The automotive industry has transitioned from being an electromechanical 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 a 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 open source automotive software boom in its infancy with many implications for future research and practice.

The scope of automotive functions has grown from a single vehicle as an entity to multiple vehicles working together as an entity, referred to as cooperative driving. The current automotive safety standard, ISO 26262, is designed for single vehicles. With the increasing number of cooperative driving capable vehicles on the road, it is now imperative to systematically assess the functional safety of architectures of these vehicles. Many methods are proposed to assess architectures with respect to different quality attributes in the software architecture domain, but to the best of our knowledge, functional safety assessment of automotive architectures is not explored in the literature. We present a method, that leverages existing research in software architecture and safety engineering domains, to check whether the functional safety requirements for a cooperative driving scenario are fulfilled in the technical architecture of a vehicle. We apply our method on a real-life academic prototype for a cooperative driving scenario, platooning, and discuss our insights.