构建基于伪仿真车辆-基础设施协同规划评估基准，解决真实感与可扩展性难题。
VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
- 通过融合车与路侧设备观测，构建可扩展的伪仿真评估框架。
- 在真实交通场景下验证协同规划系统鲁棒性，误差传播效果清晰可见。
- 适合研究车路协同感知与决策的学者及工业界开发者参考。
城市环境中端到端自动驾驶需在部分可观测和复杂多智能体交互下实现稳健决策。交叉口严重遮挡与密集车流限制单智能体系统的感知能力，推动了车路协同（V2I）在感知与规划中的应用。然而现有评估协议存在根本矛盾：开环评估无法捕捉误差累积与偏离恢复，闭环评估成本高、难扩展，且常依赖存在域差距的仿真环境。为此，我们提出 VIPS——基于伪仿真的车路协同自动驾驶评估基准。VIPS 通过整合车辆与基础设施观测，实现无需全量仿真却具真实感的可扩展评估，有效捕捉系统鲁棒性与误差传播特性。我们进一步提出 CoS-V2X 协同规划框架，采用稀疏表示建模车路交互，以紧凑特征实现高效通信与异构观测下的鲁棒决策。代码与数据集详见 https://vips2026.github.io。
原文摘要 · Abstract (English)
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.
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