SAFe-Copilot：統合型共有自律フレームワーク
SAFe-Copilot: Unified Shared Autonomy Framework
VLMを用いてドライバーの意図を推定し、人間と自動運転の制御を高次元で調停する共有自律フレームワークを提案。衝突率の低減と性能向上を実現。
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著者: Phat Nguyen, Erfan Aasi, Shiva Sreeram, Guy Rosman, Andrew Silva, Sertac Karaman, Daniela Rus
分類: cs.RO
原文アブストラクト
Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerged as a promising approach to mitigate such failures by incorporating human input when autonomy is uncertain. However, most existing methods restrict arbitration to low-level trajectories, which represent only geometric paths and therefore fail to preserve the underlying driving intent. We propose a unified shared autonomy framework that integrates human input and autonomous planners at a higher level of abstraction. Our method leverages Vision Language Models (VLMs) to infer driver intent from multi-modal cues -- such as driver actions and environmental context -- and to synthesize coherent strategies that mediate between human and autonomous control. We first study the framework in a mock-human setting, where it achieves perfect recall alongside high accuracy and precision. A human-subject survey further shows strong alignment, with participants agreeing with arbitration outcomes in 92% of cases. Finally, evaluation on the Bench2Drive benchmark demonstrates a substantial reduction in collision rate and improvement in overall performance compared to pure autonomy. Arbitration at the level of semantic, language-based representations emerges as a design principle for shared autonomy, enabling systems to exercise common-sense reasoning and maintain continuity with human intent.
