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arXiv · 2509.21143

Automotive-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems

Junfeng Yan·Biao Wu·Meng Fang·Ling Chen
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Abstract

Multimodal agents have demonstrated strong performance in general GUI interactions, but their application in automotive systems has been largely unexplored. In-vehicle GUIs present distinct challenges: drivers' limited attention, strict safety requirements, and complex location-based interaction patterns. To address these challenges, we introduce Automotive-ENV, the first high-fidelity benchmark and interaction environment tailored for vehicle GUIs. This platform defines 185 parameterized tasks spanning explicit control, implicit intent understanding, and safety-aware tasks, and provides structured multimodal observations with precise programmatic checks for reproducible evaluation. Building on this benchmark, we propose ASURADA, a geo-aware multimodal agent that integrates GPS-informed context to dynamically adjust actions based on location, environmental conditions, and regional driving norms. Experiments show that geo-aware information significantly improves success on safety-aware tasks, highlighting the importance of location-based context in automotive environments. We will release Automotive-ENV, complete with all tasks and benchmarking tools, to further the development of safe and adaptive in-vehicle agents.

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Junfeng Yan, Biao Wu, Meng Fang, Ling Chen. 2025-09-27. Automotive-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems. https://arxiv.org/abs/2509.21143

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