Applications of Visual Perception in Autonomous Driving

Abstract

This paper focuses on the application of visual perception in autonomous driving.  The types of cameras in visual perception systems are introduced, including charge-coupled device (CCD), and Complementary metal oxide semiconductor (CMOS) image sensors, and the LiDAR system with vision sensors. The applications of visual perception in autonomous driving are discussed, such as environmental perception (including road recognition, lane line detection, and traffic sign and signal light recognition) and vehicle positioning. However, visual perception technology faces challenges, such as the effects of light changes and interference from harsh weather on visual sensors. To address these challenges, solutions include the fusion of vision and other sensors, as well as progress in hardware and software technology. By optimizing and improving visual sensors in terms of precision, cost, power consumption, stability, and data perception and processing ability, the performance of autonomous driving systems can be enhanced. The multi-sensor fusion method utilizes the advantages of different sensors to obtain more comprehensive environmental information and improve the reliability and adaptability of the vehicle system. This paper can help autonomous driving researchers understand the application of visual sensors in autonomous driving more quickly.