1 code implementation • CVPR 2022 • Qingzhao Zhang, Shengtuo Hu, Jiachen Sun, Qi Alfred Chen, Z. Morley Mao
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation.
1 code implementation • 17 Jun 2021 • Yulong Cao*, Ningfei Wang*, Chaowei Xiao*, Dawei Yang*, Jin Fang, Ruigang Yang, Qi Alfred Chen, Mingyan Liu, Bo Li
In this paper, we present the first study of security issues of MSF-based perception in AD systems.
1 code implementation • 27 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.
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.
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.
1 code implementation • ICCV 2023 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In this paper, we present a novel adversarial training method for trajectory prediction.
1 code implementation • 22 Sep 2023 • Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu, Jiachen Sun, Xumiao Zhang, Qi Alfred Chen, Z. Morley Mao
To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks.
no code implementations • 16 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.
no code implementations • 3 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.
no code implementations • 30 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.
no code implementations • 14 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.
no code implementations • 24 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.
no code implementations • 27 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.
no code implementations • 6 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.
no code implementations • 13 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.
no code implementations • 28 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.
no code implementations • 10 Mar 2022 • Junjie Shen, Ningfei Wang, Ziwen Wan, Yunpeng Luo, Takami Sato, Zhisheng Hu, Xinyang Zhang, Shengjian Guo, Zhenyu Zhong, Kang Li, Ziming Zhao, Chunming Qiao, Qi Alfred Chen
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
no code implementations • 27 May 2022 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.
no code implementations • 9 Mar 2023 • Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu
We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.
no code implementations • 19 Mar 2023 • Takami Sato, Yuki Hayakawa, Ryo Suzuki, Yohsuke Shiiki, Kentaro Yoshioka, Qi Alfred Chen
To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets.
no code implementations • ICCV 2023 • Ningfei Wang, Yunpeng Luo, Takami Sato, Kaidi Xu, Qi Alfred Chen
In this work, we conduct the first measurement study on whether and how effectively the existing designs can lead to system-level effects, especially for the STOP sign-evasion attacks due to their popularity and severity.
no code implementations • 30 Aug 2023 • Takami Sato, Justin Yue, Nanze Chen, Ningfei Wang, Qi Alfred Chen
Motivated by the finding, we construct a large-scale dataset, Natural Denoising Diffusion Attack (NDDA) dataset, to systematically evaluate the risk of the natural attack capability of diffusion models with state-of-the-art text-to-image diffusion models.
no code implementations • 15 Dec 2023 • Chen Ma, Ningfei Wang, Qi Alfred Chen, Chao Shen
Our evaluation results show that the system-level effects can be significantly improved, i. e., the vehicle crash rate of SlowTrack is around 95% on average while existing works only have around 30%.
no code implementations • 7 Jan 2024 • Takami Sato, Sri Hrushikesh Varma Bhupathiraju, Michael Clifford, Takeshi Sugawara, Qi Alfred Chen, Sara Rampazzi
We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras.