24 citations · 80 across the 8 of their papers we have counts for
8 papers
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…
SoK: On the Semantic AI Security in Autonomous Driving
Junjie Shen, Ningfei Wang, Ziwen Wan +10
Autonomous Driving (AD) systems rely on AI components to make safety and correct driving decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to adv…
Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks
Ziwen Wan, Junjie Shen, Jalen Chuang +4
In high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making high-level driving decisions such as cruising and stopping, and thus highly securitycritic…
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
Ruochen Jiao, Hengyi Liang, Takami Sato +3
In the development of advanced driver-assistance systems (ADAS) and autonomous vehicles, machine learning techniques that are based on deep neural networks (DNNs) have been widely…
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
Takami Sato, Junjie Shen, Ningfei Wang +3
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical. In this work, we are the first to systematically stud…
Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)
Junjie Shen, Jun Yeon Won, Zeyuan Chen +1
For high-level Autonomous Vehicles (AV), localization is highly security and safety critical. One direct threat to it is GPS spoofing, but fortunately, AV systems today predominant…
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Takami Sato, Junjie Shen, Ningfei Wang +3
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical. In this work, we design and implement the first sys…
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Yunhan Jia, Yantao Lu, Junjie Shen +3
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. Howe…