Towards Behavioural Cloning for Autonomous Driving

This paper proposes an off-policy imitation learning methodology for autonomous driving using a doubly-deep recurrent convolutional architecture that learns compositional representations in both space and time domains. The architecture has been referred to as NAVNet (Navigation Network) and is end-to-end trainable. The recurrent long-term models are directly connected with the visual convolutional models. The models can be trained together to learn both temporal dynamics as well as the convolutional perceptual representations. The approach is non-data driven in nature and the system learns a regression-based mapping function between input images and steering angle. Results presented in this research indicate distinct advantages of the proposed LRCN model over the state-of-the-art deep learning techniques for autonomous navigation