5 papers
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
Lingdong Kong, Shaoyuan Xie, Zeying Gong +135
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…
SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving
Wonjeong Ryu, Seungjun Yu, Seokha Moon +4
End-to-End (E2E) planning has become a powerful paradigm for autonomous driving, yet current systems remain fundamentally uncertainty-blind. They assume perception outputs are full…
Robust Driving QA through Metadata-Grounded Context and Task-Specific Prompts
Seungjun Yu, Junsung Park, Youngsun Lim +1
We present a two-phase vision-language QA system for autonomous driving that answers high-level perception, prediction, and planning questions. In Phase-1, a large multimodal LLM (…
3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation
Seonho Lee, Jiho Choi, Inha Kang +3
Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain fundamentally limited in their understanding of 3D spatial s…
No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather
Junsung Park, Hwijeong Lee, Inha Kang +1
Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In t…