一句话：提出统一可扩展的 Drivora 基础设施，解耦测试引擎、场景执行与 ADS 集成，支持基于搜索的自动驾驶测试复用与扩展。
基于搜索的测试对评估自动驾驶系统（ADS）的安全性与可靠性至关重要，但现有方法往往建立在异构框架之上——场景空间、模拟器和 ADS 各不相同，导致跨设置复用与适配成本高昂。为此，作者提出 Drivora，一个构建于广泛使用的 CARLA 模拟器之上的统一且可扩展的基于搜索的 ADS 测试基础设施。Drivora 引入统一的场景定义 OpenScenario，使用低层可执行参数描述场景，在兼容现有方法的同时支持多自动驾驶车辆测试等新设计的扩展。其核心设计是将测试引擎、场景执行与 ADS 集成解耦：测试引擎利用进化计算探索新场景并支持核心组件灵活定制；场景执行通过并行机制运行任意场景，最大化大规模批量仿真的硬件利用率；ADS 集成则通过统一接口接入 12 个 ADS，简化配置与新 ADS 的加入。工具已开源。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2601-05685,
title = {Drivora: A Unified and Extensible Infrastructure for Search-based Autonomous Driving Testing},
author = {Mingfei Cheng and Lionel Briand and Yuan Zhou},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3774748.3787599}
}