arXiv:2509.21143v3 Announce Type: replace Abstract: Multimodal models have demonstrated strong capabilities in web, desktop, and mobile GUI environments, but their performance in automotive cockpit systems remains largely unexplored. In-vehicle GUIs introduce unique challenges beyond traditional GUI interaction, including vehicle-state reasoning, implicit intent understanding, and safety-critical decision making. To address this gap, we introduce Automotive-ENV, an interactive benchmark for evaluating multimodal models in automotive cockpit environments. Built on Android Automotive OS, Automotive-ENV combines cockpit-screen interaction with vehicle-state signals and deterministic state-based evaluation. The benchmark contains 420 carefully constructed tasks covering routine cockpit operation and safety-critical scenarios. Unlike prior GUI benchmarks that rely on action matching or LLM-based judging, Automotive-ENV evaluates model behavior through reproducible validators over UI states, vehicle signals, and safety-related conditions. We benchmark representative general-purpose multimodal models and GUI-specialized vision-language models on Automotive-ENV. Experimental results show that current models remain far from reliable in automotive environments, with substantial performance degradation in implicit and safety-critical tasks. Further analysis reveals consistent failure modes in safety-aware reasoning, vehicle-control understanding, and implicit hazard recognition. We release Automotive-ENV, together with tasks, validators, and evaluation tools, to support future research on automotive multimodal systems.