Tuesday, April 5, 2022
Software-Defined Vehicles: Developing Service-Oriented Applications with Simulink
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.
Join this session to learn how you can use Simulink® to design, simulate, and deploy applications into these new software architectures according to industry standards such as AUTOSAR Adaptive, DDS, and ROS.
Automotive DevOps for Model-Based Design with AWS, NXP, and MathWorks
Vehicle features and capabilities are transitioning from being mostly mechanically defined to being software defined. OEMs are adopting agile software development methods to update and maintain this software, driving the need for DevOps. NXP Semiconductors, MathWorks, and AWS have collaborated on a DevOps solution for Model-Based Design utilizing advanced vehicle control algorithms. In this talk, NXP will present the full cloud-to-vehicle solution built with AMS CodeSuite services by using MathWorks design tools targeting NXP automotive processors. This presentation also covers NXP® Model-Based Design Toolbox supporting code execution and profiling on the NXP S32 GreenBox II and NXP S32G GoldBox.
Model-Based Design Meets CI: I Connected Simulink with My CI System—What’s Next?
Many development teams have started integrating Model-Based Design into their continuous integration (CI) pipeline. What’s next? Learn how to optimize the interactive development practices to increase CI flow rate, maximize automation in the pipeline, and plan the extension of CI to DevOps.
Challenges and Successes in Migrating Legacy Software Modeling to a Simulation-Based Product Development System
To meet the demands of the modern on-highway market, engineering teams are moving towards agile development with a continuous integration and delivery development methodology. To adopt this new methodology, sometimes evolutionary changes in product and process aren’t enough and a revolutionary approach must be embraced, including knowing which parts of 100 years of development are integral to the company and which parts are holding it back from growth. Cummins has been reshaping its product and process to meet these challenges, targeting more effort on simulation and architecture with an initiative called simulation-based product development. MathWorks products and services are a strategic part of that vision. We will discuss how key enablers like executive sponsorship and vision, AUTOSAR, CICD, and “simulate everything” are helping to cement change and surmount challenges in culture, legacy technical debt, and skill sets.
Turning the Tables on Validation with Agile Model-Based Design
The growing complexity of today’s products—fueled by ever-increasing software content and coupled with shrinking timelines for delivering new products—requires engineers to “turn the tables” on existing workflows. The core principle of agile Model-Based Design centers on the idea of reducing rework. Typical workflows result in on-product validation at the end of the development cycle. Requirement issues found at that point drive rework—often taking the design through the entire development cycle again. Early integration and validation through agile Model-Based Design provides an opportunity to minimize rework through up-front, rapid iteration on architecture and requirements.
Building the Digital Thread from MBD to MBSE to Meet ISO 26262 for Embedded Software
Adoption of ISO 26262 has led to the expansion of development methodology from hand code to Model-Based Design. A number of process improvements were identified to achieve traceability and thread pulling across the system engineering process, including connecting architecture models and implementation models, implementing traceability between requirements and models, and understanding model architecture’s implication on impact analysis. This presentation will provide an early overview of the solution that involves an integrated Model-Based Design-Model-Based Systems Engineering workflow, limited duplication of sources of truth, establishment of traceability and coverage with a requirement management tool, and a componentized modeling style.
Addressing Common Inefficiencies When Targeting AUTOSAR and ISO 26262 with Simulink
Although the AUTOSAR standard has included constructs to support functional safety concepts since its inception, the automotive functional safety standard ISO 26262 came later from a different community. As a result, an engineering team needs to understand the gaps between these two standards when applying both at the same time. If done appropriately, the team can reduce the effort required for achieving compliance. However, finding the right solutions could be time consuming and done at the expense of engineering time on the product. In this presentation, explore a set of best practices in AUTOSAR for adhering to ISO 26262, distilled from working with automotive engineers in the last 5 years, covering architecture, data management and transfer, and workflow.
Effective Model-Based Development Strategies for ASPICE and Safety Compliance
Overcoming the challenges of effective software architecting and detailed designing in hybrid model-based and code-based environments, and doing so in a compliant manner, requires strategic decisions among the project team. These strategies, as well as compliance, are often not well formed or understood by project teams, and the answers are complicated in hybrid environments. This session will demonstrate how model-based development can be compliant in similar fashion to hand code but also have distinct advantages, especially when utilizing key features of Simulink®.
Faster Software Delivery with Polyspace in Your Software Factory
Increasing C/C++ software content and faster delivery requirements are common trends across vehicle software development organizations. Static analysis is a critical technique for verifying that the code meets quality, safety, and security goals. Learn about the benefits of using static code analysis at each step of the software development workflow, beginning with coding and during code integration. See how Polyspace® code verification tools can be integrated into developers’ IDE and software factories to enable faster software delivery and higher quality code.
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, you’ll explore a project which spans 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. You’ll also learn about key considerations when planning your digital twin project.
Thursday, April 7, 2022
Battery SOH and SOC Estimation Using a Hybrid Machine Learning Approach
KPIT developed a hybrid approach to overcome the shortcomings of existing individual methods for SOC and SOH estimation. It combines a battery model and a neural network to predict SOC and then uses the obtained SOC to derive SOH parameters. Deep Learning Toolbox™ and MATLAB® were used to train a feedforward neural network, which was then extensively validated for robustness. The neural network was then incorporated into Simulink® and deployed to a PowerPC-based embedded platform using Embedded Coder® and AUTOSAR Blockset. This workflow has been validated on multiple datasets for LFP and LCO chemistry. It has provided SOC and SOH estimation with improved accuracy well within +- 5% consistently over a different driving cycle range.
Developing Onboard SOH Estimation Using DVA and ICA for LFP Batteries
In this session, see how we developed a high accuracy onboard battery state of health estimation method based on the differential voltage (DVA) and incremental capacity analysis (ICA) for electric vehicles. Using cycling data from lithium-ion battery cells at various temperatures, we extracted the charging cycles and calculated the DVA and ICA curves, which are then filtered with an IIR-filter to reduce noise. Multiple features (i.e., peaks or valleys of the curves) are extracted and analyzed, and the most promising features are selected for further steps. The selected features are brought into correlation with the capacity fade, and a linear regression model is calculated between the selected features at various temperatures. With these linear models, a 2D Look-Up Table (LUT) is created by interpolating the values between the linear models. For the onboard implementation, we developed a Simulink® model which realized the calculation and filtering of the ICA- and DVA-curve. Also, we implemented a feature detection algorithm that detects and verifies the selected features, which are forwarded to the 2D LUT to calculate the current SOH. We tested and converted this model to AUTOSAR-compliant code and will validate it on Gotion’s in-house developed BMS.
Onboard Battery Pack State of Charge Estimation Using a Trained Neural Network
Using battery cell charging data stored in Gotion’s cloud data platform, we train and validate a neural network to estimate pack state of charge (SOC) during vehicle charging with the Deep Learning Toolbox™ and in-house data query API. We create an onboard SOC estimation strategy in Simulink® using the trained neural network. Afterward, we verify the algorithm’s ability to meet functional requirements using Simulink Test™ and deploy it as a “shadow” strategy within an existing AUTOSAR SOC software component. The impact on CPU and memory resources of the microcontroller is evaluated first. Then, we evaluate the neural network-based SOC estimator on test vehicles and find that the results (< 3%) are promising.
AI Workflows for Battery State Estimation
State of charge (SOC) estimation is among the most important tasks of a battery management system (BMS). SOC estimation is typically performed by current integration or using a Kalman filter. In this session, we will describe an alternative method based on AI. A deep neural network is trained to predict SOC based on voltage, current, and temperature measurements. The resulting network is then implemented in Simulink® and incorporated into a closed-loop BMS model. Finally, C code is automatically generated from the net for hardware implementation on an NXP S32K3 board used in PIL mode.
Building a Virtual Vehicle for Large-Scale Simulation Studies
The importance of virtual vehicle simulation has only grown as the automotive industry shifts to a more electrified, autonomous, and connected world. Their impact across organizations, however, is limited by the challenge of building models that can answer engineering questions with acceptable accuracy and speed. Building the vehicle model is only the start, and the platform must support the need to scale up the simulation studies needed for engineering decision making. Discover recent developments at MathWorks that make the process of building a flexible, customizable simulation platform easier and more automated. See an example showing how the virtual vehicle models can be deployed to the cloud for large-scale simulation studies.
Applying AI Technologies to Vehicle Sensor Modeling
This presentation introduces a method of applying AI technology to vehicle sensor modeling. In this method, machine learning with non-parametric regression is applied. A detailed GT-SUITE vehicle model is coupled with Simulink® to generate design data needed for sensor modeling. Relevant predictors are selected. The AI training algorithm is applied. The NOx sensor model is designed based on the data generated from the detailed GT-SUITE vehicle model. The FTP-75, US06, and HWFET are used for the vehicle running setup. The developed NOx sensor model is embedded into Simulink and coupled with the GT vehicle model to verify the prediction capacity. The LA92 driving cycle and Brazilian road test are used on the sensor model validation. The machine learning technology on NOx sensor modeling proves to be successful and will have a wide range of applications in the automotive industry.
Multi-Stack Fuel Cell Electric Vehicle Modeling and Applications
Multi-stack fuel cells offer various performance improvements over traditional fuel cell systems. In this talk, see how MATLAB® and Simulink® can be used to simulate these systems on a component and system level. As an application of this type of model, investigate a control approach to improve the overall efficiency of this multi-stack electric vehicle model for a given drive cycle.
What’s New in MATLAB, Simulink, and RoadRunner for Automated Driving Development
MATLAB®, Simulink®, and RoadRunner help engineers to build automated driving systems with increasing levels of automation. In this session, you will discover new features and examples in R2021b and R2022a that will allow you to:
- Author scenes and scenarios for driving simulation
- Simulate sensors and vehicle dynamics
- Design detection, localization, sensor fusion, planning, and controls algorithms
- Deploy to C, C++, GPU, and ROS
- Test functionality and code
Converting Spreadsheet-Based Scenario Definitions to OpenSCENARIO Files
The goal of this project was to create a tool to convert spreadsheet-based scenario definitions into the OpenSCENARIO standard format for easy exchange, usability, and storage for Ford engineers. Through standardization, the tool allowed for scenario reuse regardless of the simulation tool and test case format. The tool was first used to support automated conversion of scenarios defined in complicated spreadsheets to the beta version of OpenSCENARIO. This tool is capable of showing the resulting scenario in the Automated Driving Toolbox™ drivingScenario tool and exporting OpenSCENARIO 0.9.1 files representing the corresponding CarSim scenarios. Lastly, the app was modified so that users could enter information required by OpenSCENARIO to build up scenarios from other documentation—enabling reusability and easy shareability across Ford.
Design and Simulate Scenarios for Automated Driving Applications
The development of advanced driver-assistance systems (ADAS) and autonomous driving applications often depends on simulation to reduce in-vehicle testing. The automotive industry is investing in standards like OpenSCENARIO to describe dynamic content in these driving simulation environments. In this session, you will learn how to author scenarios on realistic road networks designed in RoadRunner. You can use this workflow to simulate scenarios with built-in agents as well as author and integrate custom agents designed in MATLAB®, Simulink®, or CARLA. The scenarios can be exported to OpenSCENARIO for simulation and analysis in external tools if desired. Using this workflow, you can quickly author and simulate scenarios to gain system insight and test your designs.
From Motorcycle to Chevy Bolt: A Journey with MATLAB in Autonomous Vehicles and Robots Research
Hear about our two decades of lessons learned and experience with autonomous vehicle and robot navigation through four case studies in chronical order. We started our journey by developing the world’s first autonomous motorcycle to participate in the DARPA Grand Challenges (DGCs) 2004 and 2005. We developed perception, vehicle navigation, and control algorithms for this non-minimum phase system to navigate in a desert terrain. Following DGC, we developed intelligent navigation algorithms for skid-steered vehicles. Collaborating with Johnson Space Center, we have also developed localization and mapping algorithms for NASA Robonaut, a humanoid robot serving on the International Space Station. Most recently, our students have successfully competed in the first GM/SAE Autodrive Challenge. MATLAB® has been an irreplaceable toolset and an integral part of our developing experience.
Design of a Vehicle Platooning Controller with V2V Communication
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.
Jim Ross is a senior principal technical consultant at MathWorks. Jim works with customers to address embedded controls development challenges and implement Model-Based Design. With over 25 years of industry experience, Jim designed air system and emission controls for John Deere diesel engines before leading the adoption of Model-Based Design, first for the Engine Controls Group and then across the entire enterprise. Prior to retiring from John Deere in 2020, Jim led a virtual design verification team responsible for supporting process and tools related to Model-Based Design, SIL testing, and HIL testing. Jim received his B.S. and M.S. in electrical engineering and an M.S. in aerospace engineering, all from the University of Illinois at Urbana-Champaign.
As a senior technical consultant at MathWorks, Michael Boyle loves solving tough problems in engineering, helping people out, and learning new things. He pursued these passions first as a research engineer in the biomedical space, where he developed a system for a brain-controlled brain stimulation to aid in studying and probing human brain rhythms. He then worked as an advanced support engineer supporting Model-Based Design workflows in many industries, but with a focus on AUTOSAR in the automotive industry. Michael holds B.S. and M.S. degrees in biomedical engineering from the University of North Carolina at Chapel Hill.
Shusen Zhang is an application engineer at MathWorks for advanced driver-assistance systems (ADAS) and automated driving segments. He is responsible for helping customers establish workflows using MathWorks solutions and building proofs of concept. His primary interest is algorithm development and testing for localization, mapping, planning, and controls. Prior to this role, Shusen was an application engineer focused on multidomain physical modeling and system simulation. Before joining MathWorks, Shusen was a development engineer at Navistar working on smart cruise control systems integrated with map databases.
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, and truck platooning with V2V. One of his recent focus areas is to develop a scenario creation from recorded data based on RoadRunner Scenario.
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).
Mike Sasena is a product manager focusing on the automotive products developed at the MathWorks office in Novi, Michigan. Prior to joining MathWorks, Mike spent 14 years working on model-based systems engineering projects for the automotive industry. His experience includes hybrid electric vehicle modeling for fuel economy analysis, modeling predictive controls development, and heterogeneous system simulation. Mike received his Ph.D. in mechanical engineering from the University of Michigan.
Brad Hieb is an application engineer at MathWorks focusing on control design. Prior to joining MathWorks, Brad worked for Ford Motor Company for 14 years. His Ford experience comprised powertrain controls design, vehicle controls for Ford’s Formula 1 racing group (including two years at Pi Research in the UK), and vehicle dynamics simulation, tools, and methods work. Prior to Ford, Brad worked for several years as a logic design engineer at Cray Research in Chippewa Falls, Wisconsin. He holds an M.S.E. from the University of Michigan, Ann Arbor, and a B.S. from Iowa State University, both in electrical engineering.
Jason Rodgers is a senior application engineer at MathWorks. Prior to MathWorks, he spent five and a half years at Toyota R&D in the Model-Based Design Group. He specialized in powertrain modeling and using control development, along with various optimization techniques to develop new powertrain systems. Jason earned a B.S.M.E. and an M.Sc. from the University of Michigan.
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).
Curt Hillier has been with NXP Semiconductors for 11 years. In this span, his work has included radar, hybrid and electric vehicle propulsion, battery management, and vehicle connectivity domains. His background is in applications engineering, covering both customer support and delivering advanced solutions and demonstrations for tradeshows, conferences, and customer workshops. Recently, his focus has been on the connected and electrified vehicle. In this domain, NXP and partners are developing solutions for real-time propulsion such as energy management systems, electric motor control, and battery cell state of charge (SoC) estimation as well as non-real-time vehicle health applications.
Kugler Maag Cie
Peter Abowd is CEO of Kugler Maag Cie North America. Peter’s expertise ranges from building successful global embedded software organizations and providing organizational change leadership to guiding teams in implementing practices from ASPICE, functional safety, Agile, software product lines, and CMMI. Before Kugler Maag, Peter led Altia, Inc., and held engineering and leadership positions at Visteon and Ford. Peter holds a B.S.E. degree from the University of Notre Dame and an M.S.E. from Carnegie Mellon University.
Kugler Maag Cie
Steve Tengler is a principal at Kugler Maag Cie North America. Steve’s expertise includes marketing and user experience development, as well as guiding teams in implementing best practices in ASPICE and Agile development. Prior to Kugler Maag, Steve served as senior director of Connected Vehicle and global director of UX at Honeywell Connected Vehicle. He also held management positions at Altia, Inc., OnStar, Nissan North America, and Ford. Steve is also a senior contributor to Forbes.com. Steve holds B.S.E. and M.S.E. degrees from the University of Michigan.
Ford Motor Company
Emily Foster is a research engineer at Ford Motor Company. She works within the Driver Assistance Technology Modeling and Simulation Group. For the last three years, she has been involved in feature modeling and tool creation. Emily holds bachelor’s and master’s degrees in mechanical engineering from Lawrence Technological University and is a second-year Ph.D. student at Oakland University.
KPIT Technologies Limited
Mahesh Ghivari is the associate vice president, practice offering leader for Electric & Conventional Powertrain at KPIT. He is responsible for roadmap and development of technology solutions and providing software services to customers in the Electric Powertrain and Conventional Powertrain Practices within KPIT. Over the past 22 years, he has gained experience across process industries, applied research and development, business development, technical engagement, and delivery management. Mahesh holds an M.Tech. in chemical engineering with specialization in system and controls from the Indian Institute of Technology, Kanpur. He also holds an M.B.A. in strategic engineering management from Coventry University, UK.
KPIT Technologies Limited
Debango Chakraborty is an expert in battery management systems and a senior designer at KPIT Technologies. He is responsible for researching and developing new technology trends in battery management systems and writing technical solutions for customer proposals based on organization offerings within KPIT. He has over 10 years of experience in battery management and onboard charger systems working with x-EV variants. Debango graduated from the Institute of Engineering and Management, India, with a degree in electronics and communication.
Trevor Jones is a staff application software engineer at Gotion, Inc., and has worked in the field of model-based control system design and simulation for over 15 years. His interests include applying model-based control, machine learning, and simulation methods in production environments. Trevor received an M.S. degree in mechanical engineering from the University of California, Berkeley, and a B.S. degree in mechanical engineering from the University of California, Davis.
David Jauernig is an application software engineer at Gotion, Inc., and previously worked for a German automotive OEM. His interests include state-estimation of secondary cells, battery management systems (BMS), battery-powered electric vehicles (BEV), software development, and machine learning. David received an M.S. degree in electrical engineering from Technical University of Munich and a B.S. degree in engineering science from the University of Bayreuth.
Texas A&M University
Dezhen Song is a professor and associate department head for academics in the Department of Computer Science and Engineering, Texas A&M University. He is also a multimedia editor for Springer Handbook of Robotics. Song's primary research area is robot perception, networked robots, visual navigation, automation, and stochastic modeling. He was an associate editor of IEEE Transactions on Robotics (T-RO) and Automation Science and Engineering (T-ASE) and a senior editor for IEEE Robotics and Automation Letters (RA-L). Song received a Ph.D. from the University of California, Berkeley, and an M.S. and B.S. from Zhejiang University.
Rafael Átila Silva
Rafael Átila Silva is a virtual analysis engineer at Stellantis. As a member of the virtual engineering team, Rafael’s focus is building virtual models of sensors and actuators for hybrid and electric propulsion systems. He has spent over six years in automotive R&D. His interests are around vehicle electrification, including AC motor drives and power electronics. Before that, he spent three years as a consulting engineer. Rafael received his master’s degree in electrical engineering from the Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil. He is working towards his doctor’s degree, also in electrical engineering.
Ford Motor Company
Joshua McCready is an autonomous vehicle brake controls engineer responsible for AV-specific Ford in-house software functions. Previously at Ford, he was also a software integrator and an electric power steering system engineer. He graduated from UM-Ann Arbor and UM-Dearborn with a B.S.E. and an M.S.E. in electrical engineering.
Ford Motor Company
Hans Gangwar is a brake controls software engineer responsible for Ford‘s in-house modeling of autonomous vehicle brake controls.
Luigi Milia is an automotive industry manager for the EMEA market at MathWorks. He has 20 years of experience in the automotive industry and comes from FCA Italy, where he was responsible for tools and methodologies adopted in powertrain controls and software development. His focus areas are software architectures and development processes, code generation, and virtual validation. Luigi holds a master’s degree in electronic engineering from Politecnico of Turin.
Shwetha Bhadravathi Patil is a senior product manager at MathWorks focusing on AUTOSAR, DDS, and Simulink code generation products. Before joining MathWorks, she worked as a software developer at Aptiv (formerly known as Delphi Automotive) on AUTOSAR-based projects and at Analog Devices Inc. as a technical marketing engineer for automotive video codecs. Shwetha graduated from Tufts University with an M.S. in engineering management and from Manipal University with an M.S. in automotive embedded systems.
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. Tjorben holds a Ph.D. in mathematics from the TU Kaiserslautern.
Jason Stallard manages a global team at Cummins Inc. developing Model-Based Design processes and tools, and is helping to lead Cummins away from proprietary legacy towards standard architecture, processes, and tools like AUTOSAR, Agile, and continuous integration/continuous delivery. He has been with Cummins for over 20 years, and has also worked on model-based development, in-vehicle data logging, rapid prototyping, and automated test and release. Jason holds a doctorate of technology from Purdue University and is a Certified SAFe 5 product owner/product manager.
Brandon Trombley is a technical advisor to automotive accounts at MathWorks. Prior to joining MathWorks, Brandon had a 20-year career working primarily for Tier 1 automotive suppliers. He managed a team that established an ISO 26262–qualified workflow for Model-Based Design in AUTOSAR. His experience also includes vehicle crash algorithm subsystem architecture design; component development; production code generation, validation, and calibration; MATLAB and Simulink tool development and deployment; signal processing; sensor modeling; vision systems; and business intelligence. Brandon holds a B.S. in physics from the University of Michigan and pursued graduate studies at the University of Michigan, Dearborn, in electrical engineering.
Patrick Munier joined MathWorks in 2007 as a director of code verification engineering. He cofounded PolySpace Technologies in 1999 and held several senior positions in the company, including head of engineering, marketing, customer service, and consulting. Before PolySpace, Patrick was head of the engineering team of Verilog and the project manager for the qualification of the code generator for the DO-178B standard.
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.
Ford Motor Company
Hans Gangwar is a brake controls software engineer responsible for Ford‘s in-house modeling of autonomous vehicle brake controls.
Ford Motor Company
Joshua McCready is an autonomous vehicle brake controls engineer responsible for AV-specific Ford in-house software functions. Previously at Ford, he was also a software integrator and an electric power steering system engineer. Joshua graduated from UM-Ann Arbor and UM-Dearborn with a B.S.E. and an M.S.E. in electrical engineering.
Scott Furry is a principal technical consultant for MathWorks. His focus is helping organizations adopt Model-Based Design for embedded controls, and he has worked with world-leading companies on a wide range of applications, including automotive, aerospace, and defense. Prior to joining MathWorks in 2005, he worked for General Motors and Bosch in control systems engineering, including powertrain controls design, rapid prototyping, HIL, calibration, and plant modeling. Scott received his B.S. and M.S. in mechanical engineering from the University of Michigan.
Pitambar Dayal is the product manager for Automated Driving Toolbox at MathWorks. In the past, he worked on building MATLAB examples for automated driving, deep learning, image processing, and computer vision. Pitambar graduated with a B.S. in biomedical engineering from the New Jersey Institute of Technology.
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