Deep learning-based interaction-aware trajectory prediction for autonomous vehicles

Abstract

xv strong scene adaptability. Besides, the algorithms developed based on the proposed HGS pooling technique and the HEAT network won the championships of two worldwide autonomous vehicle prediction challenges, respectively. These outcomes demonstrate the feasibility and e↵ectiveness of the proposed methods. In addition, the high-level algorithm architectures, methodologies employed, and models developed in this work will expand the current theories of autonomous driving and intelligent transportation systems. They can also be expanded to a wide range of robotics and automation applications.