Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study

2020/07/16 by Teng Liu, Liu, Teng, Xingyu Mu +9
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2007.08343

openalex publication_date 2020/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.

Citations

• Dueling Network Architectures for Deep Reinforcement Learning

Related

• Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
• Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
• A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
• Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning