Autonomous Vehicle Driving Path Control with Deep Reinforcement Learning

Autonomous vehicle (AV) uses the artificial intelligence (AI) technologies to control the vehicle without human intervention. The implementation of AV has the advantages over the human-driven vehicle such as reducing the road traffic deaths that caused by human errors, increasing the traffic efficiency and minimizing the carbon emission to save the environment. The main objective of this paper is to develop an AV that keeps a safe distance while following the lead car and remains at the centerline of road. The proposed Deep Reinforcement Learning (DRL) algorithm for the autonomous driving simulation is Deep Deterministic Policy Gradient (DDPG). In this paper, the DDPG model for the path following control, reward function, actor network and critic network are created. The DDPG agent has been trained until 1650-episode rewards have been received. After the training, the proposed DDPG agent has been simulated to verify the performance. Then, the values of the two hyperparameters, which are mini-batch size and actor learning rate, are tuned to obtain the shortest training time.