activity
20242026
collaborators

13 papers

cs.CV2026

Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks

Yuanhao Huang, Yilong Ren, Jinlei Wang +3

AI-enabled visual perception systems are increasingly deployed in intelligent transportation infrastructure and autonomous vehicle related applications. However, physically realiza…

cs.CV2026

AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression

Yuanhao Huang, Yilong Ren, Jinlei Wang +3

AI-based visual perception systems are increasingly deployed in infrastructure surveillance, including roadside monitoring units, highway cameras, and smart-city pedestrian managem…

cs.CV2025

SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration

Zhiyuan Liu, Daocheng Fu, Pinlong Cai +5

High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously ach…

cs.CV2025

AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction

Ruikai Li, Xinrun Li, Mengwei Xie +12

Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a cr…

cs.CV2025

Stability Under Scrutiny: Benchmarking Representation Paradigms for Online HD Mapping

Hao Shan, Ruikai Li, Han Jiang +8

As one of the fundamental modules in autonomous driving, online high-definition (HD) maps have attracted significant attention due to their cost-effectiveness and real-time capabil…

cs.CV2025

AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems

Yuanhao Huang, Yilong Ren, Jinlei Wang +4

Autonomous vehicles are typical complex intelligent systems with artificial intelligence at their core. However, perception methods based on deep learning are extremely vulnerable…