打通2D与3D驾驶仿真，自动验证自动驾驶规划算法漏洞
MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation
- 用多智能体协同仿真和程序化生成场景，跨平台无缝切换测试
- 实测发现规划算法在真实场景中存在行为偏差，暴露假设缺陷
- 适合自动驾驶算法开发与验证团队，降低仿真切换成本
基于场景的仿真测试是自动驾驶软件验证的核心。以往开发者需在低保真度2D仿真器（高效探索场景空间）与高保真度3D仿真器（细致分析特定场景）之间权衡，以降低测试成本并缩小仿真与现实的差距。本文提出一种新框架，利用多智能体协同仿真与程序化场景生成，支持运动规划算法在低/高保真度仿真器间的场景化测试。该框架减少场景迁移工作量，自动化执行实验、轨迹分析与可视化。基于参考规划器的实验表明，框架揭示了规划器预期行为与实际行为之间的差异，暴露了在更真实条件下规划假设的弱点。代码已开源：https://github.com/TUM-AVS/MultiDrive
原文摘要 · Abstract (English)
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators to explore the scenario space efficiently, and high-fidelity 3D simulators to study relevant scenarios in more detail, thus reducing testing costs while mitigating the sim-to-real gap. This paper presents a novel framework that leverages multi-agent co-simulation and procedural scenario generation to support scenario-based testing across low- and high-fidelity simulators for the development of motion planning algorithms. Our framework limits the effort required to transition scenarios between simulators and automates experiment execution, trajectory analysis, and visualization. Experiments with a reference motion planner show that our framework uncovers discrepancies between the planner's intended and actual behavior, thus exposing weaknesses in planning assumptions under more realistic conditions. Our framework is available at: https://github.com/TUM-AVS/MultiDrive
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