collaborators

5 papers

cs.CV2026

TwinIR: Coordinated Invisible Dual-Point Attacks on Online HD Map Construction

Haibo Hu, Jianghuai Deng, Chen Tang +3

Online HD map construction is critical to prediction and planning in autonomous driving. We find that existing physical attacks against online map construction are limited by a cro…

cs.CV2026

Robust 4D Driving Scene Reconstruction from Imperfect Visual Priors

Xiaoyun Dong, Qian Xu, Yun Wang +3

Reconstructing 4D driving scenes in the wild (e.g., internet and AI-generated videos) is critical for diverse autonomous driving simulation. While recent Gaussian Scene Graph (GSG)…

cs.RO2026

OmniSCS: Omni Safety-Critical Scenario Synthesis for Autonomous Driving via a Fully Editable Driving World

Xiaoyun Dong, Qian Xu, Yang Lu +3

The synthesis of safety-critical scenarios (SCS) and their evaluation through closed-loop simulations are crucial for developing robust autonomous driving systems. A key aspect of…

cs.CV2025

SATMapTR: Satellite Image Enhanced Online HD Map Construction

Bingyuan Huang, Guanyi Zhao, Qian Xu +3

High-definition (HD) maps are evolving from pre-annotated to real-time construction to better support autonomous driving in diverse scenarios. However, this process is hindered by…

cs.CR2025

Asymmetry Vulnerability and Physical Attacks on Online Map Construction for Autonomous Driving

Yang Lou, Haibo Hu, Qun Song +5

High-definition maps provide precise environmental information essential for prediction and planning in autonomous driving systems. Due to the high cost of labeling and maintenance…