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Found 15 papers, 0 papers with code
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions.
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.
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.
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.
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.
Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles
Thus, in this work, we perform an analysis of camera-LiDAR fusion, in the AV context, under LiDAR spoofing attacks.
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training.
Robust Trajectory Prediction against Adversarial Attacks
We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data.
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors.
Language-Guided Traffic Simulation via Scene-Level Diffusion
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
This works aims to address this by developing a framework that employs reinforcement learning with human preference (RLHF) to enhance the realism of existing traffic models.
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency
Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions.
Dolphins: Multimodal Language Model for Driving
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness.
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing.
WIPI: A New Web Threat for LLM-Driven Web Agents
To evaluate the effectiveness of the proposed methodology, we conducted extensive experiments using 7 plugin-based ChatGPT Web Agents, 8 Web GPTs, and 3 different open-source Web Agents.
