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

2026-09-02

AI Summary

为解决城市环境中自动驾驶的决策问题，提出VIPS基准通过伪仿真进行车辆-基础设施协同规划评估，并引入CoS-V2X框架增强鲁棒性。

Abstract

End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Severe occlusions and dense traffic at intersections limit the perception capability of single-agent systems, motivating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation for perception and planning. However, existing evaluation protocols face a fundamental trade-off: open-loop evaluation fails to capture error accumulation and recovery from deviations, while closed-loop evaluation is costly, difficult to scale, and often relies on simulated environments that may suffer from domain gaps. To bridge this gap, we propose VIPS, a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation. VIPS extends pseudo-simulation by integrating vehicle and infrastructure observations. This enables scalable yet realistic evaluation of robustness and error propagation without full simulation. We further present CoS-V2X, a cooperative planning framework based on sparse representations. CoS-V2X models vehicle-infrastructure interactions using compact features for efficient communication and robust decision-making under heterogeneous observations. Code and dataset are available at https://vips2026.github.io.

Problem

• end-to-end autonomous driving
• partial observability
• multi-agent interactions
• Vehicle-to-Infrastructure (V2I) cooperation
• evaluation protocols

Innovation

• Pseudo-Simulation
• Vehicle-Infrastructure Cooperation
• Sparse Representations
• Robust Decision-Making