Self-driving scale car trained by Deep reinforcement learning
This work addresses generalization and safety issues in self-driving for scale cars, but it is incremental as it applies existing sim2real and reinforcement learning techniques to a specific domain.
The paper tackled the problem of low generalization ability in end-to-end self-driving control by using a sim2real method with a virtual simulation environment and double Deep Q-network, and demonstrated that the trained model enabled a scale car to achieve autonomous driving in the real world.
The self-driving based on deep reinforcement learning, as the most important application of artificial intelligence, has become a popular topic. Most of the current self-driving methods focus on how to directly learn end-to-end self-driving control strategy from the raw sensory data. Essentially, this control strategy can be considered as a mapping between images and driving behavior, which usually faces a problem of low generalization ability. To improve the generalization ability for the driving behavior, the reinforcement learning method requires extrinsic reward from the real environment, which may damage the car. In order to obtain a good generalization ability in safety, a virtual simulation environment that can be constructed different driving scene is designed by Unity. A theoretical model is established and analyzed in the virtual simulation environment, and it is trained by double Deep Q-network. Then, the trained model is migrated to a scale car in real world. This process is also called a sim2real method. The sim2real training method efficiently handle the these two problems. The simulations and experiments are carried out to evaluate the performance and effectiveness of the proposed algorithm. Finally, it is demonstrated that the scale car in real world obtain the capability for autonomous driving.