Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability
in Autonomous Driving Planning under Physical-World Attacks

Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks

12 January 2022

Jiaqi Ma

Qi Alfred Chen

Papers citing "Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks"

5 / 5 papers shown

Title
A First Physical-World Trajectory Prediction Attack via LiDAR-induced Deceptions in Autonomous Driving Yang Lou Yi Zhu Qun Song Rui Tan Chunming Qiao Wei-Bin Lee Jianping Wang AAML 44 2 0 17 Jun 2024
Lateral-Direction Localization Attack in High-Level Autonomous Driving: Domain-Specific Defense Opportunity via Lane Detection Junjie Shen Y. Luo Ziwen Wan Qi Alfred Chen AAML 11 3 0 26 Jul 2023
DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing Seulbae Kim Major Liu J. Rhee Yuseok Jeon Yonghwi Kwon C. Kim 24 42 0 25 Oct 2022
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World Daniel J. Fremont Edward Kim Yash Vardhan Pant S. Seshia Atul Acharya Xantha Bruso Paul Wells Steve Lemke Q. Lu Shalin Mehta 76 123 0 17 Mar 2020
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector Shang-Tse Chen Cory Cornelius Jason Martin Duen Horng Chau ObjD 148 424 0 16 Apr 2018