Johannes Betz, Daniel Henel, Frederik Werner, Alexander Langmann
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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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.21109")
get_code_for_paper("2609.21109")
have("2609.21109")
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curl https://syntology.ai/api/ran/2609.21109.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.21109)
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