In order to advance the development of secure and effective self-driving technology, a novel approach for intelligent vehicle target detection and automated navigation has been introduced. To improve the exploitation of semantic information, we enhanced the FPN structure by using the Spatial Adaptive Filter (ASF) module and input it into the FPN structural layer. Then the ROS system is used to realize the function package to complete the configuration of parameters and the real-time construction of the map. On this basis, the elite ant colony algorithm is combined to realize the planning of optimal paths. The experimental outcomes demonstrate that in the improved algorithm scheme, the average accuracy mean under simple, medium, and complex categories is 87.12, 77.80, and 76.02 respectively. The set of four different types of obstacles can realize the path planning of the intelligent vehicle and obtain better results. To conclude, the effectiveness and feasibility of the program is verified by multiple sets of experimental data.
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