A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction

arXiv:2309.15284v2 Announce Type: replace
Abstract: In vehicle trajectory prediction, physics models and data-driven models are two predominant methodologies. However, each approach presents its own set of challenges: physics models fall short in predictability, while data-driven models lack interpretability. Addressing these identified shortcomings, this paper proposes a novel framework, the Physics-Enhanced Residual Learning (PERL) model. PERL integrates the strengths of physics-based and data-driven methods for traffic state prediction. PERL contains a physics model and a residual learning model. Its prediction is …

abstract arxiv challenges cs.lg data data-driven framework however interpretability novel paper perl physics prediction residual set trajectory type