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

• URL: http://arxiv.org/abs/2403.06524v1
• Date: Mon, 11 Mar 2024 08:58:42 GMT
• Title: Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
• Authors: Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
• Abstract summary: We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions.
• Score: 4.404496835736175
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.

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