Search Results for author: Laura Zheng

Found 4 papers, 0 papers with code

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering

no code implementations NeurIPS 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.

Autonomous Driving Self-Driving Cars

Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

no code implementations15 Mar 2021 Shivam Akhauri, Laura Zheng, Tom Goldstein, Ming Lin

Practical learning-based autonomous driving models must be capable of generalizing learned behaviors from simulated to real domains, and from training data to unseen domains with unusual image properties.

Autonomous Driving Data Augmentation +2

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images

no code implementations26 Feb 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +1

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

no code implementations23 Jul 2020 Shivam Akhauri, Laura Zheng, Ming Lin

Simulation data can be utilized to extend real-world driving data in order to cover edge cases, such as vehicle accidents.

Autonomous Driving Transfer Learning

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