1 code implementation • ICCV 2023 • Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain.
no code implementations • 1 Jun 2023 • Jiachen Li, Xinwei Shi, Feiyu Chen, Jonathan Stroud, Zhishuai Zhang, Tian Lan, Junhua Mao, Jeonhyung Kang, Khaled S. Refaat, Weilong Yang, Eugene Ie, CongCong Li
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
4 code implementations • 12 Jul 2022 • Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous.
Ranked #6 on
Motion Forecasting
on Argoverse CVPR 2020
1 code implementation • 7 Jul 2022 • Rebecca Roelofs, Liting Sun, Ben Caine, Khaled S. Refaat, Ben Sapp, Scott Ettinger, Wei Chai
Finally, we release the causal agent labels (at https://github. com/google-research/causal-agents) as an additional attribute to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting.
2 code implementations • 29 Nov 2021 • Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Khaled S. Refaat, Nigamaa Nayakanti, Andre Cornman, Kan Chen, Bertrand Douillard, Chi Pang Lam, Dragomir Anguelov, Benjamin Sapp
Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving.
Ranked #16 on
Motion Forecasting
on Argoverse CVPR 2020
no code implementations • 19 Sep 2019 • Khaled S. Refaat, Kai Ding, Natalia Ponomareva, Stéphane Ross
We propose a system to rank agents around an autonomous vehicle (AV) in real time.
no code implementations • NeurIPS 2014 • Khaled S. Refaat, Arthur Choi, Adnan Darwiche
We propose a technique for decomposing the parameter learning problem in Bayesian networks into independent learning problems.
no code implementations • NeurIPS 2013 • Khaled S. Refaat, Arthur Choi, Adnan Darwiche
Second, it facilitates the design of EDML algorithms for new graphical models, leading to a new algorithm for learning parameters in Markov networks.