DriveTester统一自动驾驶测试环境，提升方法可复现性。
DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
- 基于Apollo构建统一测试平台，整合轻量交通模拟器
- 支持多种前沿测试技术，保障环境一致性与稳定性
- 适合研究者高效对比算法，推动自动驾驶测试标准化
基于仿真的测试在评估自动驾驶系统（ADS）的安全性和可靠性方面至关重要。然而，其核心挑战在于仿真环境的准备与配置复杂，尤其体现在仿真器与自动驾驶系统之间的兼容性与稳定性问题。这导致研究人员需投入大量精力自定义环境，造成开发平台和底层系统的差异，使得方法的复现与比较难以实现。为此，我们提出DriveTester，一个基于Apollo（最广泛使用的开源工业级自动驾驶平台之一）构建的统一仿真测试平台。DriveTester提供一致可靠的测试环境，集成轻量级交通模拟器，并融合多种先进的自动驾驶测试技术，使研究者能在标准化平台上高效地开发、测试和对比方法，显著提升不同测试方案间的可复现性与可比性。代码已公开：https://github.com/MingfeiCheng/DriveTester。
原文摘要 · Abstract (English)
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.
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