5 citations · 10 across the 7 of their papers we have counts for
7 papers
Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective
Ningfei Wang, Shaoyuan Xie, Takami Sato +3
Traffic Sign Recognition (TSR) is crucial for safe and correct driving automation. Recent works revealed a general vulnerability of TSR models to physical-world adversarial attacks…
Towards Automated Driving Violation Cause Analysis in Scenario-Based Testing for Autonomous Driving Systems
Ziwen Wan, Yuqi Huai, Yuntianyi Chen +2
The rapid advancement of Autonomous Vehicles (AVs), exemplified by companies like Waymo and Cruise offering 24/7 paid taxi services, highlights the paramount importance of ensuring…
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…
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
Ningfei Wang, Yunpeng Luo, Takami Sato +2
In autonomous driving (AD), accurate perception is indispensable to achieving safe and secure driving. Due to its safety-criticality, the security of AD perception has been widely…
Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection
Junjie Shen, Yunpeng Luo, Ziwen Wan +1
Localization in high-level Autonomous Driving (AD) systems is highly security critical. While the popular Multi-Sensor Fusion (MSF) based design can be more robust against single-s…
Learning Representation for Anomaly Detection of Vehicle Trajectories
Ruochen Jiao, Juyang Bai, Xiangguo Liu +4
Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations a…