468 citations · 922 across the 20 of their papers we have counts for
21 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…
LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies
Takami Sato, Yuki Hayakawa, Ryo Suzuki +3
LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous drivi…
Towards Driving-Oriented Metric for Lane Detection Models
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 det…
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…
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…
scenoRITA: Generating Less-Redundant, Safety-Critical and Motion Sickness-Inducing Scenarios for Autonomous Vehicles
Sumaya Almanee, Xiafa Wu, Yuqi Huai +2
There is tremendous global enthusiasm for research, development, and deployment of autonomous vehicles (AVs), e.g., self-driving taxis and trucks from Waymo and Baidu. The current…
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
Won Park, Nan Liu, Qi Alfred Chen +1
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…
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving
Takami Sato, Qi Alfred Chen
After the 2017 TuSimple Lane Detection Challenge, its evaluation based on accuracy and F1 score has become the de facto standard to measure the performance of lane detection method…
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks
Yulong Cao*, Ningfei Wang*, Chaowei Xiao* +6
In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on came…
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…
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks
Jiachen Sun, Karl Koenig, Yulong Cao +2
3D point clouds play pivotal roles in various safety-critical applications, such as autonomous driving, which desires the underlying deep neural networks to be robust to adversaria…
Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control
Shihong Ed Huang, Wai Wong, Yiheng Feng +3
Connected vehicle (CV) technology enables data exchange between vehicles and transportation infrastructure and therefore has great potentials to improve current traffic signal cont…
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…
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
Jiachen Sun, Yulong Cao, Qi Alfred Chen +1
Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent s…
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…
Threat Detection and Investigation with System-level Provenance Graphs: A Survey
Zhenyuan Li, Qi Alfred Chen, Runqing Yang +1
With the development of information technology, the border of the cyberspace gets much broader, exposing more and more vulnerabilities to attackers. Traditional mitigation-based de…
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…
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
Yulong Cao, Chaowei Xiao, Benjamin Cyr +6
In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environm…
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…
Towards Secure and Safe Appified Automated Vehicles
Yunhan Jack Jia, Ding Zhao, Qi Alfred Chen +1
The advancement in Autonomous Vehicles (AVs) has created an enormous market for the development of self-driving functionalities,raising the question of how it will transform the tr…