Research on application method of intelligent driving technology based on monocular vision sensor

Abstract

With the development of driverless cars, intelligent driving technology is increasingly used in the automotive industry, monocular vision sensor plays an indispensable role in intelligent driving technology because of its simple structure, low cost and abundant information. This paper discusses and optimizes the application of the monocular vision sensor in intelligent driving. The basic principle and key technologies of the monocular vision sensor are described in detail. In the specific application of the monocular vision sensor, this paper focuses on the monocular vision sensor's depth learning network, multi-information fusion technology, improved target detection and tracking algorithm. Through in-depth research and analysis, a series of optimization strategies based on the monocular vision sensor, such as the FAST Region Convolutional Neural Network (FASTRCNN) vehicle target detection method and improved Scale-Invariant Feature Transform (SIFT) algorithm, are proposed. Finally, this paper summarizes the intelligent driving technology based on the monocular vision sensor and suggests that the monocular vision sensor will play a more important role in intelligent driving technology. Future research shall focus on improving the accuracy of the algorithm, such as the development of end-to-end convolutional neural network fusion methods, the proposed depth multi-modal sensor fusion network, and so on.