2 papers
cs.CV2025
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
Zhaobin Mo, Yunlong Li, Xuan Di
Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in naturalistic datasets, current a…
cs.CV2024
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model
Yongjie Fu, Yunlong Li, Xuan Di
Autonomous driving training requires a diverse range of datasets encompassing various traffic conditions, weather scenarios, and road types. Traditional data augmentation methods o…