MUSON: 都市環境における社会的規範に沿ったナビゲーションのための推論指向マルチモーダルデータセット
MUSON: A Reasoning-oriented Multimodal Dataset for Socially Compliant Navigation in Urban Environments
歩行者との社会的規範を考慮したナビゲーションのための、5段階の思考連鎖で注釈付けされた1万件超の一人称視点マルチモーダルデータセットを構築し、複数の視覚言語モデルを評価した。
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著者: Zhuonan Liu, Xinyu Zhang, Zishuo Wang, Runji Cai, Tomohito Kawabata, Qianyi Li, Xuance Peng, Tianze Yu, Zhen Xiong, Xuesu Xiao, Ling Xiao
分類: cs.CV, cs.RO
原文アブストラクト
Socially compliant navigation requires structured reasoning about dynamic pedestrians and physical constraints to ensure safe and interpretable decisions. Vision-language models (VLMs) provide a promising foundation for this task because they can integrate visual observations with language-based social knowledge. However, existing untuned VLMs still struggle to reliably understand fine-grained social norms, making task-specific fine-tuning essential. At the same time, no large-scale egocentric dataset is available this task. To address these challenges, we introduce MUSON, a multimodal dataset for short-horizon social navigation containing 10,110 egocentric samples collected across diverse indoor and outdoor social scenes. MUSON adopts a structured five-step chain-of-thought annotation framework comprising perception, prediction, reasoning, action, and explanation. It explicitly models static physical constraints and employs a standardized six-action decision space. Compared with existing social-navigation datasets, MUSON provides consistent annotations for reasoning, actions, and explanations. We evaluate ten representative small-to-medium VLMs on MUSON. Qwen3-VL-8B achieves the strongest decision-level performance, attaining the highest action accuracy of 0.7765 and Macro-F1 score of 0.7490, as well as the lowest collision rate of 0.0609. These results demonstrate that MUSON is an effective and reusable benchmark for advancing socially compliant navigation. The dataset is publicly available at https://github.com/MUSON-dataset/MUSON/releases/tag/v1.0.
