7 papers
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…
Decoupled Functional Evaluation of Autonomous Driving Models via Feature Map Quality Scoring
Ludan Zhang, Sihan Wang, Yuqi Dai +3
End-to-end models are emerging as the mainstream in autonomous driving perception and planning. However, the lack of explicit supervision signals for intermediate functional module…
CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
Qi Xiang, Kunsong Shi, Zhigui Lin +1
Bird's Eye View (BEV) perception systems based on multi-sensor feature fusion have become a fundamental cornerstone for end-to-end autonomous driving. However, existing multi-modal…
ME-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Siyi Lu, Run Liu, Dongsheng Yang +1
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretab…