6 papers
CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
Jiacheng Tang, Zhiyuan Zhou, Zhuolin He +3
Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads…
Composing Driving Worlds through Disentangled Control for Adversarial Scenario Generation
Yifan Zhan, Zhengqing Chen, Qingjie Wang +7
A major challenge in autonomous driving is the "long tail" of safety-critical edge cases, which often emerge from unusual combinations of common traffic elements. Synthesizing thes…
DynamicVGGT: Learning Dynamic Point Maps for 4D Scene Reconstruction in Autonomous Driving
Zhuolin He, Jing Li, Guanghao Li +9
Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed…
Vision-Language Feature Alignment for Road Anomaly Segmentation
Zhuolin He, Jiacheng Tang, Jian Pu +1
Safe autonomous systems in complex environments require robust road anomaly segmentation to identify unknown obstacles. However, existing approaches often rely on pixel-level stati…
Multi-modality Anomaly Segmentation on the Road
Heng Gao, Zhuolin He, Shoumeng Qiu +2
Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles th…
Detecting OOD Samples via Optimal Transport Scoring Function
Heng Gao, Zhuolin He, Jian Pu
To deploy machine learning models in the real world, researchers have proposed many OOD detection algorithms to help models identify unknown samples during the inference phase and…