With the development of autonomous driving technology, intelligent vehicle detection and tracking has become a key research field. The traditional method based on a single visual sensor faces challenges in complex scenes. Therefore, the method of multi-visual information fusion has become an important way to improve the performance of detection and tracking. This research aims to explore an intelligent vehicle detection and tracking method based on multi-visual information fusion function model. First, we propose a multi-visual information fusion functional model and combine them through a feature fusion module. In this way, we can comprehensively utilize the advantages of multiple vision sensors to improve the accuracy and robustness of detection and tracking. Extensive experimental evaluations are then performed on real datasets. The final experimental results show that the intelligent vehicle detection and tracking method based on the multi-visual information fusion function model has higher accuracy and robustness than the traditional method. By comprehensively utilizing information from multiple vision sensors, we can better deal with complex scenarios and challenges, and provide more reliable perception capabilities for autonomous driving systems. The results of this research are of great significance for promoting the development of intelligent vehicle detection and tracking technology, and also provide reference and reference for multi-sensor information fusion methods in other fields.