Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driv-ing world models forecast the external environment, in-cabin intelligenceremains strictly recognition-oriented and lacks multi-step rollout capa-bilities for driver dynamics. We introduce Driver-WM, a driver-centriclatent world model that rolls out in-cabin dynamics causally conditionedon out-cabin traffic context. This formulation unifies physical kinemat-ics forecasting with auxiliary behavioral and emotional semantic recog-nition. Operating in a compact latent space constructed from frozenvision-language features, Driver-WM adopts a dual-stream architectureto separately encode external traffic and internal driver states. Thesestreams are directionally coupled via a gated causal injection mechanism,which uses a learned vector gate to modulate external contextual per-turbations while strictly enforcing temporal causality. Experiments onAIDE show robust long-horizon forecasting on reactive high-motion clips,improved driver/traffic semantic alignment, and controlled interventionsthat expose the external-to-internal mechanism.