Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

arXiv:2403.06524v1 Announce Type: new
Abstract: We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on …

abstract acc arxiv autonomous autonomous trucks change control cost cruise cs.ai cs.lg cs.ro decision decision making framework making reinforcement reinforcement learning results total truck trucks type