Multi-Vehicle Tracking Using an Environment Interaction Potential Force Model

On-road vehicle tracking is crucial for the development of automated vehicles and advanced driver assistance systems. Most existing tracking algorithms assume that the vehicles move independently without considering the interactions between surrounding vehicles. However, in order to keep a safe distance and avoid collisions, the motion behavior of vehicles should be affected by the surrounding vehicles. To address this limitation, this paper presents a novel vehicle tracking method that comprehensively considers the motion dependence interactions between vehicles. In this paper, the environment interaction models of lateral and longitudinal are constructed and incorporated into the estimation process that model the interaction behaviors in a 2-D road coordinate system. The problem of motion dependence estimation is handled by an interacting multiple model, which operates different dynamic models in parallel. Furthermore, to adapt the switching between the lateral maneuvering models, a Quasi-Bayesian recursion algorithm with adaptive transition probability is proposed. This adaptive switching strategy improves the accuracy of lateral motion behavior estimation and thus improves the tracking performance. The proposed algorithm is evaluated by simulation, and its performance is quantified by the posterior Cramer-Rao lower bound. The simulation results show that the presented algorithm provides higher accuracy and reliability after considering the environment interaction.