Deep Q Learning Based High Level Driving Policy Determination

Kyushik Min, Hayoung Kim

Abstract: With the commercialization of various Driver Assistance Systems (DAS), those vehicles have some autonomous functions like Smart Cruise Control (SCC) and Lane Keeping System (LKS). It is believed that autonomous driving can be achieved by combining the DAS functions in the limited situations such as on highways. However, in order to coordinate the DAS functions for autonomous driving, a supervisor is needed to select an appropriate DAS function. In this paper, we propose a method for training a supervisor that selects proper DAS by deep reinforcement learning. The driving policy operates based on camera images and LIDAR data that are accessible in autonomous vehicles. Therefore, deep reinforcement learning network model is designed to analyze both camera image and LIDAR data. This system aims to drive in simulated traffic situation of highway without collision and with high speed. Unlike the systems which learn how to throttle, brake and steering directly, the proposed method can guarantee safe driving because the learned driving policy is based on the existing commercialized DAS functions. In order to verify the algorithms, a simulation tool is developed using Unity for highway environment with multiple vehicles and autonomous driving performance is compared with the proposed supervisor.