Work

Tactical decision making for autonomous trucks by deep reinforcement learning with total cost of operation based reward

Year: 2025

Type: article

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.

Source: Artificial Intelligence Review

DOI: 10.1007/s10462-025-11448-8

Authors: Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

Institutions: Volvo (Sweden), Chalmers University of Technology, University of Gothenburg

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: Chalmers Tekniska Högskola, Knut och Alice Wallenbergs Stiftelse