10 papers
PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving
Kang Ding, Zhigui Lin, Hongsong Wang +5
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene t…
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
Qi Liu, Yabei Li, Hongsong Wang +2
Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-L…
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2
Traffic sign detection is a fundamental component of environmental perception in autonomous driving and intelligent transportation systems. However, most existing detectors rely on…
HQ-OV3D: A High Box Quality Open-World 3D Detection Framework based on Diffision Model
Qi Liu, Yabei Li, Hongsong Wang +1
Traditional closed-set 3D detection frameworks fail to meet the demands of open-world applications like autonomous driving. Existing open-vocabulary 3D detection methods typically…
FMCE-Net++: Feature Map Convergence Evaluation and Training
Zhibo Zhu, Renyu Huang, Lei He
Deep Neural Networks (DNNs) face interpretability challenges due to their opaque internal representations. While Feature Map Convergence Evaluation (FMCE) quantifies module-level c…
VLM-3D:End-to-End Vision-Language Models for Open-World 3D Perception
Fuhao Chang, Shuxin Li, Yabei Li +1
Open-set perception in complex traffic environments poses a critical challenge for autonomous driving systems, particularly in identifying previously unseen object categories, whic…