The traditional automatic driving system has the problems of low perception accuracy and low driving intelligence. To solve this problem, the research proposes an autonomous driving system based on the Internet of Things and visual cognition. Based on the traditional system, a symmetrical visual cognition sensor is added and the internal module of the sensor is designed. Then, the road information is obtained by combining visual cognition technology, and the driving route is planned by the sparrow optimization algorithm. In the process of automatic driving, dynamic sensing optimization is realized through dynamic detection and path control navigation. The experimental results showed that the highest automatic driving perception accuracy obtained by the proposed automatic driving system was 100%, and the perception time was within 7.62 seconds. Compared with other automatic driving methods, it had higher perceptual accuracy and faster response speed. This shows that the proposed system can improve the comfort and safety of autonomous driving, providing a positive research path for autonomous driving technology in the automotive industry.
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