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

2025-01-31

AI Summary

A systematic, quantitative comparison of the Autoware and Apollo open-source autonomous driving platforms is currently lacking, hindering informed platform selection and technical advancement. This paper presents the first module-level,横向 evaluation, focusing on perception, planning, control, and middleware performance. We employ module decoupling tests, ROS/DDS communication latency measurements, and closed-loop simulation validation—leveraging real-world sensor data and high-definition map environments. Experimental results reveal significant differences: Autoware achieves 12% lower average end-to-end latency; Apollo exhibits higher inter-module coupling; Autoware demonstrates broader hardware compatibility, particularly with domestic AI chips; and Apollo provides more comprehensive documentation, enhancing developer usability. Our work fills a critical gap in quantitative benchmarking of open-source autonomous driving platforms, delivering a reproducible evaluation framework and evidence-based guidance for platform selection and architectural decision-making.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Audits of web technologies, standards, platforms, and applications

Abstract

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.

Problem

• Autoware
• Apollo
• autonomous driving software comparison

Innovation

• Autoware
• Apollo
• Autonomous Driving Middleware Comparison