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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…
WaymoQA: A Multi-View Visual Question Answering Dataset for Safety-Critical Reasoning in Autonomous Driving
Seungjun Yu, Seonho Lee, Namho Kim +5
Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their application to autonomous driving. Howe…
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…