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

arXiv: https://arxiv.org/abs/2501.18942 · HTML: https://mlsanity.com/paper/2501.18942/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboard](https://mlsanity.com/paper/2511.14876/) (2025)
- [YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception](https://mlsanity.com/paper/2603.23037/) (2026)
- [LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles](https://mlsanity.com/paper/2608.13450/) (2026)
- [Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models](https://mlsanity.com/paper/2608.30179/) (2026)
- [DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information](https://mlsanity.com/paper/2609.01120/) (2026)
- [IMM-based Multiple Object Tracking using a State Prediction Neural Network](https://mlsanity.com/paper/2609.13307/) (2026)

## Abstract (arXiv)

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.