Talk to Me, Jarvis: An Open-Source Edge-Deployable Voice Assistant Framework for Autonomous Racecars

2026-09-17

AI Summary

本文开发了一种名为Jarvis的离线语音助手，用于自动驾驶车辆的高级行为命令。通过集成轻量级本地框架和领域特定微调模型，解决了在线模型的延迟问题，实现了97.63%的意图识别准确率。

Abstract

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.

Problem

• large language models
• voice assistants
• autonomous driving
• inference latency
• offline

Innovation

• offline voice assistant
• low-latency inference
• autonomous vehicles
• intent recognition