Autonomous navigation algorithm for intelligent vehicle in weak GPS environment
1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang， Jiangsu 212013, China； 2. Department of Computer and Information Science, University of MichiganDearborn, Dearborn， Michigan MI 48128, USA
Abstract： To solve the problem that global positioning system (GPS) was easy to fail in urban environment, an autonomous navigation algorithm for intelligent vehicle was proposed by visual location recognition. To identify the global position of vehicle in the GPSfree environment, the discriminative visual features in the image were obtained through the attention model and the finegrained feature extraction module, and the matching retrieval between the aerial image and the offline satellite image was realized. According to the vehicle position information, the elite ant colony optimization algorithm was used to output the direction of the road branch ahead for the vehicle and perform global path planning. The results show that the finegrained feature extraction module can extract more discriminative features. The labelsmoothed crossentropy loss function training can be used to achieve effective identification of actual environmental locations, and vehicles can use the proposed algorithm to navigate autonomously in weak GPS environment.
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