LAUSR

Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.