149 citations · 153 across the 10 of their papers we have counts for
10 papers
DTP-Attack: A decision-based black-box adversarial attack on trajectory prediction
Jiaxiang Li, Jun Yan, Daniel Watzenig +1
Trajectory prediction systems are critical for autonomous vehicle safety, yet remain vulnerable to adversarial attacks that can cause catastrophic traffic behavior misinterpretatio…
Automotive-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems
Junfeng Yan, Biao Wu, Meng Fang +1
Multimodal agents have demonstrated strong performance in general GUI interactions, but their application in automotive systems has been largely unexplored. In-vehicle GUIs present…
Adversarial Examples in Environment Perception for Automated Driving (Review)
Jun Yan, Huilin Yin
The renaissance of deep learning has led to the massive development of automated driving. However, deep neural networks are vulnerable to adversarial examples. The perturbations of…
WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer
Huilin Yin, Pengyu Wang, Senmao Li +2
Robust object detection for Unmanned Surface Vehicles (USVs) in complex water environments is essential for reliable navigation and operation. Specifically, water surface object de…
Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
Jun Yan, Pengyu Wang, Danni Wang +3
Semantic segmentation is a significant perception task in autonomous driving. It suffers from the risks of adversarial examples. In the past few years, deep learning has gradually…
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory prediction
Huilin Yin, Jiaxiang Li, Pengju Zhen +1
Trajectory prediction is critical for the safe planning and navigation of automated vehicles. The trajectory prediction models based on the neural networks are vulnerable to advers…
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network
Huan Hua, Jun Yan, Xi Fang +3
The information bottleneck (IB) method is a feasible defense solution against adversarial attacks in deep learning. However, this method suffers from the spurious correlation, whic…
Wavelet Regularization Benefits Adversarial Training
Jun Yan, Huilin Yin, Xiaoyang Deng +4
Adversarial training methods are state-of-the-art (SOTA) empirical defense methods against adversarial examples. Many regularization methods have been proven to be effective with t…
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
Wei Zhou, Dong Chen, Jun Yan +3
Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving an…
On Procedural Adversarial Noise Attack And Defense
Jun Yan, Xiaoyang Deng, Huilin Yin +1
Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images. Rese…