no code implementations • 2 Nov 2023 • Jay Sarva, Jingkang Wang, James Tu, Yuwen Xiong, Sivabalan Manivasagam, Raquel Urtasun
In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers.
no code implementations • 2 Nov 2023 • Chris Zhang, James Tu, Lunjun Zhang, Kelvin Wong, Simon Suo, Raquel Urtasun
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
no code implementations • CVPR 2023 • Simon Suo, Kelvin Wong, Justin Xu, James Tu, Alexander Cui, Sergio Casas, Raquel Urtasun
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
no code implementations • 24 Mar 2021 • Alex Chang, Vinith Suriyakumar, Abhishek Moturu, James Tu, Nipaporn Tewattanarat, Sayali Joshi, Andrea Doria, Anna Goldenberg
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities.
no code implementations • ICCV 2021 • James Tu, TsunHsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
no code implementations • 17 Jan 2021 • James Tu, Huichen Li, Xinchen Yan, Mengye Ren, Yun Chen, Ming Liang, Eilyan Bitar, Ersin Yumer, Raquel Urtasun
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
no code implementations • 16 Jan 2021 • Abbas Sadat, Sean Segal, Sergio Casas, James Tu, Bin Yang, Raquel Urtasun, Ersin Yumer
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
no code implementations • CVPR 2021 • Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
no code implementations • 12 Nov 2020 • Davi Frossard, Simon Suo, Sergio Casas, James Tu, Rui Hu, Raquel Urtasun
In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
no code implementations • 10 Nov 2020 • Nicholas Vadivelu, Mengye Ren, James Tu, Jingkang Wang, Raquel Urtasun
Learned communication makes multi-agent systems more effective by aggregating distributed information.
3 code implementations • ECCV 2020 • Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, James Tu, Raquel Urtasun
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on 3D Object Detection on OPV2V
no code implementations • CVPR 2020 • James Tu, Mengye Ren, Siva Manivasagam, Ming Liang, Bin Yang, Richard Du, Frank Cheng, Raquel Urtasun
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations.