Development of Road Recognition and Navigation Algorithm for Smart Cars Based on Machine Vision

The development of smart cars depends on machine vision technology, which extracts and identifies traffic environment information by simulating human visual system, which is very important to realize high-precision automatic driving. In this paper, a set of road identification and navigation algorithms for smart cars based on machine vision is proposed, aiming at improving the accuracy and robustness of road identification for smart cars and optimizing navigation path planning. The system architecture is divided into sensor module, data processing module and decision-making and feedback module, and various sensor technologies such as high-definition camera, LiDAR, millimeter-wave radar, GPS and INS are integrated to obtain comprehensive environmental awareness. The data processing module realizes the accurate detection of roads, traffic signs and obstacles through image enhancement, machine vision algorithm and deep learning model. The decision-making and feedback module plans the optimal path based on A* algorithm, and adopts the obstacle avoidance algorithm based on model predictive control (MPC) to make real-time obstacle avoidance decision. The experiment is carried out in a simulated urban road environment, and the results show that the system maintains a high accuracy rate of more than 90% under different weather conditions, which proves its good robustness and adaptability.