Real-Time Operational Driving Energy Management with Stochastic Vehicles Behavior Prediction

摘要

This paper explains a novel adaptive cruise control (ACC) driving with coasting to improve fuel economy. The purpose is to reduce the energy loss with predictive control when the preceding vehicle decelerates, while acceptable driving feeling is guaranteed. To achieve this goal, prediction of the preceding vehicle behavior is introduced to determine the ego vehicle behavior realized by using inverse reinforcement learning (IRL). In addition, the evaluation function is designed to determine the best coasting timing by balancing longer coasting time and acceptable driving feeling, while the ego vehicle speed is controlled with a rule-based control at a non-coasting period. The performance of this control strategy has been validated with simulation, showing 9.7% fuel economy improvement on average for hybrid electric vehicles in the case of following the preceding vehicle before an intersection. It has also been verified with an actual test vehicle, where a high level balance between efficiency and acceptable feeling is realized.