Lane Change Decision-Making through Deep Reinforcement Learning

2021/12/24 by Ghimire, Mukesh, Choudhury, Malobika Roy, Lagudu, Guna Sekhar Sai Harsha
#15-04 #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2112.14705

Abstract

Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN performs better than the DQN method. The rule-based DQN achieves a safety rate of 0.8 and average speed of 47 MPH

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