Learning a Deep Motion Planning Model for Autonomous Driving

Abstract

To deal with the issue of computational complexity and robustness of traditional motion planning methods for autonomous driving, an end-to-end motion planning model based on a deep cascaded neural network is proposed in this paper. The model can directly predict the driving parameters from the input sequence images. We combine two classical deep learning models including the convolution neural network (CNN) and the long short-term memory (LSTM) which are used to extract spatial and temporary features of the input images, respectively. The proposed model can fit the nonlinear relationship between the input sequence images and the output motion parameters for making the end-to-end planning. The experiments are conducted using the data collected from a driving simulator. Experimental results show that the proposed method can efficiently learn humans' driving behaviors, adapt to different roads, and has a better robustness performance than some existing methods.