VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation

End-to-end autonomous driving in urban environments re-quires robust decision-making under partial observability and complexmulti-agent interactions. Severe occlusions and dense traffic at inter-sections limit the perception capability of single-agent systems, moti-vating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation forperception and planning. However, existing evaluation protocols face afundamental trade-off: open-loop evaluation fails to capture error accu-mulation and recovery from deviations, while closed-loop evaluation iscostly, difficult to scale, and often relies on simulated environments thatmay suffer from domain gaps. To bridge this gap, we propose VIPS, abenchmark for cooperative autonomous driving in V2I settings based onpseudo-simulation. VIPS extends pseudo-simulation by integrating ve-hicle and infrastructure observations. This enables scalable yet realisticevaluation of robustness and error propagation without full simulation.We further present CoS-V2X, a cooperative planning framework basedon sparse representations. CoS-V2X models vehicle–infrastructure inter-actions using compact features for efficient communication and robustdecision-making under heterogeneous observations. Code and dataset areavailable at https://vips2026.github.io.