Effects of environmental feature selection on end‐to‐end vehicle steering controller

Abstract

The method of deep learning has been widely used for end-to-end vehicle controllers' training because of its approximately non-linear functions. However, the convolutional neural network (CNN) training process requires a large amount of labelled data sets and takes a lot of time. Aiming at this problem, three experimental frameworks are designed to study the influence of single features on end-to-end controller in the simple and complex environments. The performance of controllers trained with different features is analysed, and the criteria of feature selection are given to reduce calculation cost. Firstly, two types of images with different environmental complexities are collected and pre-processed into three types of missing feature sets (data sets for sky, roadside, and road features are discarded, respectively). Then, based on the NVIDIA network model, the feasibility and road verification of the three frameworks designed are carried out. The experimental results show that: (i) Road features are indispensable to the training controller in simple or complex environments; (ii) roadside features help to improve the generalisation of the controller; (iii) sky features play a limited role in training vehicle steering controllers.