Search Results for author: Takami Sato

Found 7 papers, 1 papers with code

Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

no code implementations27 May 2022 Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu

Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems.

Adversarial Robustness Decision Making +1

Towards Driving-Oriented Metric for Lane Detection Models

1 code implementation CVPR 2022 Takami Sato, Qi Alfred Chen

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.

Autonomous Driving Lane Detection

On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving

no code implementations6 Jul 2021 Takami Sato, Qi Alfred Chen

We demonstrate that the conventional evaluation fails to reflect the robustness in end-to-end autonomous driving scenarios.

Autonomous Driving Lane Detection

End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering

no code implementations27 Feb 2021 Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu

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.

Autonomous Driving

Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

no code implementations14 Sep 2020 Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.

Lane Detection

Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack

no code implementations3 Mar 2020 Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jack Jia, Xue Lin, Qi Alfred Chen

Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.

Adversarial Attack

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