Research on a Vision-Based Perception Strategy for Autonomous Vehicle Control

This paper delves into a driverless vehicle control strategy based on visual perception technology and proposes a series of innovative solutions to address the main challenges faced by autonomous vehicles in practical applications—environmental perception and path planning. Initially, the existing visual perception techniques are thoroughly analyzed, with a focus on the SSD (Single Shot MultiBox Detector) algorithm and its application in autonomous driving. The SSD algorithm is favored in the autonomous driving field for its efficient real-time target detection capability. Additionally, the paper discusses improvements to the YOLOv5 algorithm and introduces monocular ranging-based image depth perception technology to tackle complex environmental perception issues faced by autonomous vehicles. For path planning, the paper suggests employing the RTT* (Rapidly-exploring Random Tree Star) algorithm, which can swiftly plan optimal driving paths in dynamic environments, ensuring the safety and efficiency of vehicle operation. Finally, the effectiveness and practicality of the proposed control strategy are validated through a series of simulation experiments.