VLMPlanner: 視覚言語モデルと運動計画の統合
VLMPlanner: Integrating Visual Language Models with Motion Planning
視覚言語モデル(VLM)を自動運転の運動計画に組み込み、生画像から得た文脈情報でリアルタイムプランナを誘導するハイブリッド手法を提案。シーン複雑度に応じて推論頻度を調整する機構も導入した。
詳しい要約
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著者: Zhipeng Tang, Sha Zhang, Jiajun Deng, Chenjie Wang, Guoliang You, Yuting Huang, Xinrui Lin, Yanyong Zhang
分類: cs.AI, cs.RO
原文アブストラクト
Integrating large language models (LLMs) into autonomous driving motion planning has recently emerged as a promising direction, offering enhanced interpretability, better controllability, and improved generalization in rare and long-tail scenarios. However, existing methods often rely on abstracted perception or map-based inputs, missing crucial visual context, such as fine-grained road cues, accident aftermath, or unexpected obstacles, which are essential for robust decision-making in complex driving environments. To bridge this gap, we propose VLMPlanner, a hybrid framework that combines a learning-based real-time planner with a vision-language model (VLM) capable of reasoning over raw images. The VLM processes multi-view images to capture rich, detailed visual information and leverages its common-sense reasoning capabilities to guide the real-time planner in generating robust and safe trajectories. Furthermore, we develop the Context-Adaptive Inference Gate (CAI-Gate) mechanism that enables the VLM to mimic human driving behavior by dynamically adjusting its inference frequency based on scene complexity, thereby achieving an optimal balance between planning performance and computational efficiency. We evaluate our approach on the large-scale, challenging nuPlan benchmark, with comprehensive experimental results demonstrating superior planning performance in scenarios with intricate road conditions and dynamic elements. Code will be available.
