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Found 7 papers, 1 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 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.
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.
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.
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.
