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Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering
We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving
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.
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation
Simulation data can be utilized to extend real-world driving data in order to cover edge cases, such as vehicle accidents.
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