Multi-Vehicle Tracking Using Microscopic Traffic Models

In this paper, the multi-vehicle tracking problem is revisited, with greater consideration being given to the interactions between vehicles. Traditionally, algorithms for tracking multiple vehicles in the multi-lane case assume that vehicles move independently of one another and that longitudinal and lateral vehicle dynamics are mutually independent. However, due to traffic volume, limited lane resources, and traffic heterogeneity, vehicles have to interact with neighboring vehicles for the purposes of maintaining a safe distance from the leading vehicle or improving their navigability by passing slower vehicles. To address the limitations in the literature, this paper proposes a novel multi-vehicle tracking algorithm that integrates the microscopic traffic models (MTM) for modeling interaction behaviors among vehicles in a 2-D road coordinate system. Due to the dependence between the longitudinal and later motions, their corresponding estimates are updated sequentially in a recursive manner. An adaptive deferred decision logic is proposed to improve the accuracy of lateral state estimates and thus improve overall performance. Simulation results show that the proposed MTM-based tracking algorithm can achieve better performance than a conventional multi-lane vehicle tracking algorithm with extension to multi-vehicle tracking, which does not consider interactions among vehicles but updates the longitudinal and lateral motion estimates independently.