Search Results for author: James Tu

Found 9 papers, 1 papers with code

Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving

no code implementations17 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.

Adversarial Robustness Denoising

Adversarial Attacks On Multi-Agent Communication

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.

Domain Adaptation

AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

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.

Diverse Complexity Measures for Dataset Curation in Self-driving

no code implementations16 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.

Active Learning Motion Forecasting

StrObe: Streaming Object Detection from LiDAR Packets

no code implementations12 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.

object-detection Object Detection

Learning to Communicate and Correct Pose Errors

no code implementations10 Nov 2020 Nicholas Vadivelu, Mengye Ren, James Tu, Jingkang Wang, Raquel Urtasun

Learned communication makes multi-agent systems more effective by aggregating distributed information.

Motion Forecasting object-detection +1

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

1 code implementation 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.

3D Object Detection Motion Forecasting

Physically Realizable Adversarial Examples for LiDAR Object Detection

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.

Adversarial Defense Autonomous Driving +3

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