1 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
diffIRM: A Diffusion-Augmented Invariant Risk Minimization Framework for Spatiotemporal Prediction over Graphs
Zhaobin Mo, Haotian Xiang, Xuan Di
Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow diffe…
Can LLMs Understand Social Norms in Autonomous Driving Games?
Boxuan Wang, Haonan Duan, Yanhao Feng +4
Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which…
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…