Traditional in-vehicle agents mainly follow an instruction-following paradigm, where actions are executed only after explicit user commands. However, real-world cabin scenarios often require agents to proactively determine whether intervention is necessary by jointly reasoning over visual and state inputs. To address this gap, we propose CabinProAct, a multimodal benchmark for proactive intelligent cabin agents. In this setting, agents must jointly reason over in-cabin and out-cabin visual information, vehicle states, and executable APIs to autonomously decide whether to intervene and generate corresponding actions. We further introduce an execution-verification-based synthetic data generation framework to automatically construct execution-consistent proactive interaction data. We evaluate six state-of-the-art multimodal models. The results indicate that execution-grounded synthetic supervision effectively transfers to real-world in-cabin settings, enabling more robust proactive responses in real-world cabin scenarios.