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Software Development Applying Model-Based Design Process & Tools
Kim Murphy, Ford Motor Company
With the industry demanding new automotive technologies be delivered at an ever-increasing rate, Ford has embraced both MBD and MBSE to help deliver these technologies. With MBD as the foundation, it became essential for Ford to have well-defined MBD processes and tools in place. The Ford Motor Company MBD Core team was established to do just that. Through agile processes and lifecycle planning, the team develops and deploys common MBD processes and tools for all users globally at Ford. Our belief is that with a centralized MBD support team, we are not only developing common processes, best practices, and lessons learned for MBD, but we are also laying the groundwork so that we can effectively perform virtual vehicle level testing.
Evolving Model-Based Engineering Environments to Manage Complexity and Scale
Ramamurthy Mani, MathWorks
Massive change is underway in the automotive industry with trends in autonomous driving, vehicle electrification, and connectivity. In this talk, Ramamurthy Mani shares how MathWorks is addressing complexity, scale, and collaborative workflows in tune with evolving demands on automotive software architectures.
Model Service-Oriented Architectures in Simulink
Luigi Milia, MathWorks
Service-oriented architecture (SOA) is a software architecture based on the concept that a system consists of a set of services in which one service may use another, and applications use one or more of the services based on their needs. SOA promotes a loosely coupled component-based approach using middleware for service-oriented communication. SOA concepts are used in multiple industry standards, including: AUTOSAR, ROS and DDS.
In this talk, MathWorks showcases how the Simulink® is used to model and simulate application software based on SOA. The presentation highlights:
Modeling of message-based communication between software components
Modeling of Adaptive AUTOSAR software components
C++ production code generation with Adaptive middleware interfaces (ara::com), and AUTOSAR XML export
Continuous Integration within a Model Based Workflow
Nick Mazzilli, A123 Systems
A123 integrated their Model-Based Design toolset for AUTOSAR development with Jenkins for continuous integration. This was done to support the expanding team and usage of models. The resulting environment automates 90% of the steps along the common software development process, and also facilitates design reviews that are based on a rich set of data and metrics.
Common Pitfalls and Solutions When Applying Model-Based Design to ISO 26262 Development
Jason Moore, MathWorks
MathWorks has helped many teams migrate their software development processes to meet ISO 26262. In the process, we also helped these teams optimize the use of Model-Based Design methods and tools. We identified a few areas of common pitfalls when executing the migration, such as formal process mapping, artifact generation, software architecture design and review, and tool qualification. We would like to share our learning and recommended solutions through this presentation.
Enhanced Data Dictionary for Model Based Automotive Production Software Development
Todd Nordby, Navistar International
Navistar developed a new Data Dictionary tool (DD) enhancing Simulink’s Data Dictionary to simplify control model design while supporting production code generation and deployment. Providing further data object abstraction, the DD simplifies various user groups’ processes and streamlines collaboration during development. For example, function developers use the DD to specify functional requirements for data objects such as names, physical units, and signal value ranges; while embedded software engineers simultaneously specify production code specific aspects. This approach lets function developers focus on functional requirements while embedded software engineers focus on production code implementation and integration. The DD also includes a lightweight component-based framework supporting the integration of hundreds of software components for both AUTOSAR and non-AUTOSAR applications using a bottom-up approach.
Enterprise Engineering Platform for AI
Seth DeLand, MathWorks
Successful application of AI to engineering requires a complete design workflow rather than data and algorithms. This presentation will discuss how MathWorks developed a complete enterprise engineering platform for AI. You will see the reasons for Gartner to name MathWorks a “Leader” in the 2020 Magic Quadrant for Data Science & Machine Learning Platforms.
Using MATLAB on Apache Spark for ADAS Feature Usage Analysis and Scenario Generation
Sanjay Abhyankar, Ford
In the past, engineers download terabyte-sized ADAS datasets to look for edge cases. This approach consumes huge amount of network bandwidth and local storage space. We created a new and more efficient way, which utilizes MATLAB to access Apache Spark resources to decode, analyze data, and search for edge cases right on the Hadoop file system. It dramatically improves throughput and reduces the amount of data downloaded to the engineer’s workstation.
This approach was successfully used to analyze ADAS feature usage from the CAN traffic on Ford’s Big-Data-Drive fleet of vehicles. It will be deployed for all future Big-Data-Drive vehicle analysis.
Tackling Fleet Test Data with MATLAB
Will Wilson, MathWorks
Do you have a strategy to analyze the data from your connected test vehicle fleet? How fast are you able to develop and apply analytics on huge sets of data to find desired events or find trends that were previously unknown? Are you able to work with all of your data instead of a subset?
In this talk, Will Wilson will demonstrate how to implement a workflow with MATLAB® that addresses these issues. Topics include:
Exploring the types of questions you can ask of your fleet data
Preparing your data for efficient analytics
Developing analytics that execute on a “per unit” or “across all” basis
Deploying analytics to keep up with the continuous intake of test data
Machine Learning Case Studies for Quality Evaluations
Marc Harris, TimkenSteel
The rapid boom in big data generation, data science, and machine learning has led to a massive opportunity for continuous improvement, optimization, and automation across virtually all industries. The focus here is on applications of machine learning tools within the steel industry, demonstrating the capabilities, speed, and accuracy using MathWorks® Deep Learning Toolbox. Two steel specific case studies are presented: automated non-metallic inclusion classification and coarse dimensional measurement of in-process steel production. The technique of transfer learning was employed to reduce computational overhead, and it was found that a proper ground truth training dataset with intelligent image pre-processing yielded results with better-than-human accuracies at vastly superior speeds. Implementation of finished models in a standardized dynamic-link library format provided seamless integration with other common programming languages, which led to a straightforward, easily scaling, production roll out. Advantages were immediately apparent with regard to task specific man-hours and evaluation consistency. Basic architectures, pre-processing steps, training parameters, and model performance are described for each case study.
A Perspective on Deploying Reinforcement Learning to Augment Classic Control Design
Ali Borhan, Cummins
With the advancement in machine learning, access to data with V2X connectivity, and more reliable plant model simulation, reinforcement learning has been considered recently as a control design option for the feedback control of automotive systems. In this talk, the challenges of applying classic control methods with focus on PID structure are briefly discussed and a perspective to deploy reinforcement learning to address some of these challenges is presented.
Advanced Tool Capabilities for Embedding Machine Learning into ECUs
Gokhan Atinc, MathWorks
Machine learning is a hot topic in the automotive industry. Deploying machine learning algorithms to electronic control units (ECUs) is often a bottleneck because of the memory, CPU throughput, and software development and integration techniques required to support machine learning algorithms.
In this presentation, Gokhan Atinc provides an overview of machine learning technologies and deployment workflows for embedded processors. He will also discuss advanced capabilities that are of interest for automotive and adjacent industries, including in-place modification support, Simulink® support, and fixed-point conversion.
Making MATLAB Data Analytics Accessible Across Enterprise
Arvind Hosagrahara, MathWorks
MATLAB® has scaled up to support cluster and cloud-based data and computing frameworks. In the meantime, data and computing framework technologies continue to evolve rapidly. In this presentation, Arvind provides an example of how an enterprise customer integrated MATLAB with their existing framework. The integration enables engineers to slice and dice very large datasets using Apache Spark™ and extract forensic slices to develop analytics that can then be pushed down to execute at scale on the cluster. The integration with MATLAB also supports workflows that conform to enterprise-level security, governance, and access controls requirements while enabling users to make the results of their analytics easily accessible across the organization.
Design 3D Scenes for Automated Driving Simulation with RoadRunner
Peter Fryscak, MathWorks
In this presentation, you will learn how to easily create 3D scenes and road networks for automated driving simulation using RoadRunner. You will also see how to automate the creation of road networks from high-definite (HD) maps. After you finish creating and editing the scenes, you can export them to industry standard file formats such as OpenDRIVE and automated driving simulators such as CARLA, NVIDIA DriveSIM, and Simulink.
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