozens of different driving scenarios and 19 343 tracks. Various test environments have been established in CarMaker. The experimental results indicate that our method can ensure low energy cost without energy recovery and battery hardware control in various scenarios. The energy saving rate can reach about 5%, up to more than 7%.