White Paper: Automotive
Model-Based Design for Software-Defined Vehicles
SPONSORED BY: ![]()
The transformation to software-defined vehicles (SDVs) will enable automakers to add new features throughout a vehicle’s life.
To support frequent updates, teams are adopting new approaches, including central/zonal E/E architectures with high-performance computers (HPC), embedded software with service-oriented architecture, automation with continuous integration and continuous delivery, and virtual validation using simulation.
This white paper shares insights and best practices derived from MathWorks engagements with automotive companies around the world. It covers the ways Model-Based Design enables the development of software-defined vehicles.
Don't have an account?
Overview
The white paper on Model-Based Design for Software-Defined Vehicles (SDVs) discusses the transformative shift in the automotive industry towards integrating advanced software capabilities into vehicles. This transition enables automakers to continuously enhance vehicle features throughout their lifecycle, responding to evolving customer expectations for safety, digital interaction, and advanced functionalities.
Key challenges in developing SDVs include increased software complexity, the need for functional safety, and the historical separation between vehicle system and software development teams. The paper emphasizes the importance of collaboration between these teams, advocating for shared tools and goals to streamline the development process.
Model-Based Design (MBD) is presented as a crucial approach that facilitates the development of SDVs. It allows for software reuse across various platforms, such as high-performance computers (HPCs) and electronic control units (ECUs), thereby reducing development time through automation. The integration of physical system models with virtualized software stacks supports early defect detection and enhances test coverage, a practice known as "shift-left integration."
The paper highlights the role of continuous integration and continuous delivery (CI/CD) in automating validation processes, which is essential for maintaining high-quality software. It cites the example of Geely, which reported a 90% success rate in first-time software integration tests by leveraging these practices.
Furthermore, the document underscores the need for modern software development practices to empower domain experts, enabling them to create high-quality software that meets safety and quality requirements. The ultimate goal is to align software and system mindsets, tools, and processes to deliver reliable, innovative, and customer-centric vehicle technologies.
In conclusion, the white paper positions MathWorks as a partner in this journey, offering tools that support AI, cloud technology, and modern software practices to facilitate the development of software-defined vehicles.