LVLMとMPCの連携による自動運転：安全性を考慮したタスク拡張可能な制御アーキテクチャ
LVLM-MPC Collaboration for Autonomous Driving: A Safety-Aware and Task-Scalable Control Architecture
大規模視覚言語モデル（LVLM）が生成する高レベルなタスク指令に基づき、MPC Builderがオンデマンドでモデル予測制御器を自動生成し、安全性とタスク切替の実現可能性を担保する自動運転向け統合制御フレームワークを提案した。
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著者: Kazuki Atsuta, Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper proposes a novel Large Vision-Language Model (LVLM) and Model Predictive Control (MPC) integration framework that delivers both task scalability and safety for Autonomous Driving (AD). LVLMs excel at high-level task planning across diverse driving scenarios. However, since these foundation models are not specifically designed for driving and their reasoning is not consistent with the feasibility of low-level motion planning, concerns remain regarding safety and smooth task switching. This paper integrates LVLMs with MPC Builder, which automatically generates MPCs on demand, based on symbolic task commands generated by the LVLM, while ensuring optimality and safety. The generated MPCs can strongly assist the execution or rejection of LVLM-driven task switching by providing feedback on the feasibility of the given tasks and generating task-switching-aware MPCs. Our approach provides a safe, flexible, and adaptable control framework, bridging the gap between cutting-edge foundation models and reliable vehicle operation. We demonstrate the effectiveness of our approach through a simulation experiment, showing that our system can safely and effectively handle highway driving while maintaining the flexibility and adaptability of LVLMs.
