Vehicle Tracking under Vehicle-Road Collaboration Using Improved Particle Flow Filtering Algorithm

In recent years, with the rapid development of the driverless technology, how to accurately track the target vehicle has become a concern. With the advancement of the global satellite navigation system, the positioning accuracy of the vehicle has been greatly improved. However, in some cases, GPS signal is easy to be interfered, such as in cities with high-rise buildings. Therefore, how to ensure the accuracy of positioning in complex clutter environment and achieve reliable target tracking has become the main research content of this work. In this paper, a framework suitable for vehicle-road data fusion is proposed, which mainly receives perception information from the roadside and on-board GPS. After that, based on this framework, an improved IMM-JPDA-PFF algorithm for updating the fusion is proposed to improve the accuracy of vehicle tracking in complex clutter environments. This paper evaluates the method by means of matlab simulation, and the experiments show that the method can adapt to the clutter scene with changing GPS accuracy. Besides, the adaptability and the calculation efficiency are significantly enhanced than the traditional particle filter algorithm does. Therefore, this method helps to improve the safety and reliability of automatic driving assistance systems.