Search-based testing is critical for evaluating the safety and reliability of autonomous driving systems (ADSs). However, existing approaches are often built on heterogeneous frameworks (e.g., distinct scenario spaces, simulators, and ADSs), which require considerable effort to reuse and adapt across different settings. To address these challenges, we present Drivora, a unified and extensible infrastructure for search-based ADS testing built on the widely used CARLA simulator. Drivora introduces a unified scenario definition, OpenScenario, that specifies scenarios using low-level, actionable parameters to ensure compatibility with existing methods while supporting extensibility to new testing designs (e.g., multi-autonomous-vehicle testing). On top of this, Drivora decouples the testing engine, scenario execution, and ADS integration. The testing engine leverages evolutionary computation to explore new scenarios and supports flexible customization of core components. The scenario execution can run arbitrary scenarios using a parallel execution mechanism that maximizes hardware utilization for large-scale batch simulation. For ADS integration, Drivora provides access to 12 ADSs through a unified interface, streamlining configuration and simplifying the incorporation of new ADSs. Our tools are publicly available at https://github.com/MingfeiCheng/Drivora.

翻译：基于搜索的测试对于评估自动驾驶系统（ADSs）的安全性与可靠性至关重要。然而，现有方法通常构建在异构框架之上（例如，不同的场景空间、模拟器和ADSs），这在不同设置间复用和适配需要大量工作。为应对这些挑战，我们提出了Drivora，一个基于广泛使用的CARLA模拟器构建的、用于基于搜索的ADS测试的统一可扩展基础设施。Drivora引入了一个统一的场景定义OpenScenario，它使用低层次、可操作的参数来指定场景，以确保与现有方法的兼容性，同时支持扩展到新的测试设计（例如，多自动驾驶车辆测试）。在此基础上，Drivora将测试引擎、场景执行和ADS集成进行解耦。测试引擎利用进化计算探索新场景，并支持核心组件的灵活定制。场景执行可通过并行执行机制运行任意场景，该机制为大规模批量模拟最大化硬件利用率。对于ADS集成，Drivora通过统一接口提供了对12个ADSs的访问，简化了配置过程并便于新ADSs的纳入。我们的工具已在 https://github.com/MingfeiCheng/Drivora 公开提供。