Adapting a Container Infrastructure for Autonomous Vehicle Development

TL;DR

This paper proposes a container infrastructure strategy for autonomous vehicle development, analyzing its performance and determinism impacts, to aid developers in effective deployment and integration.

Contribution

It introduces a container infrastructure approach tailored for AV development and provides an analysis of its performance and determinism characteristics.

Findings

Containers add delay but improve determinism

Nested containers do not increase delays but reduce determinism

The strategy helps developers optimize AV development infrastructure

Abstract

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.