no code implementations • 30 Oct 2024 • Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, Yin Zhou, James Guo, Dragomir Anguelov, Mingxing Tan
We show that co-training EMMA with planner trajectories, object detection, and road graph tasks yields improvements across all three domains, highlighting EMMA's potential as a generalist model for autonomous driving applications.
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 • 21 Dec 2022 • Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine
To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.
no code implementations • 16 Dec 2022 • Wenjie Luo, Cheolho Park, Andre Cornman, Benjamin Sapp, Dragomir Anguelov
We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories.
3 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
no code implementations • 8 Jun 2022 • DiJia Su, Bertrand Douillard, Rami Al-Rfou, Cheolho Park, Benjamin Sapp
These models are intrinsically invariant to translation and rotation between scene elements, are best-performing on public leaderboards, but scale quadratically with the number of agents and scene elements.
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
4 code implementations • 19 Aug 2020 • Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, Cong-Cong Li, Dragomir Anguelov
Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
1 code implementation • 12 Oct 2019 • Yuning Chai, Benjamin Sapp, Mayank Bansal, Dragomir Anguelov
Predicting human behavior is a difficult and crucial task required for motion planning.
Ranked #2 on
Trajectory Prediction
on PAID
no code implementations • CVPR 2019 • Joey Hong, Benjamin Sapp, James Philbin
We focus on the problem of predicting future states of entities in complex, real-world driving scenarios.
1 code implementation • 20 Nov 2015 • Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei
Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.
Ranked #6 on
Fine-Grained Image Classification
on CUB-200-2011
(using extra training data)
no code implementations • NeurIPS 2010 • David Weiss, Benjamin Sapp, Ben Taskar
For many structured prediction problems, complex models often require adopting approximate inference techniques such as variational methods or sampling, which generally provide no satisfactory accuracy guarantees.