A Comparison of ROS 2 and AUTOSAR Adaptive Platform Against Industry-Elicited Automotive Middleware Requirements
career value
253K/year
🤖 AI Summary
This study addresses the lack of industrial empirical evaluation in automotive software middleware selection by systematically comparing ROS 2 Jazzy and AUTOSAR Adaptive Platform R24-11 across key dimensions—communication efficiency, integration capability, and engineering applicability—based on real-world engineering requirements from ZF Group, a leading automotive supplier. Through an integrated approach combining requirement analysis, functional mapping, and empirical testing, the work reveals the technical strengths and limitations of these two mainstream middleware solutions. The findings fill a critical gap in industry-oriented assessment and provide empirically grounded insights to support informed architectural decisions in automotive software development.
Application Category
📝 Abstract
In software-defined vehicles, automotive middleware plays a fundamental role in enabling efficient communication, integration, and coordination among software components. This paper examines how well two of the currently most popular middleware frameworks, ROS 2 Jazzy and AUTOSAR Adaptive Platform R24-11, meet practical requirements elicited from automotive software engineers at one of the major automotive supplier companies, ZF Group. Our objective is to provide insight into an otherwise difficult-to-obtain industrial perspective and support a clearer understanding of priorities in the development and evaluation of middleware for automotive applications.
Problem
Research questions and friction points this paper is trying to address.
automotive middleware
ROS 2
AUTOSAR Adaptive Platform
software-defined vehicles
industry requirements
Innovation
Methods, ideas, or system contributions that make the work stand out.
automotive middleware
ROS 2
AUTOSAR Adaptive Platform
industry requirements
software-defined vehicles
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