Future-Proofing Automotive Software: Modularity, Reuse, and Safety with Model-Based Design
Overview
Model-Based Design (MBD) is a development approach that uses models to design, simulate, and validate automotive systems and software. This webinar will discuss how to "future-proof” automotive software with MBD to enhance modularity, reuse, and safety in the SDV era. Our engineering experts will explore key strategies for decoupling software from hardware, designing for reuse, and overcoming industry challenges.
Highlights include:
- How MBD streamlines software transformation, empowers domain experts, and ensures first-time integration success
- How to leverage tools for software architecture and automatic code generation to create scalable, reusable components
This webinar offers the opportunity to discover how MBD is reshaping automotive software development and driving future innovation. An audience Q&A session will follow the technical presentation.
Highlights
Dave Hoadley, Ph.D., Senior Principal Technical Consultant, MathWorks
Dr. Dave Hoadley is a Senior Principal Technical Consultant for MathWorks who specializes in enabling customers to adopt model-based design in their product development workflow and maximize the positive impacts of that change for their organizations. This focus regularly includes mathematical modeling, verification and validation, code generation, and workflow optimization. Dave has worked with many industries, including on- and off-highway vehicles.
Brandon Trombley, Global Technical Account Manager, MathWorks
Brandon Trombley has served as MathWorks’ Global Technical Account Manager since 2018. Prior to joining MathWorks, Brandon had a 20 year career working primarily for automotive Tier 1 suppliers in various roles — including managing a team to establish an ISO 26262-qualified workflow for model-based design in AUTOSAR; vehicle crash algorithm subsystem architecture design; component development; production code generation, validation, and calibration; MATLAB/Simulink tool development and deployment; signal processing; sensor modeling; vision systems; and business intelligence. Brandon holds a bachelor’s degree in physics from the University of Michigan and pursued graduate studies in electrical engineering at the University of Michigan, Dearborn.
