Affiliation:
1. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Abstract
Environmental perception forms the basis of intelligent driving systems and is a prerequisite for path planning and vehicle control. Among them, dynamic multi-obstacle tracking is the key to environmental perception. In order to solve the problem of a large amount of correlation calculations and false correlations in the process of dynamic multi-obstacle tracking, and to obtain more accurate surrounding environment information, this paper first designs an obstacle data correlation algorithm based on improving the joint probabilistic data-association algorithm. Then, in order to solve the problem of obstacle movement mobility and the poor filtering effect of a single model, the interacting multiple model is designed to complete the filtering of multiple behavior patterns of obstacles. An obstacle state estimation algorithm based on the unscented Kalman filter is designed to solve the nonlinear problem of obstacle motion. Finally, an experimental prototype is built and tested. The results show that the data association algorithm designed in this paper can complete the data association of obstacles at different times, and there is no problem of obstacle loss and association error. The average running time of each frame is 51.63 ms. The result comparison between the proposed method and the traditional method shows that the proposed method is more effective.
Funder
“Qing-Chuang science and technology plan” of colleges and universities in Shandong Province
National Natural Science Foundation Project of China
key R&D projects in Shandong
Major Innovation Projects in Shandong
Postdoctoral Science Foundation of China and Shandong