Plenary Session: Software Transformation
The three main concerns for buying EVs are range, cost, and charging. Energy consumption is the key driver for all three, and therefore is the main focus of Lightyear’s mission to deliver clean mobility for everyone.
To achieve roughly half of the energy consumption of current EVs, Lightyear has developed the Lightyear 0 solar car from the ground up, including 4 motors directly in the wheels and 5 square meters of solar panels. Besides an overview of where the company stands today, how they got there, and a peek into where they're going, Lightyear’s CTO will dive into the Model-Based Design process that enabled this radical reduction in energy consumption, the technology that resulted from it, and how MATLAB and Simulink are used along the way.
MAN develops commercial vehicles that are—thanks to digitalization—fully connected software and server platforms. In this keynote, learn about the possibilities of the current systems and how the development process is enabling those possibilities. New boundary conditions are leading to a new development approach. Hear about different perspectives of the approach and the roles of Android Automotive, ROS, and Adaptive AUTOSAR. MAN is in the last step of transformation towards becoming a software company.
Transforming current automotive business models requires vehicles to be software-defined and a fresh take at how that software is built. Model-Based Design is already being leveraged with containers and continuous integration pipelines to design, test, and deploy software-defined systems on modern vehicles. Hear how MathWorks is partnering with the industry to extend its offerings—making them more turnkey to support methods and process in DevOps.
Explore how your longstanding journey with Model-Based Design can help Mercedes-Benz to tackle future challenges.
- The first production-ready, auto-code-generated software functions developed with Model-Based Design in Mercedes-Benz series cars in the early 2000s
- Initial ideas and vision of virtual distributed system simulations
- Roadmap towards an integrated tool chain based on an automotive standard
- Implementation steps
- Roadmap and results
- Success factors
Dr. Thomas Ringler, and
Dr. Florian Wohlgemuth,
Our driving experience will soon be defined by the software running in the car and in the cloud. A new holistic approach for software architectures based on services and service-oriented communication is emerging. This approach enables continuous development and deployment of innovative software features and makes new types of collaboration possible between OEMs and software platform providers. Learn how the Simulink® product family is evolving to be a technology accelerator for software-defined vehicles. Discover new capabilities to model, simulate, and deploy models to modern service-oriented architectures.
Simulink® enables users to model and simulate different time-dependent systems before going into production. To accelerate the development cycle, automotive engineers are increasingly incorporating real-world data streams directly into models from the earliest stages. Data Distribution Service (DDS) is a publish/subscribe middleware used in production-grade automotive distributed systems that provides the data backbone for systems to be accurately modeled with the same inputs and outputs from real-world conditions through Quality of Service (QoS) configuration. DDS Blockset provides apps and blocks for modeling and simulating software applications. DDS blocks can be added to Simulink models, enabling the connection to other components through the DDS communication framework. This allows for the introduction of QoS into the earliest phase, to reduce project risks and costs of system development. Get an introduction to DDS and learn how to work with DDS Blockset to simulate and implement real-time communications between Simulink applications and other DDS-based applications.
Faster delivery needs and rising software complexity are common trends in automotive organizations. This raises numerous challenges for conventional software development processes. Questions to be answered during this transformation include:
- How can agile teams develop a comprehensive issue detection process?
- How can software architecture and quality be improved while still adhering to industry standards like ISO 26262?
- How can advanced workflow options be leveraged with cloud platforms?
In order to answer the questions above and to improve DevOps metrics, you’ll learn about best practices for interactive and automated development during this presentation. Best practices include continuous integration workflows and a shift-left approach using Model-Based Design and Polyspace® in your software factory. You’ll also discover methods for lowering the risk of delayed software delivery and increasing confidence in software quality.
As part of the Women in Tech initiative, MathWorks will be hosting a Women in Tech lunch during this year’s MathWorks Automotive Conference, which is intended for female delegates and presenters. Join the lunch to hear from leading technical experts and to discuss your experiences. Use this opportunity to meet and network with other female industry peers.
Electrification and Virtual Engineering
The worldwide investment to businesses involving carbon neutrality and decarbonization has become a priority after the Kyoto Protocol in 1997 and the Paris Agreement in 2015 for the prevention of global warming. Hydrogen (H2) energy is considered one of the most promising alternative energy sources to fossil fuels from the viewpoints of storability, portability, and productivity from a variety of energy sources such as solar power and wind power.
The fuel cell (FC) system plays a main role in the enhancement of H2 utilization due to high efficiency compared to conventional internal combustion engines. FC system manufacturers demand the development of a variety of system products for automotive, stationary, railway, marine, and aviation purposes. Despite such an expectation, development of FC system products is conducted by trial and error because the behaviors of the FC system are highly complex. Significant cost and efforts are required for manufacturing the prototypes, calibrating the controllers, and testing the system responses. It is one of the highest barriers for entry to the FC industry and enhancement of H2 utilization. From such backgrounds, implementation of model-based development to the entire FC system development process is demanded by the FC industry. In Model-Based Design, the specification of the system components and controllers can be determined by considering the interactions among them before manufacturing the prototypes.
Though studies of the entire FC system are important, the fuel cell itself has been more intensively investigated. Less research has been done on the FC system including the FC stack; the system components of air, H2, and cooling subsystems; and the FC system controllers. In addition, a system simulator that can estimate the dynamic behavior of the FC system—and can take the interactions among the FC stack, FC system components, and controllers into account with an acceptable computational speed and accuracy—has not been proposed yet. The objective of this study is to develop the integrated system simulator, including the FC system component models and controllers in the entire FC system, which can be utilized as a design platform for the wide range of applications such as automotive, railway, marine, aviation, and stationary power generation purposes. The authors have developed the one-dimensional (1D) FC system model including the 1D physical models of the FC stack in previous research and the FC system components of air, H2, and cooling subsystems. The developed simulator can estimate the dynamic behavior of the entire FC system with acceptable computational speed and accuracy. It is possible to modify the simulator according to the system requirements of the various applications.
Companies want to make more use of virtual development to reduce time-to-market and development costs. Building physical vehicle prototypes and testing them under different climatic conditions is expensive, and it takes a long time for prototypes to become available. Virtual vehicles offer the possibility to start development and verification/validation much earlier ("shift left"). However, some of the technical challenges are to integrate many plant models from different physical domains as well as software models, to select appropriate fidelity for the required analysis, and to make the models and tools available to a wide community. MathWorks offers solutions to overcome these challenges, allowing you to get started quickly, continue working on a trusted and shared platform, be flexible, and scale via the cloud. Learn how to perform some of the key workflows for building an electric virtual vehicle and moving from desktop studies to large-scale studies in the cloud.
A Modular Approach to Physical Modeling Using MATLAB, Simulink, and Simscape for Automobile System Modeling
Learn how Volvo Cars designed a robust modular simulations platform for simulation, analysis, and optimization of engineering systems with dynamic control blocks. The systems are modeled to accommodate multifidelity models based on the depth of physics and the purpose of application. Based on the requirements, one can use a 2D or a 3D model within the same architecture at the expense of time for better resolution in specific parts of the systems. The components in the system are modeled for continuous development (CD) and continuous integration (CI). The system is composed of vehicle dynamics, transmission, electrical, and thermal systems. The systems are modeled using the conservation principles of classical physics.
Creating these models and systems in this fashion helps in estimating energy efficiency, performance, and design validation in all stages of the vehicle development process. The systems and modular approach allow for a single platform used for multiple dedicated purposes, such as SIL and HIL testing. The solution execution times are so small that a large combination of vehicle configurations can be simulated—reducing the CAE cost, energy usage, and CO2 emission and generating data. In the future, the collected data will be used with machine learning to improve efficiency in real-time drive.
Advanced technologies such as artificial intelligence offer new opportunities to improve existing software development processes in a modern vehicle. Oftentimes such improvements can be accomplished through exact knowledge of critical vehicle states and inputs. Using physical sensors for such tasks can be expensive or even impossible, and the implementation of an alternative virtual sensor using artificial intelligence offers significant advantages. However, many times the deployment to embedded hardware can be challenging. Typically, memory footprint is crucial on embedded hardware and production code needs to be optimized. In this session, see how a virtual pressure sensor, developed with a recurrent neural network, can be implemented in a production code generation tool chain using only fixed-point datatype. The newly implemented workflows are fully automated and were developed in a joint project between MathWorks and Mercedes-Benz.
See a practical case study showing that a virtual software development environment and testing methodology, free from the constraints of hardware lab setups, can accelerate development time.
The key in this environment is the need to “shift-left” development activities. This shift becomes important as performance and efficiency are challenging the OEMs, pushing the need to make decisions early in the design cycle. Learn about the need for a virtual hardware-based Software Development Kit that can allow early algorithm studies of the control system from concept through to binary optimized control software implementation.
Where the automotive ‘V’-cycle traditionally uses simulation at the early stages of product definition with MIL and moving to SIL, the HIL stage has many dependencies and access issues. It is necessary to have a more complete software development kit on a virtual target early in the design cycle to accelerate development and ensure quality before reaching the board. Through a practical EV motor control case study, discover the concept of a virtual hardware-in-the-loop (vHIL) SDK environment, which allows a Synopsys® virtual prototype of the Infineon® TC4xx MCU to be coupled with other simulators (in this case Simulink®) to form a fully virtual, high visibility closed loop simulation with plant models. It also allows a highly efficient environment with which to develop, test, and verify binary target software that is ‘board-ready.’
Automated Driving and ADAS
Bosch Engineering presents an ADAS platform that allows test activities, first calibration of features participated on different ECUs, and includes an automated tool chain for scenario execution and evaluation of measurements. This talk will also show:
- Radar-based ADAS features and virtualization of Bosch ECUs
- A customer-specific solution for SIL tests as a virtual platform on a vehicle simulator
- Integration of many Bosch components to create a complete ADAS system in a virtual environment
Building automated driving systems is a complex task that spans multiple disciplines. Discover new features and examples in R2022a and R2022b that will allow you to:
- Create scenes and scenarios for driving simulation
- Simulate sensors for automated driving applications
- Design planning, control, and detection and tracking algorithms
- Deploy to C, C++, GPU, and ROS
There are a large variety of AI learning frameworks. If you are interested in a particular convolutional neural network, you are restricted to the framework it was originally developed in. Often Docker containers are used to run different networks at the same computing hardware. When running different networks into a test vehicle, a standardized way of deployment is mandatory instead of maintaining different Docker containers with competing requirements to the GPU driver and libraries. It is a comfortable handling of the complete vision stack, including image acquisition, network inference, and all preprocessing and postprocessing steps.
MATLAB® and Simulink® provide many image processing functions and supports to run neural networks based on the Open Neural Network Exchange (ONNX) format—an established standard in the community. Furthermore, the capability of C/C++ code generation is beneficial for in-vehicle usage.
In this presentation, see different deployment options using CPUs, GPUs, standard PCs, or embedded devices.
Learn how to design a controller for vehicle platooning applications with vehicle-to-vehicle (V2V) communication. Every following vehicle in a platoon maintains a constant spacing from its preceding vehicle. Vehicles traveling in tightly spaced platoons can improve traffic flow, safety, and fuel economy. Each vehicle obtains the position and movement information of the other vehicles in the platoon wirelessly via the V2V communication. A given acceleration profile drives the lead vehicle, and every trailing vehicle follows the lead vehicle while maintaining a predefined space by a platooning controller.
The development of reliable automated driving software depends on the test environment. During software development, a large number of corner cases need to be tested and additional cases have to be discovered. For this reason, the use of simulation is vital to perform a rigorous, controlled, and extensive testing of the vehicle operating conditions.
In this master class, see how you can use MATLAB®, Simulink®, and RoadRunner to design and simulate realistic driving scenarios and explore:
- Designing scenarios with Automated Driving Toolbox™
- Building 3D scenes with RoadRunner
- Designing scenarios interactively using RoadRunner Scenario
- Creating RoadRunner scenarios variations programatically
- Performing co-simulation of RoadRunner with Simulink
Christian Dziobek is the senior development engineer for body and comfort functions at Mercedes-Benz. He has over 20 years of experience in model-based software development in the automotive industry and introducing AUTOSAR methodology for series production of body and comfort functions. Christian has a diploma of electrical engineering from RWTH Aachen.
Dr. Tjorben Gross
Dr. Tjorben Gross is team lead for the Automotive Application Engineering Team at MathWorks Germany. He works with customers in the areas of functional safety (ISO 26262) and cyber security (ISO/SAE 21434) while integrating with DevOps concepts like CI. Before joining MathWorks, he was involved in different development projects at Fraunhofer ITWM. He holds a Ph.D. in mathematics from the TU Kaiserslautern.
Skanda Naglapur Ramamurthy
Skanda Naglapur Ramamurthy is an application engineer for Polyspace in the EMEA region, supporting automotive accounts at MathWorks Germany. His main assignments include supporting advanced software verification and validation workflows, CI/CD automation, and compliance with safety and security requirements. Before joining MathWorks, he worked in embedded application software development at Continental Automotive, IAV, and MAN Truck & Bus. He holds a master's degree in embedded systems and microelectronics from Darmstadt University of Applied Sciences.
Kyoto University and Toyota Motor Corporation
Shigeki Hasegawa is a project-specific assistant professor at Kyoto University, focusing on the development of fuel cell system simulators. He is also a project manager with Toyota Motor Corporation and has 18 years of experience with power train, system, and controller development of fuel cell electric vehicles (FCEV).
Dr. Irina Kaiser
Dr. Irina Kaiser is an expert for simulation in ADAS projects at Bosch Engineering. She earned a double master’s degree from MEI TU Moscow and TU Illmenau and a Ph.D. from TU Illmenau.
Dr. Hugo de Kock
Dr. Hugo de Kock
Dr. Hugo de Kock is a senior application engineer at MathWorks Germany. He engages with customers to understand their project goals and elicit their requirements. He develops and presents effective technical solutions using MATLAB and Simulink. Before joining MathWorks, Hugo was involved in different development projects in the automotive industry in Germany. He holds a Ph.D. in electrical engineering from the University of Stellenbosch in South Africa.
Dr. Jan Janse van Rensburg
Dr. Jan Janse van Rensburg is a senior virtual vehicle specialist at MathWorks Germany. He works with customers to produce proof-of-concept projects. Before joining MathWorks, Jan was active in various research and development projects focused on topics such as additive manufacturing, nuclear power, and specialized vehicles. Jan holds a Ph.D. in mechanical engineering from the North-West University in South Africa.
Simone Hämmerle works as a product specialist for ADAS/AD at MathWorks. Her focus is on scene and scenario generation, perception, planning, and ROS. Before joining MathWorks, she worked as a research assistant for the chair for Image Understanding and Knowledge-Based Systems at Technical University of Munich. Simone holds a diploma degree in computer science from the Augsburg University of Applied Sciences and a master’s degree in biomedical engineering from the Furtwangen University of Applied Sciences.
Dimitri Hamidi is a senior application engineer at MathWorks focusing on FPGA design, image processing, and sensor fusion and tracking. Prior to joining MathWorks, he was as a research associate at the German Aerospace Center (DLR) and worked as an algorithm engineer in Continental’s advanced engineering department for ADAS and AD. Dimitri studied electrical engineering at the Technische Universität München.
Sriram Mandayam is a senior CAE engineer at Volvo Cars, focusing on sustainability and efficiency. He has worked as a CAE engineer in climate, electric propulsion, and as a CFD engineer. Sriram has a master’s in engineering mechanics from KTH Royal Institute of Technology.
Dr. Stephan Kirstein
Dr. Stephan Kirstein is a senior software developer with a focus on data processing of camera, radar, lidar, and ultrasonic sensors. He has worked on computer vision and environment perception in the context of static and dynamic obstacle detection since 2011. Stephan earned a Ph.D. in Neuroinformatics. He was awarded the Best Paper Award ICONIP 2008 (Life-Long Learning of Categories), Continental Gold Award 2015 (Automated Parking), and Continental Gold Award 2018 (Valet Parking).
Katja Deuschl is an AI developer in the division of eDrive at Mercedes-Benz. She is an embedded software expert and has a diploma in computer science.
Dr. Gaspar Gil-Gomez
Dr. Gaspar Gil-Gómez is a senior automotive engineer at MathWorks. He advises automotive teams in northern Europe to accelerate the digitalization of their R&D organizations and works with engineers and senior managers to identify the right solutions in software factories, autonomous driving, electromobility, simulations, and data science. Before joining MathWorks in 2020, Gaspar worked at ESA, Volvo Cars Group, and McLaren Racing. His experience extends from developing initial hardware-in-the-loop (HIL) solutions, scenes, and scenarios for AD/ADAS to project leading an ultimate driver-in-the-loop (DIL) racing simulator. Gaspar holds a Ph.D. from KTH in Sweden.
Dr. Mohammad Abu-Alqumsan
Dr. Mohammad Abu-Alqumsan is the product manager for the IEC Certification Kit at MathWorks. He focuses on functional safety and consults with industry participants on qualifying tools and developing workflows that comply with popular certification standards such as ISO 26262, SOTIF, ISO 21434 and Automotive SPICE. Before joining MathWorks, he worked at Validas AG as a project manager and research software engineer. Mohammad has a doctorate degree in brain-computer interfaces and robotics from the Technical University of Munich.
Infineon Technologies India
Dineshkumar Selvaraj leads the virtual prototype development of the AURIX family of microcontrollers at Infineon Technologies. He has over 17 years of experience in ESL domain, focusing on the development of VP for classical pre-silicon software validation, RTL co-simulation, and early performance analysis and optimization using models. Prior to Infineon, he worked with Intel and Tata Elxsi. He studied electrical engineering at NIT, Trichy.
Gernot Schraberger is a principal application engineer in the Munich office of MathWorks with 14 years of experience. His main application focus is on electrification, physical system modeling, and control design. Prior to joining MathWorks, he worked as a development engineer for motor control and embedded software engineering in the machinery industry for electronic assembly systems. Gernot holds a master’s degree in automation engineering from the Technical University of Munich.
Dr. Lorenzo Nicoletti
Dr. Lorenzo Nicoletti is an application engineer in the MathWorks Munich office. His main application focuses are electrification, virtual vehicles, and physical modeling. Prior to joining MathWorks, Lorenzo collaborated in a research project with AUDI AG focusing on the parametric modeling of battery electric vehicles. In the scope of the project, he obtained a Ph.D. in mechanical engineering from the Technical University of Munich. Prior to the Ph.D., he obtained an M.Sc. in mechanical engineering and an M.Sc. in automotive engineering from the Technical University of Munich.
Maxime François joined MathWorks France in 2022 as an application engineer. His areas of expertise cover topics related to autonomous systems, simulation, and V&V. Maxime previously worked in the Engineering Development Group at MathWorks UK. He graduated from INSA Lyon with a degree in mechanical engineering.
Dr. Marko Gecić
Infineon Technologies AG
Dr. Marko Gecić is a principal application engineer at Infineon Technologies AG, working with automotive microcontrollers with a focus on electrical drives. Prior to Infineon, he worked at Torqeedo GmbH developing low voltage electrical drives and range extenders, and was a researcher and teaching assistant with Faculty of Technical Science, University of Novi Sad, Serbia. Marko received his B.Sc., M.Sc., and Ph.D. degrees in electrical and computer engineering from Faculty of Technical Science, University of Novi Sad, Serbia.
Arjo van der Ham
Arjo van der Ham is co-founder and chief technology officer of Lightyear, a Dutch company commercializing solar-powered electric vehicles. He is responsible for engineering, research, testing, and intellectual property and focuses mostly on core technologies and design process. Arjo holds an M.Sc. in power electronics from the Eindhoven University of Technology.
MAN Truck & Bus
Stefan Teuchert is senior vice president of research and development and is responsible for the development of electric/electronic and software, including automated driving, at TRATON SE and at MAN Truck & Bus. In 2016, he worked with the Digital Venture GmbH in Berlin to generate new digital business models and founded new companies (startups). From 2009 to 2016, Stefan lead function and software development for powertrain (including ePowertrain), connectivity, and driver assistance systems. During that time, he built up in-house software development and restructured the organization to an IT house with a clear separation between hardware and software. From 2004 to 2009, he was responsible for testing, base software, and tool development. He started his career in the DHL Logistic Group optimizing routes with mathematical and heuristic approaches.
Penny Anderson is the director responsible for mathematical and data science functionality in MATLAB and several mathematical toolboxes, including Optimization Toolbox. Her current focus is DevOps for MATLAB and Simulink. Penny joined MathWorks in 1996 as a software developer. Her contributions include key features and enhancements to the MATLAB product family, including Parallel Computing Toolbox. She has an M.Sc. in computer science and a B.Sc. in honors mathematics from McGill University in Canada.
Tunc Simsek is the senior development manager for architecture modeling platforms. Prior to joining MathWorks in 2004, he performed research on distributed and networked control systems with a focus in anytime channel coding. He holds an M.S. and a Ph.D. from the University of California, Berkeley.
Angel Martinez Bernal
Angel Martinez Bernal works in the RTI CTO office, where he leads initiatives to improve the RTI family of products from the user’s point of view. He also focuses on connecting the Data Distribution Service (DDS) standard with other technologies. Angel is the lead RTI engineer for the integration of the RTI Connext software framework with Simulink and DDS Blockset.
Angel joined RTI in 2014 for an internship to leverage the code examples from the RTI Community. He became part of the RTI Tools development team and served as the technical lead of the RTI DDS Toolkit for LabVIEW. He studied computer science engineering at the University of Cordoba and received his master’s degree in computer and network engineering at the University of Granada.
Dr. Roxana Daniela Florescu
Continental Automotive Romania SRL
Dr. Roxana Daniela Florescu is a senior software developer in computer vision and visualization for autonomous driving systems. She is also a lecturer at Technical University "Gh.Asachi" of Iași since 2017. Roxana earned a Ph.D. in electrical engineering.
Eva Pelster works as a senior application engineer and supports customers in the areas of physical systems modeling and Model-Based Design. Over the last several years she has supported Women in Tech efforts at local MATLAB EXPO and MathWorks Automotive Conference events. Eva joined MathWorks in 2013 as a training engineer and prior to MathWorks worked for an engineering consulting company focusing on modeling and simulation projects. Eva holds a degree in aerospace engineering from the University of Stuttgart.
Advait Valluri works as an application engineer for ADAS/AD at MathWorks. His focus is on providing workflow solutions to customers using RoadRunner and Automated Driving Toolbox. Prior to joining MathWorks, Advait worked for eight years as a product and project manager in various roles at AUDI AG within the domains of vehicle dynamics and automated driving. He has a master's degree in automotive engineering from RWTH Aachen, Germany, and a bachelor’s in mechanical engineering from Osmania University, India.
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