49 citations · 54 across the 17 of their papers we have counts for
17 papers
Adversarial Calibration Attack on Autonomous Vehicles
Liangkai Liu, Qingzhao Zhang, Kang G. Shin
Autonomous vehicles (AVs) rely on accurate camera-LiDAR calibration for multimodal sensor fusion. In practice, calibration can drift due to vibration, temperature variation, or min…
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Qingzhao Zhang
Traffic forecasting by graph-based AI is a critical component of intelligent transportation systems, motivating security research on robustness to malicious sensor readings. We arg…
Banshee: Target Switch Attacks on Gimbal-Stabilized Visual Tracking Systems via Acoustic Injection
Jiarui Li, Joseph Brewington, Qingzhao Zhang +1
Gimbal-stabilized visual tracking is critical for modern autonomous systems such as Unmanned Aerial Vehicles (UAVs). While prior work shows acoustic signals can disturb gimbal inte…
CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving
Ruiyang Zhu, Yuehan He, Boyuan Zheng +4
End-to-end autonomous driving systems powered by Vision-Language-Action (VLA) models achieve strong performance on common driving scenarios, yet remain brittle in rare but safety-c…
Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving
Shuo Ju, Qingzhao Zhang, Huashan Chen +6
Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or trajectory prediction, through…
From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception
Qingzhao Zhang, Runting Zhang, Z. Morley Mao
Collaborative perception allows connected and autonomous vehicles (CAVs) to improve perception by sharing sensory data, but it also introduces security risks from manipulated input…
Adversarial Trust Poisoning in Vehicular Collaborative Perception
Yutong Liu, Chenyi Wang, Ming F. Li +1
Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To defend against adversaries that fabricat…
SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
Qingzhao Zhang, Shaocheng Luo, Z. Morley Mao +2
Autonomous vehicles, including self-driving cars, ground robots, and drones, rely on multi-modal sensor pipelines for safe operation, yet remain vulnerable to adversarial sensor at…
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion
Minkyoung Cho, Yulong Cao, Jiachen Sun +5
An important paradigm in 3D object detection is the use of multiple modalities to enhance accuracy in both normal and challenging conditions, particularly for long-tail scenarios.…
Compute Or Load KV Cache? Why Not Both?
Shuowei Jin, Xueshen Liu, Qingzhao Zhang +1
Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-va…
LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks
Qingzhao Zhang, Ziyang Xiong, Z. Morley Mao
Safety is a paramount concern for large language models (LLMs) in open deployment, motivating the development of safeguard methods that enforce ethical and responsible use through…
Plato: Plan to Efficiently Decode for Large Language Model Inference
Shuowei Jin, Xueshen Liu, Yongji Wu +7
Large language models (LLMs) have achieved remarkable success in natural language tasks, but their inference incurs substantial computational and memory overhead. To improve effici…
On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures
Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu +4
Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth…
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Jiachen Sun, Haizhong Zheng, Qingzhao Zhang +3
Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirabili…
What Would Trojans Do? Exploiting Partial-Information Vulnerabilities in Autonomous Vehicle Sensing
R. Spencer Hallyburton, Qingzhao Zhang, Z. Morley Mao +2
Safety-critical sensors in autonomous vehicles (AVs) form an essential part of the vehicle's trusted computing base (TCB), yet they are highly susceptible to attacks. Alarmingly, T…
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura +3
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is le…
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
Qingzhao Zhang, Shengtuo Hu, Jiachen Sun +2
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness o…