  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.
