Knowledge extraction for automatic driving control by using local correlation features

Abstract

In recent years, with the rapid development of artificial intelligence technology, more and more companies have shifted their attention to the field of automatic driving. The target detection is one of the core technologies in the field of automatic driving, which can perceive the environment by using distributed sensors and affect the vehicle driving control. How to describe vehicle image is particularly important in target detection for automatic driving. In this paper, we combine edge information, HOG features and local association features to describe the targets in image for automatic driving. Then, the extracted features are used to learn a classifier for further vehicle detection. The experimental results show that the proposed method can achieve better prevision, recall, and F1‐measure than merely using edge features or HOG features. The proposed method can extract more comprehensive features for vehicle detection.