Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
This work addresses highway driving efficiency for autonomous trucks, but it is incremental as it builds on existing methods with multi-objective reward tuning.
The paper tackles tactical decision-making for autonomous trucks using deep reinforcement learning, focusing on Adaptive Cruise Control and lane changes, and finds that separating high-level decisions from low-level control improves performance.
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.