This paper studies the decision making problem of autonomous vehicles in traffic. We model the interaction between an autonomous vehicle and the environment as a stochastic Markov decision process (MDP) and consider the driving style of an experienced driver as the target to be learned. The road geometry is taken into consideration in the MDP model in order to incorporate more diverse driving styles. By designing the reward function of the MDP, the desired, driving behavior of the autonomous vehicle is obtained using reinforcement learning. Simulated results demonstrate the desired driving behaviors of an autonomous vehicle.
Highway Traffic Modeling and Decision Making for Autonomous Vehicle Using Reinforcement Learning
2018 IEEE Intelligent Vehicles Symposium (IV) ; 1227-1232
2018-06-01
1392168 byte
Conference paper
Electronic Resource
English
HIGHWAY TRAFFIC MODELING AND DECISION MAKING FOR AUTONOMOUS VEHICLE USING REINFORCEMENT LEARNING
British Library Conference Proceedings | 2018
|Intelligent vehicle highway ramp convergence decision-making method based on reinforcement learning
European Patent Office | 2023
|