Interaction-Aware Motion Prediction for Autonomous Driving: A Multiple Model Kalman Filtering Scheme
Vasileios Lefkopoulos, Marcel Menner, Alexander Domahidi, Melanie N. Zeilinger
Abstract
We consider the problem of predicting the motion of vehicles in the surrounding of an autonomous car, for improved motion planning in lane-based driving scenarios without inter-vehicle communication. First, we address the problem of single-vehicle estimation by designing a filtering scheme based on an Interacting Multiple Model Kalman Filter equipped with novel intention-based models. Second, we augment the proposed scheme with an optimization-based projection that enables the generation of non-colliding predictions. We then extend the approach to the problem of simultaneously estimating multiple vehicles by using a hierarchical approach based on a priority list. The priority list is dynamically adapted in real-time according to a proposed sorting algorithm. Finally, we evaluate the proposed scheme in simulations using real-life vehicle data from the Next Generation Simulation (NGSIM) dataset.