話しかけて、ジャービス：自律走行レースカー向けのオープンソース・エッジ展開可能な音声アシスタントフレームワーク
Talk to Me, Jarvis: An Open-Source Edge-Deployable Voice Assistant Framework for Autonomous Racecars
自律走行車の高レベルな行動指示を音声で行うためのオフライン音声アシスタント「Jarvis」を開発し、Mistral 7Bのドメイン特化ファインチューニングにより97.63%の意図認識精度と平均1.39秒の低遅延を実現した。
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著者: Daniel Henel, Frederik Werner, Alexander Langmann, Johannes Betz
分類: cs.LG, cs.RO
原文アブストラクト
Recent advances in large language models have improved their effectiveness as back-end components for voice assistants, particularly in intent understanding and context-aware input classification. However, online-hosted models introduce network dependency and variable inference latency, limiting their suitability for time-critical autonomous driving applications. In this work, we address these issues by developing Jarvis, an offline voice assistant for high-level behavioral commands of autonomous vehicles. Its architecture integrates speech recognition and synthesis with natural language command classification into a lightweight, local framework. Jarvis core component is a text-to-command classifier, built using a domain-specific fine-tuning of the Mistral 7B model, demonstrating low-latency inference. Our experimental evaluation demonstrates that our solution outperforms larger online-hosted models, achieving 97.63 % intent recognition accuracy with an average processing latency of 1.39 s, making it well-suited for operations requiring quick response times. To support further research and fine-tuning, we provide an open-source implementation.