Interaction-Aware Motion Prediction for Autonomous Driving: A Multiple Model Kalman Filtering Scheme

• Published in: IEEE robotics and automation letters Vol. 6; no. 1; pp. 80 - 87
• Main Authors: Lefkopoulos, Vasileios, Menner, Marcel, Domahidi, Alexander, Zeilinger, Melanie N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Adaptation models; Autonomous cars; Computational modeling; Driving; Estimation; Forecasting; intelligent transportation systems; Kalman filters; Mathematical models; Motion and path planning; Motion planning; Optimization; Predictive models; probability and statistical methods; Sorting algorithms; Vehicles
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.3032079

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.