MathWorks
AUTOMOTIVE CONFERENCE 2023
North America
April 25 | Plymouth, Michigan
MathWorks Automotive Conference brought together engineers, researchers, and scientists to share real-world examples and learn more about the latest automotive trends like software-defined vehicles, electrification, automated driving, and AI in engineering.
Featured Sessions
Alan Amici, Center for Automotive Research
Mary Ann Freeman, MathWorks
Agenda
Registration and Welcome
Keynote: The EV Transition Begins
The auto industry is now in the midst of a shift from internal combustion engine (ICE) to electric vehicle (EV) propulsion. This is the most significant change in the 100-year history of the automobile. Investment in EVs has been substantial in the past 2 years with more than $35 billion committed to assembly and battery production in North America alone. R&D priorities are shifting to meet future market demand. Digital engineering is a key ingredient to reducing development costs and improving speed to market. Hear production forecasts for ICE and EVs through 2030, the EV investment landscape for North America, legislative policy, and the impact on R&D.
Alan Amici
Alan Amici is the president and CEO of the Center for Automotive Research (CAR). Alan joined CAR after a 35+ year career with Chrysler and TE Connectivity, holding a variety of positions in engineering, manufacturing, and service. His roles at TE included:
- VP and CTO, Transportation Solutions
- VP Engineering, Automotive Americas
Highlights of his tenure at Chrysler included:
- Head of Global Uconnect – Infotainment and Connected Car Platform
- Head of Electrical/Electronics Engineering (Torino, Italy, and Auburn Hills, Michigan)
- Senior Manager, Global Service and Parts (Stuttgart, Germany)
Alan also serves on the Board of Advisors at Penn State Harrisburg and the Department of Industrial Engineering at Wayne State University. He holds an M.B.A, a Master of Science degree, and a Bachelor of Science degree in electrical engineering from the University of Michigan. He is a graduate of the Chrysler Institute of Engineering. Alan is the owner of two patents and is the recipient of the Walter P. Chrysler Technology Award.
Keynote: Developing and Delivering the New Generation of Software-Defined Vehicles
The development of automotive systems and software is being redefined to deliver perpetually upgradeable software-defined vehicles. The user experience of these vehicles will be largely defined by their software capabilities and value-added services. The solution landscape is complex with no single winning strategy. Delivering this software requires a mindset shift; updated organizational structures; introduction of new processes, methods, and development platforms; and the forging of new partnerships. Some additional challenges include optimizing engineering methods with changing OEM/Tier-1 relationships, leveraging hyperscalers, and using both models and vehicle data for optimization. Model-Based Design, AI, and DevOps have emerged as a set of typical approaches that both challengers and incumbents are adopting. Clean-slate implementation of Model-Based Design and development processes has enabled newcomers to exploit their software development expertise. Established players are looking to turn their technical legacy into their advantage by bringing years of rigorous Model-Based Design and system development experience together with new software and data capabilities to master the business transformation.
In this talk, Jim Tung, chief strategist at MathWorks, contrasts these approaches and presents mindset shifts and strategies needed to enable the systematic use of models and data that is helping to develop a new generation of software-defined vehicles.
Jim Tung
Jim Tung has more than 30 years of experience in the technical computing software markets. He is a 25-year veteran of MathWorks, holding the positions of vice president of marketing and vice president of business development before assuming his current role focusing on business and technology strategy and analysis. Jim previously held marketing and sales management positions at Lotus Development and Keithley DAS, a pioneering manufacturer of PC-based data acquisition systems. Jim holds a bachelor's degree from Harvard University.
Break and Technology Showcase
Cloud-Native Development and Model-Based Approaches for Software-Defined Vehicles
The future of automotive technology is cloud native and software defined. This evolution presents challenges and opportunities to leverage cloud-native capabilities from software development to safe and secure operations, as well as model-based approaches to ensure reusability, reliability, and quality. This presentation explores how AWS and its partner ecosystem’s automotive-specific solutions for connected and software-defined vehicles can be a value multiplier for automotive OEMs and their ecosystem. We discuss how model-based development and design can be integrated with cloud-native development and deployment, creating a powerful framework for developing and scaling software-defined vehicles. We also explore the benefits of leveraging cloud-native and model-based approaches, including increased efficiency, performance, agility, and innovation.
Stefano Marzani
Stefano Marzani is worldwide tech leader for Software-Defined Vehicles at Amazon Web Services (AWS), where he focuses on solving the most significant challenges facing the auto industry. As a key contributor in the development of cloud-to-vehicle environmental parity architectures, Stefano has played a significant role in shaping the auto industry’s move toward software-defined vehicles. This transition enables more efficient development of autonomous functionalities, mobility fleet solutions, and delightful user experiences. Stefano has over 20 years of expertise in IoT, cloud and vehicle architectures, HMI, and software development and tooling. Before AWS, Stefano built world-class innovative teams and startups, including cofounding a European leading company focused on interaction engineering. Stefano is a governing body member of the SOAFEE SIG.
Scaling Vehicle Simulations with MATLAB Parallel Computing
Vehicle simulations play a critical part in enabling engineers to design a product to meet attribute targets at a much faster pace and lower cost than real-world testing. While simulations can benefit several engineers, their use is often restricted to vehicle modeling experts that can navigate the complexities of configuring simulations on commercial software. The accessibility to vehicle simulations is further challenged by the need for computing power beyond the capability of their work laptops. The Vehicle Simulation Interface (VSI) is an in-house software platform based on MATLAB® designed to enable any engineer, irrespective of their simulation expertise, to configure and run vehicle simulations using a simplified GUI front-end with curated vehicle and load case inputs tailored to Rivian products. VSI leverages MATLAB object-oriented programming to create simulation objects that encapsulate simulation input data and can each call a different commercial software in the backend depending on the needs of the load case. Each simulation object is then distributed to a local or remote worker using Parallel Computing Toolbox™ and MATLAB Parallel Server™. This workflow allows users to run multi-software simulation jobs in parallel and scale computing resources with additional remote clusters. In this presentation, gain insight into how this platform powers quick decision making by allowing engineers to run simulations with ease and generate key metrics within minutes. You’ll also hear details on how various MathWorks products such as App Designer, Parallel Computing Toolbox, and MATLAB Parallel Server were instrumental in creating this platform.
Adithya Vignesh Jayaraman
Adithya Vignesh Jayaraman is a senior toolset engineer at Rivian Automotive with five years of experience conceiving and developing engineering tools in MATLAB. He leads the development of a company-wide vehicle simulation platform that is aimed at making full vehicle simulations accessible to all engineers. Prior to joining Rivian, he worked at Cummins Inc. as a systems analytics and modeling engineer for three years, where he focused on anomaly detection and statistical tools for fleet data analytics. He holds a master's degree in mechanical engineering from Purdue University and a bachelor's degree in mechanical engineering from Anna University.
How AI Trends Are Impacting Automotive Development
As AI becomes increasingly mainstream, new trends are emerging and impacting engineering design and development. While AI can enable significant productivity gains, incorrect implementation can lead to unnecessary complexity and marginal improvements. In this talk, Mary Ann Freeman, senior director of engineering for AI and data science for MathWorks, covers emerging trends in AI, tools in MATLAB® and Simulink® that MathWorks is developing in response—including for ROM, robust AI, and data-centric AI—and how you can apply them to improve your automotive engineered systems.
Mary Ann Freeman
Mary Ann Freeman is senior director of engineering for AI and data science. Her responsibilities comprise data science and predictive analytics, AI, and visualization products, including MATLAB graphics and data tools, app building, Statistics and Machine Learning Toolbox, Deep Learning Toolbox, Optimization Toolbox, and Symbolic Math Toolbox. Mary Ann joined MathWorks in 1995 as lead developer on Optimization Toolbox. She has been involved in the development and evolution of a number of MathWorks products, including MATLAB, Deep Learning Toolbox, Curve Fitting Toolbox, and Optimization Toolbox. Mary Ann received her Ph.D. in computer science from Cornell University. Her area of research was large-scale numerical optimization methods. Mary Ann holds additional degrees in mathematics and computer science.
Lunch and Technology Showcase
Women in Tech
The Women in Tech session brings together several leading women engineers from Hyundai Mobis, KPIT Technology, Ford Motor Company, and MathWorks, sharing their insights and contributions to the latest automotive trends including autonomous driving, software-defined vehicles, and electrification. This live session provides an opportunity to learn and be inspired.
Accelerating Model-Based Design Through Continuous Integration
Continuous integration (CI) is an agile methodology in which developers regularly submit and merge their source code changes into a central repository, which are then automatically built, tested, and released. CI plays a critical part in automating key parts of the Model-Based Design workflow, including verification, code generation, and testing. This approach enables developers to focus on developing new features, not on verifying features have been integrated correctly. Cummins and MathWorks developed a custom CI toolbox using object-oriented programming in MATLAB® and a production Jenkins® build automation server to automate Cummins’s entire Model-Based Design process including verifying AUTOSAR ARXML changes, checking compliance with industry modeling standards and guidelines, verifying requirements and coverage of model-in-the-loop and software-in-the-loop, performing design error detection using Simulink Design Verifier™, ensuring successful code generation, and proving the absence of critical run-time errors and applying industry code standard checking using Polyspace Bug Finder™ and Polyspace Code Prover™.
Jason Stallard
Dave Hoadley
Dave Hoadley is a principal technical consultant in the MathWorks office in Michigan. He specializes in supporting customers adopting Model-Based Design in their product development workflow. This focus regularly includes mathematical modeling and verification and validation of designs for medical devices, automotive, and aerospace and defense applications. Since 2015, Dave has also engaged with industry leaders, academia, and US FDA’s CDRH on the role that computational modeling will play in the future of regulating medical devices. Before joining MathWorks, Dave was a project scientist at VI Engineering, where he developed test, measurement, and process control applications for these same industries. Dave holds a B.S. in mathematics and astrophysics and a Ph.D. in condensed matter physics from Michigan State University and an M.S.E. in electrical engineering with a focus in control systems from the University of Michigan.
Battery Management Controls Using Simulink
Learn about the speaker's decade-long experience developing battery management systems and control algorithms using Model-Based Design across a diverse application stream.
Awad Syed Ali
Awad Syed Ali, director of systems engineering at Our Next Energy (ONE), is working on developing best-in-class controls algorithms and systems engineering. Awad has worked on energy storage solutions for over a decade; starting in 2011 when he was part of the controls team at Bosch that developed the BMS system for the Chrysler F500e. Awad joined the Advanced Engineering Group at Stellantis and successfully developed and productized battery management systems from the ground up. Awad has a Master of Science in electrical engineering from The Ohio State University, majoring in power electronics and controls systems. He is a strong believer in the philosophy that the sum of the parts always exceeds individual contributions.
What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
MATLAB®, Simulink®, and RoadRunner help engineers build automated driving systems with increasing levels of automation. In this session, you will discover new features and examples in R2022b and R2023a that will allow you to:
- Interactively design scenes and scenarios for driving simulation
- Generate 3D scenes from HD map data
- Generate driving scenarios from recorded sensor data
- Simulate driving applications like emergency braking, lane following, lane change, platooning, and parking valet
Div Tiwari
Div Tiwari is a senior product manager for automated driving. He has supported MathWorks customers in establishing and evolving their workflows forautonomous systems, artificial intelligence, high-performance computing, and other domains. Div holds B.S. and M.S. degrees in electrical and computer engineering and computer science from Cornell University.
Find C/C++ Bugs as You Code – Integrate Polyspace into Your IDE
Maximize your C/C++ code quality, safety, and security with Polyspace as You Code plugin. Integrate this tool into your IDE, such as Visual Studio, Visual Studio Code, or Eclipse, to detect bugs early in the development process. Verify code quality before committing changes to your source code repository and receive instant feedback on vulnerabilities. Adhere to industry standards such as MISRA and CERT C/C++ with ease. Experience faster software delivery and higher code quality by integrating Polyspace® code verification tools into your development environment.
John Boyd
John Boyd joined MathWorks in 2007 and has held various roles in the organization. His current focus is Polyspace code verification and its application to safety-critical systems. His prior experience includes technical and managerial roles building hardware-in-the- loop simulation systems for the aerospace industry. John holds an M.S in computer science from Purdue University.
Adaptive Design of Experiments for Simultaneous Modeling and Optimization with AI
This presentation covers the development of a closed-loop Design of Experiments (DoE) to replace traditional open-loop DoE so all the new design points are built based on the knowledge (real-time learning) of system behavior. MATLAB® was used to build a surrogate model of the system model and make decisions about test points to search for system nonlinearity and optimality. We have applied the method on some high-dimension systems and compared with commercial and empirical results to demonstrate the performance.
Yan Wang
Dr. Yan Wang has been with Ford Research since 2021, working on wide range of automotive control problems, including actuator design and mechatronics controls, adaptive and optimal control for traditional and electrified propulsion systems, and recently driver assist technologies. His main contributions are on the application and real-time implementation of advanced/modern control methods, including both model-based and data-driven controls. Dr. Wang has authored or co-authored more than 50 papers and is an inventor of over 50 patents.
Scenario-Based Modeling and Simulation
Automated vehicle and advanced driver-assistance systems (AV/ADAS) technologies present enormous and unprecedented challenges to vehicle product development. These challenges require new approaches, including consideration of interactions within its operating domain’s environments and other participants of the vehicle and its system, disruptive hardware technologies, human equivalent perception systems, exponentially growing software complexity, and an infinite number of road scenarios. In order to deliver an optimal and robust product to customers, very thorough verification and validation must be performed under all of those infinite number of scenarios.
While hardware-based development processes remain a crucial part of product development, such processes are no longer capable of meeting the exponentially growing verification and validation needs alone. As part of any effort to achieve a suitable verification and validation approach to AV/ADAS technology development, it is necessary to identify the critical tasks to assemble such a verification and validation process and the scenario modeling and simulation capabilities that must be realized.
Our goal is to support traditional verification and validation considerations, as well as to assess scenario coverage overall. What is the scenario-based simulation? How might we approach integration of scenario models and functional models in simulation?
In this presentation, we share those considerations in the hopes to drive further standardization and discussion across the industry and tool vendors alike. An example using MATLAB® and Simulink® is also presented as a case study.
Yuming Niu
Yuming Niu is a technical expert at Ford Motor Company. His interests include Model-Based Control Design, Model-Based V&V, Scenario-based Simulation, and software system integration. He previously worked John Deere and Caterpillar, Inc. and has been working in the automotive industry for more than 18 years. Yuming received his Ph.D. from Michigan Technological University.
Running MATLAB and Simulink in Continuous Integration
In this talk, discover how to architect and automate a workflow with scripts that trigger runners in a DevOps platform like GitLab®. This frees time for engineers to focus on technical challenges while improving the speed and repeatability of continuous integration processes.
Bernard Johnson
Bernard Johnson is an application engineer at MathWorks. For the last three years he has been helping clients implement practices in systems engineering, functional safety, V&V, and DevOps CI/CD. Before this, he led organizations to design, verify, and release embedded products into the automotive, electrification, appliance, heavy machinery, defense, and wind industries. As a leader, Bernard optimizes teams, processes, and tools for fast and innovative product development. Bernard is trained in PMP practices, TRIZ, Six Sigma, Lean, DFSS, and SysML. He is a system controls and simulation engineer who has been using MATLAB and Simulink since their introduction.
Reduced Order Modeling for Battery Thermal Analysis
High currents in lithium-ion batteries may lead to significant thermal gradients depending on the cooling strategy. Above a certain level, these gradients will adversely affect performance and durability. Moreover, computing these gradients requires detailed modeling of the battery cell, making the approach unsuitable for system-level simulation. This talk describes the analysis of battery thermal gradients using a combination of finite element analysis and system-level simulation using model reduction.
Javier Gazzarri
Javier Gazzarri has worked as an application engineer at MathWorks for 10+ years, focusing on the use of simulation tools as an integral part of Model-Based Design. Before joining MathWorks, Javier worked on fuel cell modeling at the National Research Council of Canada in Vancouver, British Columbia. He has a bachelor’s degree in mechanical engineering from the University of Buenos Aires (Argentina) and master’s and Ph.D. degrees from the University of British Columbia (Canada).
Scenario Harvesting Using Automated Driving Toolbox and RoadRunner Scenario
Scenario harvesting is a workflow for generating digital twin data from vehicle logs utilizing Automated Driving Toolbox™ and RoadRunner.
This workflow consists of multiple data processing tasks, including:
- GPS and IMU sensor fusion to generate ego trajectory
- Ego localization using lane detections and a high-definition map
- Target vehicle trajectory creation from recorded lidar, radar, and vision sensors data
RoadRunner Scenario exports the scenarios created to OpenSCENARIO, which is used for regression testing of the advanced driver-assistance systems (ADAS) and autonomous driving (AD) algorithms.
Krishna Koravadi
Krishna is Engineering Manager: Cloud Computing Tools (AL/ML Group) at Aptiv. He has several years of rich software development experience in automotive, telecom and communication domains. He contributed to 40+ US patents in the field of ADAS/AD technologies. He enjoys learning new technologies and apply the knowledge in creative problem solving
Seo-Wook Park
Seo-Wook Park is a principal application engineer at MathWorks, focusing on automated driving and advanced driver assistance systems (ADAS). He has developed various automated driving applications such as ACC/AEB with sensor fusion, highway lane centering and change, truck platooning with V2V, and a scenario creation from recorded data with RoadRunner Scenario. One of his recent focus areas is to develop urban autonomy, including traffic light following at urban intersections using V2X technology. Before joining MathWorks, he worked in passive and active safety electronics development at Autoliv, Bosch, and Hyundai Autonet for over 20 years. Seo-Wook has a Ph.D. in robotics and control systems from the Korea Advanced Institute of Science and Technology (KAIST).
Break and Technology Showcase
Building a Cloud-Based Digital Twin for an EV Battery Pack
Creating, validating, and correlating the model of a physical asset is important to building a digital twin, but modeling is only one aspect of the overall process of developing and deploying digital twins. In this presentation, we showcase a project from developing the model of an EV battery, deploying it to the cloud and connecting it to the data infrastructure, and predicting battery state of health based on data from a real-world electric vehicle fleet. Join us to learn about key considerations when planning your digital twin project.
Will Wilson
Will Wilson is an application engineer at MathWorks, where he focuses on data analytics, machine learning, and big data. Before joining MathWorks in 2015, Will spent 10 years working at Robert Bosch LLC on safety-related products, including occupant classification systems and airbag control systems. His experience at Bosch included systems engineering, airbag calibration, technical project management, and strategic marketing, with a focus on ADAS technology. Prior to Bosch, Will spent seven years working at Johnson Controls, where he designed and launched power seat track mechanisms. He holds a B.S. in mechanical engineering from Kettering University.
Rapid System-Level Analysis and Control Design for EV Thermal Management Systems
Thermal management system design and its control play a critical role in the development of electrified powertrains. As modern thermal management systems get more sophisticated and capable, the control system design becomes exponentially more complicated and requires realistic yet fast physical models to be successful. In this talk, see field-proven examples of the latest capabilities of Simulink® and the Simscape™ product family for modeling automotive thermal management systems and enabling control design with realistic physical plant models. Hear about modeling and parametrization of typical components in the thermal management system of an electrified powertrain and assembling them into a system-level model, which includes both coolant and refrigerant-based systems. You’ll also learn about the workflow of modeling the refrigeration cycle with different levels of fidelity and share resources available to support customers’ modeling efforts.
Yifeng Tang
Yifeng Tang is a senior application engineer at MathWorks. He supports MATLAB and Simulink for multi-domain physical modeling and specializes in the modeling and simulation of mechanical, thermal, and fluid systems, such as hydraulic systems, thermal management systems, and fuel cells. Prior to joining MathWorks, he worked for Ford Motor Company in the Powertrain Research Department. Yifeng earned his Ph.D. in mechanical engineering from the University of Michigan and a B.S.E. in mechanical engineering from the University of Michigan and Shanghai Jiao Tong University.
Andrew Greff
Andrew Greff is a senior application engineer at MathWorks. He specializes in physical modeling using Simscape and focuses on thermal, fluid, and multibody systems. Before joining Mathworks, Andrew worked for GM and Stellantis developing advanced hardware and controls for engines. He obtained his PhD in mechanical engineering from the University of Alabama.
Lateral Control of Truck Platooning with RoadRunner Scenario
This session expands on “Vehicle Platooning Controller with V2V Communication,” presented at the 2022 MathWorks Automotive Conference, by adding a lateral control to follow a lane center of a curved road while maintaining a predefined space between vehicles in a platoon. The lateral control has been implemented using linearized truck-trailer lateral dynamics and model predictive control. The closed-loop control system includes vehicle-to-vehicle (V2V) communication and a high-fidelity 6-DOF vehicle dynamics model of a three-axle tractor towing a three-axle trailer. Using RoadRunner Scenario enables the creation of various test scenarios and cosimulation with the Simulink model implementing the platooning control.
Seo-Wook Park
Seo-Wook Park is a principal application engineer at MathWorks, focusing on automated driving and advanced driver assistance systems (ADAS). He has developed various automated driving applications such as ACC/AEB with sensor fusion, highway lane centering and change, truck platooning with V2V, and a scenario creation from recorded data with RoadRunner Scenario. One of his recent focus areas is to develop urban autonomy, including traffic light following at urban intersections using V2X technology. Before joining MathWorks, he worked in passive and active safety electronics development at Autoliv, Bosch, and Hyundai Autonet for over 20 years. Seo-Wook has a Ph.D. in robotics and control systems from the Korea Advanced Institute of Science and Technology (KAIST).
End of Event
Emily Foster
Emily Foster is a research engineer at Ford Motor Company. She works within the Driver Assistance Technology Modeling and Simulation Group. For the last four years, she has been involved in feature modeling, toolchain integration, and tool creation. Emily holds bachelor’s and master’s degrees in mechanical engineering from Lawrence Technological University and is a Ph.D. candidate at Oakland University.
Lincy George
Lincy George is a software engineer at Hyundai Mobis Technical Center of North America in the Autonomous Vehicles department. She is product owner leading efforts on a low-speed platooning project. She previously worked as an individual contributor on in-cabin monitoring projects and a L3 autonomous driving project. Before joining Hyundai Mobis, she spent 3 years working for Tata Consultancy Services, focusing on software development for image processing applications. She holds an M.S. in electrical engineering from University of Michigan, Dearborn.
Deepa Ramaswamy
Deepa Ramaswamy, Ph.D., is the chief engineer for Advanced Electrical Architecture, at Ford Motor Company. She started her career at Ford, where she led the development of the Vehicle System Controller on the 2004 Ford Hybrid Escape, which was America’s first production hybrid electric vehicle. For her work on this, she and her colleagues were awarded the prestigious Henry Ford Technology Award. Deepa left Ford in 2007 to pursue other opportunities, which included working at Ricardo, running her own company Hybrid Chakra Consulting, working at Infosys, and then being the Director at LG Chem Power Inc. for Battery Controls and Software. In 2017, Deepa happily returned to Ford and led the overall delivery of advanced driver assistance systems (ADAS) software for the Ford F-150 and Mustang Mach-E vehicles, for which she and her colleagues won an Executive Innovations Award. Since 2021, she has worked on advanced electrical architectures at Ford, focusing on projects that enable the development of software-defined smart vehicles in a smart world. She has a B.Tech. from the Indian Institute of Technology, Madras, and an M.S. and Ph.D. in electrical engineering from the University of Illinois at Urbana-Champaign.
Sophia Suo
Sophia Suo is a client solution partner at KPIT, leading holistic technical solutions and engineering services development for U.S OEM/tier 1 clients in the transformation to software-define vehicles (SDVs). She was previously vice president, Electrification/eMobility Practice at KPIT, leading technical solutions/engineering service development for global customers in emobility and electrification. She has held various engineering leadership positions for wide range of automotive electronics products development, e.g. conventional powertrain, chassis (brake, steering, driveline) controls, body control and interior electronics (audio, cluster) products, and X-by-wire technology development. Sophia has over 14 years of experience focused on electrified powertrain, and launched various EV/PHEV-related products for global automotive OEMs and tier 1s. Prior to KPIT, Sophia worked at Eaton Corporation, Ford Motor Company, Visteon Corporation, Lear Corporation, and Delta Electronics Inc.
Purvi Limaye
Purvi Janani Limaye is a technical account manager at MathWorks. Prior to joining MathWorks in 2023, Purvi spent 11 years working as a senior manager at IAV Automotive Engineering, where she led propulsion system development for gasoline and electrified powertrains. Prior to IAV, Purvi worked at Ford Motor Company, leading the development of hybrid controls, and at General Electric, driving development of controls software for diesel locomotives. Purvi holds an M.S. in electrical engineering from The University of Toledo.
Demo Stations
- AI in Engineering
- Automated Driving
- AUTOSAR
- Continuous Integration/Continuous Delivery
- Digital Twin
- MATLAB and Simulink Training
- Polyspace Code Verification Products
- Simscape Battery and System Cooling
- Software-Defined Vehicles
- Vehicle Network Toolbox
- Virtual Vehicle
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