Driving Maneuvers Prediction Based Autonomous Driving Control by Deep Monte Carlo Tree Search

Abstract

Autonomous driving has attracted significant attention in recent years. With the booming of artificial intelligence (AI), deep learning technologies have been applied to autonomous driving to help vehicles better perceive the environment. Besides the perceiving environment, predictive driving is another prominent smooth control and safe driving skill for human drivers. In this work, we develop a deep Monte Carlo Tree Search (deep-MCTS) control method for vision-based autonomous driving. Compared with existing deep learning-based autonomous driving control methods, our method can predict driving maneuvers to help improve the stability and performance of driving control. Two deep neural networks (DNNs) are employed for predicting action-state transformation and obtaining action-selection probabilities, respectively. The deep-MCTS utilizes the predicted information of the two DNNs and reconstructs multiple possible trajectories to predict driving maneuvers. An optimal trajectory is selected by the deep-MCTS based on both current road conditions and predicted driving maneuvers. The proposed method achieves high control stability by avoiding sharp turns and driving deviations. We implement our algorithm in the Udacity and Torcs self-driving environments. The testing results show that our algorithm achieves a significant improvement in training efficiency, the stability of steering control, and stability of driving trajectory compared to existing methods.