arXiv:2501.18942cs.RO2025-01被引 20

对比Autoware与Apollo,帮开发者选对自动驾驶开源平台。

Open-Source Autonomous Driving Software Platforms: Comparison of Autoware and Apollo

  • 系统对比两平台核心模块与中间件性能
  • 揭示两者在架构设计上的关键差异
  • 适合研究者与工程师做技术选型参考

全栈自动驾驶系统涵盖感知、规划、控制等多个技术领域,每个领域均需深入研究。同时,系统的验证需要仿真器、传感器和高精地图等大量支撑基础设施,这极大地提高了个体开发者和研究团队的进入门槛。近年来,开源自动驾驶软件平台应运而生,为实现和评估自动驾驶功能提供技术和基础设施支持。在主流平台中,Autoware与Apollo在学术界和工业界被广泛采用。尽管已有研究分别评估过这两个平台,但缺乏对其能力进行量化、详尽的横向对比。本文系统分析Autoware与Apollo的核心模块,并评估其中间件性能,以揭示二者的关键差异。研究结果可为研究人员与工程师提供实用参考,助力其根据自身开发环境选择最合适的平台,推动全栈自动驾驶系统的发展。

原文摘要 · Abstract (English)

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.

自动驾驶开源平台AutowareApollo

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