8 papers
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…
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)…
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…
MMRHP: A Miniature Mixed-Reality HIL Platform for Auditable Closed-Loop Evaluation
Mingxin Li, Haibo Hu, Jinghuai Deng +3
Validation of autonomous driving systems requires a trade-off between test fidelity, cost, and scalability. While miniaturized hardware-in-the-loop (HIL) platforms have emerged as…
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…
VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving
Haibo Hu, Jiacheng Zuo, Yang Lou +6
With the widespread adoption and deployment of autonomous driving, handling complex environments has become an unavoidable challenge. Due to the scarcity and diversity of extreme s…