Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulator and the ADS. This complexity often results in researchers dedicating significant effort to customize their own environments, leading to disparities in development platforms and underlying systems. Consequently, reproducing and comparing these methodologies on a unified ADS testing platform becomes difficult. To address these challenges, we introduce DriveTester, a unified simulation-based testing platform built on Apollo, one of the most widely used open-source, industrial-level ADS platforms. DriveTester provides a consistent and reliable environment, integrates a lightweight traffic simulator, and incorporates various state-of-the-art ADS testing techniques. This enables researchers to efficiently develop, test, and compare their methods within a standardized platform, fostering reproducibility and comparison across different ADS testing approaches. The code is available: https://github.com/MingfeiCheng/DriveTester.

翻译：基于仿真的测试在评估自动驾驶系统（ADS）的安全性与可靠性方面发挥着关键作用。然而，ADS测试面临的主要挑战之一是仿真环境准备与配置的复杂性，尤其是在仿真器与ADS之间的兼容性和稳定性方面。这种复杂性常导致研究人员需投入大量精力定制各自的环境，从而造成开发平台与底层系统的差异。因此，在统一的ADS测试平台上复现和比较这些方法变得十分困难。为应对这些挑战，我们推出了DriveTester——一个基于目前最广泛使用的开源工业级ADS平台之一Apollo构建的、基于仿真的统一测试平台。DriveTester提供了一个一致且可靠的环境，集成了轻量级交通仿真器，并融合了多种先进的ADS测试技术。这使得研究人员能够在标准化平台内高效地开发、测试和比较各自的方法，从而促进不同ADS测试方法之间的可复现性与可比性。代码已开源：https://github.com/MingfeiCheng/DriveTester。