Search Results for author:
Found 16 papers, 5 papers with code
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems.
Towards Driving-Oriented Metric for Lane Detection Models
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 detection methods.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation.
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
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 which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs.
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving
We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios.
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks
In this paper, we present the first study of security issues of MSF-based perception in AD systems.
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks
Since adversarial training (AT) is believed as the most robust defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the 3D model's robustness under AT.
On The Adversarial Robustness of 3D Point Cloud Classification
Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT.
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks.
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
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.
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored.
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
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.
