Painting the Landscape of Automotive Software in GitHub

TL;DR

This paper explores the open-source automotive software landscape on GitHub, highlighting its growth, characteristics, and development trends over 12 years, revealing a burgeoning industry with diverse projects and shifting technologies.

Contribution

It provides the first comprehensive analysis of open-source automotive software on GitHub, detailing its characteristics, development styles, and evolving trends from 2010 to 2021.

Findings

Over 600 active projects with 15,000+ contributors.

Shift from MATLAB to Python in programming languages.

Increase in perception and decision-making software.

Abstract

The automotive industry has transitioned from being an electro-mechanical to a software-intensive industry. A current high-end production vehicle contains 100 million+ lines of code surpassing modern airplanes, the Large Hadron Collider, the Android OS, and Facebook's front-end software, in code size by a huge margin. Today, software companies worldwide, including Apple, Google, Huawei, Baidu, and Sony are reportedly working to bring their vehicles to the road. This paper ventures into the automotive software landscape in open source, providing the first glimpse into this multi-disciplinary industry with a long history of closed source development. We paint the landscape of automotive software on GitHub by describing its characteristics and development styles. The landscape is defined by 15,000+ users contributing to ~600 actively-developed automotive software projects created in a span of 12 years from 2010 until 2021. These projects range from vehicle dynamics-related software; firmware and drivers for sensors like LiDAR and camera; algorithms for perception and motion control; to complete operating systems integrating the above. Developments in the field are spearheaded by industry and academia alike, with one in three actively developed automotive software repositories owned by an organization. We observe shifts along multiple dimensions, including preferred language from MATLAB to Python and prevalence of perception and decision-related software over traditional automotive software. This study witnesses the open-source automotive software boom in its infancy with many implications for future research and practice.