Multi-Object Trajectory Prediction with Crossing Detection Using Interacting Multiple Model Kalman Filter

This research proposes an approach to trajectory prediction using the Interacting Multiple Model Kalman Filter (IMM-KF), which forecasts the future positions of multiple sur rounding objects and determines their crossing status - whether they will cross or stay on their paths. The method accommodates diverse object behaviors by integrating constant velocity and constant acceleration models. Building on previous work, this approach enhances the accuracy of trajectory prediction for maneuvering objects, enhancing decision-making capabilities for autonomous trams to improve safety. The proposed method, which integrates a trajectory motion model with the IMM-KF algorithm, has been validated through both simulations and real-world testing on a vehicle platform. The results demonstrate significant improvements in safety and risk assessment.