Wild-Drive: 頑健なマルチモーダルルーティングと効率的な大規模言語モデルによるオフロードシーン記述と経路計画
Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model
オフロード環境でのシーン記述と経路計画を統合するフレームワークWild-Driveを提案。センサ劣化時に信頼できる情報を適応的に集約するMoRo-Formerと効率的なLLMを用い、劣化条件下でも安定した性能を実現。
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著者: Zihang Wang, Xu Li, Benwu Wang, Wenkai Zhu, Xieyuanli Chen, Dong Kong, Kailin Lyu, Yinan Du, Yiming Peng, Haoyang Che
分類: cs.RO, cs.AI
原文アブストラクト
Explainability and transparent decision-making are essential for the safe deployment of autonomous driving systems. Scene captioning summarizes environmental conditions and risk factors in natural language, improving transparency, safety, and human--robot interaction. However, most existing approaches target structured urban scenarios; in off-road environments, they are vulnerable to single-modality degradations caused by rain, fog, snow, and darkness, and they lack a unified framework that jointly models structured scene captioning and path planning. To bridge this gap, we propose Wild-Drive, an efficient framework for off-road scene captioning and path planning. Wild-Drive adopts modern multimodal encoders and introduces a task-conditioned modality-routing bridge, MoRo-Former, to adaptively aggregate reliable information under degraded sensing. It then integrates an efficient large language model (LLM), together with a planning token and a gate recurrent unit (GRU) decoder, to generate structured captions and predict future trajectories. We also build the OR-C2P Benchmark, which covers structured off-road scene captioning and path planning under diverse sensor corruption conditions. Experiments on OR-C2P dataset and a self-collected dataset show that Wild-Drive outperforms prior LLM-based methods and remains more stable under degraded sensing. The code and benchmark will be publicly available at https://github.com/wangzihanggg/Wild-Drive.
