Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting infrastructure, from simulators and sensors to high-definition maps. These complexities with barrier to entry pose substantial limitations for individual developers and research groups. Recently, open-source autonomous driving software platforms have emerged to address this challenge by providing autonomous driving technologies and practical supporting infrastructure for implementing and evaluating autonomous driving functionalities. Among the prominent open-source platforms, Autoware and Apollo are frequently adopted in both academia and industry. While previous studies have assessed each platform independently, few have offered a quantitative and detailed head-to-head comparison of their capabilities. In this paper, we systematically examine the core modules of Autoware and Apollo and evaluate their middleware performance to highlight key differences. These insights serve as a practical reference for researchers and engineers, guiding them in selecting the most suitable platform for their specific development environments and advancing the field of full-stack autonomous driving system.

翻译：全栈自动驾驶系统涵盖感知、规划与控制等多个技术领域，每个领域均需深入研究。此外，验证此类系统技术需要从仿真器、传感器到高精地图的广泛支持性基础设施。这些复杂性及高准入门槛对独立开发者和研究团队构成了显著限制。近年来，开源自动驾驶软件平台通过提供自动驾驶技术及实用的支持性基础设施，以支持自动驾驶功能的实现与评估，从而应对这一挑战。在主流开源平台中，Autoware与Apollo在学术界和工业界均被广泛采用。尽管已有研究分别评估了这两个平台，但鲜有研究对其能力进行定量且详细的直接比较。本文系统性地考察了Autoware与Apollo的核心模块，并评估了其中间件性能以凸显关键差异。这些见解为研究人员和工程师提供了实用参考，可指导他们根据具体开发环境选择最合适的平台，进而推动全栈自动驾驶系统领域的发展。