no code implementations • 29 Apr 2021 • Harshayu Girase, Jerrick Hoang, Sai Yalamanchi, Micol Marchetti-Bowick
Predicting the future motion of actors in a traffic scene is a crucial part of any autonomous driving system.
no code implementations • 27 Sep 2020 • Sumit Kumar, Yiming Gu, Jerrick Hoang, Galen Clark Haynes, Micol Marchetti-Bowick
Behavior prediction of traffic actors is an essential component of any real-world self-driving system.
no code implementations • 9 Sep 2020 • Lingyao Zhang, Po-Hsun Su, Jerrick Hoang, Galen Clark Haynes, Micol Marchetti-Bowick
We present a new method for multi-modal, long-term vehicle trajectory prediction.
no code implementations • 17 Dec 2019 • Donsuk Lee, Yiming Gu, Jerrick Hoang, Micol Marchetti-Bowick
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions.
2 code implementations • 3 Jul 2019 • Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.