  In the field of Autonomous Vehicle (AV) development, having a robust yet
flexible infrastructure enables code to be continuously integrated and
deployed, which in turn accelerates the rapid prototyping process. The
platform-agnostic and scalable container infrastructure, often exploited by
developers in the cloud domain, presents a viable solution addressing this need
in AV development. Developers use tools such as Docker to build containers and
Kubernetes to setup container networks. This paper presents a container
infrastructure strategy for AV development, discusses the scenarios in which
this strategy is useful and performs an analysis on container boundary
overhead, and its impact on a Mix Critical System (MCS). An experiment was
conducted to compare both operation runtime and communication delay of running
a Gaussian Seidel Algorithm with I/O in four different environments: native OS,
new container, existing container, and nested container. The comparison reveals
that running in containers indeed adds a delay to signal response time, but
behaves more deterministically and that nested container does not stack up
delays but makes the process less deterministic. With these concerns in mind,
the developers may be more informed when setting up the container
infrastructure, and take full advantage of the new infrastructure while
avoiding some common pitfalls.
