5 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…
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…
Towards Camera Open-set 3D Object Detection for Autonomous Driving Scenarios
Zhuolin He, Xinrun Li, Jiacheng Tang +4
Conventional camera-based 3D object detectors in autonomous driving are limited to recognizing a predefined set of objects, which poses a safety risk when encountering novel or uns…
Decoupling Scene Perception and Ego Status: A Multi-Context Fusion Approach for Enhanced Generalization in End-to-End Autonomous Driving
Jiacheng Tang, Mingyue Feng, Jiachao Liu +2
Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego statu…