3 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Preserving Spectral Structure and Statistics in Diffusion Models
Baohua Yan, Jennifer Kava, Qingyuan Liu +1
Standard diffusion models (DMs) rely on the total destruction of data into non-informative white noise, forcing the backward process to denoise from a fully unstructured noise stat…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
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…
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
Yongjie Fu, Anmol Jain, Xuan Di +2
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VL…
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…