Search Results for author: Qi Alfred Chen

Found 16 papers, 5 papers with code

Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

no code implementations27 May 2022 Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu

Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems.

Adversarial Robustness Decision Making +1

Towards Driving-Oriented Metric for Lane Detection Models

1 code implementation CVPR 2022 Takami Sato, Qi Alfred Chen

After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods.

Autonomous Driving Lane Detection

Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

no code implementations13 Sep 2021 Won Park, Nan Li, Qi Alfred Chen, Z. Morley Mao

A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs.

3D Object Detection Autonomous Vehicles +1

On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving

no code implementations6 Jul 2021 Takami Sato, Qi Alfred Chen

We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios.

Autonomous Driving Lane Detection

End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

no code implementations27 Feb 2021 Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu

The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.

Autonomous Driving

On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks

no code implementations24 Nov 2020 Jiachen Sun, Karl Koenig, Yulong Cao, Qi Alfred Chen, Z. Morley Mao

Since adversarial training (AT) is believed as the most robust defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the 3D model's robustness under AT.

3D Point Cloud Classification Adversarial Robustness +3

On The Adversarial Robustness of 3D Point Cloud Classification

no code implementations28 Sep 2020 Jiachen Sun, Karl Koenig, Yulong Cao, Qi Alfred Chen, Zhuoqing Mao

Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT.

3D Point Cloud Classification Adversarial Robustness +3

Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

no code implementations14 Sep 2020 Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.

Lane Detection

Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures

no code implementations30 Jun 2020 Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao

In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks.

Autonomous Driving Self-Driving Cars

Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack

no code implementations3 Mar 2020 Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen

Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.

Adversarial Attack

Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking

1 code implementation ICLR 2020 Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chen, Zhenyu Zhong, Tao Wei

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.

Adversarial Attack Autonomous Driving +4

Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

no code implementations16 Jul 2019 Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, Z. Morley Mao

In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored.

Autonomous Driving BIG-bench Machine Learning +2

Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking

1 code implementation27 May 2019 Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Zhenyu Zhong, Tao Wei

Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.

Adversarial Attack Autonomous Driving +4

Cannot find the paper you are looking for? You can Submit a new open access paper.