Cooperative Vehicle Tracking using Particle Filter Integrated with Interacting Multiple Models

Recently, targets tracking plays an important role in the rapidly expanding field of autonomous driving. With the presentation of vehicle-to-vehicle (V2V) communication standards and the improvement of Global Navigation Satellite System (GNSS) technology, the accuracy of cooperative vehicle tracking should be greatly improved. However, high-precision positioning suffers from a number of vulnerabilities and is unstable in clutter environment. In this paper, a modified approach using particle filter (PF) integrated with Interacting Multiple Models (IMM) algorithm is proposed to improve the accuracy of cooperative vehicle tracking in changing V2V environment. This paper focuses on a novel framework for cooperative vehicle tracking and implementation of data fusion originating from positioning information receiving from radars and connected vehicles. The simulations on scenarios of single vehicle and multi-vehicle tracking were implemented by utilizing Matlab. The performance of the proposed modified IMM-PF algorithm was evaluated. The results demonstrated that the proposed approach can adapt to a changing accuracy of positioning information and significantly reduce the tracking errors by approximately 50% compared with traditional methods. Consequently, the proposed method can effectively enhance the security and reliability of autonomous driver assistance systems, and its application has potential to support urban planning for smart cities.