一句话：提出开源框架 CapCo，自动完成 Apollo 与 Carla 闭环联合仿真及场景化模糊测试。
针对百度 Apollo 等工业级自动驾驶系统测试需要可扩展、高保真且可复现的仿真，现有工具链或依赖已弃用平台，或需大量手工配置，难以支持大规模回归与模糊测试。本文提出 CapCo，一个开源框架，自动化 Apollo 与 Carla 之间的联合仿真，覆盖从启动 Apollo、配置场景到执行与监控测试的完整流程，并将 Apollo 集成到 Carla Leaderboard，保证同步通信与实时反馈。CapCo 支持多种模糊测试策略，内置高风险交通场景集，并提供友好界面用于配置实验、跟踪进度和分析结果。通过消除人工干预，CapCo 提升了大规模闭环、基于场景的自动驾驶测试的可重复性与自动化程度。
Testing industrial-grade autonomous driving systems (ADSs) such as Baidu Apollo demands scalable, high-fidelity simulation across diverse and high-risk traffic scenarios. From a software engineering perspective, this motivates reproducible and automatable test infrastructures that support regression testing at scale. However, current simulation toolchains either rely on deprecated platforms (e.g., LGSVL) or require extensive manual setup, hindering repeatability and fuzzing-based evaluation. We present CapCo, an open-source framework that automates co-simulation between Apollo and the Carla simulator, enabling closed-loop, scenario-based testing at scale. CapCo automates the full workflow—from launching Apollo and configuring scenarios to executing and monitoring tests—without manual intervention, and integrates Apollo into the Carla Leaderboard to ensure synchronized communication and real-time feedback. CapCo supports multiple fuzzing strategies and includes a curated set of high-risk traffic scenarios, with a user-friendly interface for configuring experiments, tracking progress, and analyzing results. Video: https://youtu.be/CUPHQfnTVss. Code: https://github.com/TayYim/Capco.