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Found 3 papers, 2 papers with code
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving
The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving.
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks
Finally, a printed physical billboard containing an adversarial patch was tested in an outdoor driving scenario to assess the feasibility of the studied attacks in the real world.
Falsification-Based Robust Adversarial Reinforcement Learning
Robust adversarial RL (RARL) was previously proposed to train an adversarial network that applies disturbances to a system, which improves the robustness in test scenarios.
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