no code implementations • 7 Jun 2022 • Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu, Sang Uk Lee, Momchil S. Tomov, Nanxiang Li, Caglayan Dicle, Samuel Findler, Francisco Suarez-Ruiz, Robert Beaudoin, Bo Yang, Sammy Omari, Eric M. Wolff
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL).
Trajectory prediction is an important task in autonomous driving.
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.