John Deere is the world’s premier farm equipment manufacturer and is committed to those linked to the land. We believe that in serving them, we support improving the quality of life for people all around the world. We see several major global trends affecting the agriculture industry: population growth, increased demand for food, larger farms, skilled labor shortages, and environmental sustainability. Within John Deere, the modeling community is building the infrastructure to meet these challenges efficiently and effectively through the use of systems engineering, model-based development, and simulation. This development balances the need for cutting edge tools with stable enterprise-wide infrastructure. This presentation will highlight the technology that Deere is currently using and developing to meet our needs spanning from component design to designing the farm of the future.
Simulink® and Stateflow® are used extensively for ISO 26262–compliant embedded software development, from ASIL-A through ASIL-D. The algorithmic needs of advanced driver assistance and autonomous driving applications are often expressed more naturally in MATLAB®, however. In this session, Dave Hoadley discusses the challenges and best practices for achieving ISO 26262 compliance in a mixed MATLAB and Simulink paradigm. Examples include applying verification and validation tools to software components authored primarily in MATLAB and integrating Simulink with collaboration tools such as Git™ and Gerrit Code Review.
ADAS and Automated Driving
11:15 a.m.–12:00 p.m.
As the level of automation increases, the use scenarios become less restricted and the testing requirements increase, making the need for modeling and simulation more critical. In this session, you will learn and how MATLAB® and Simulink® support engineers building automated driving systems with increased levels of automation. You will learn about new features in Releases 2019b and 2020a for:
- Designing perception, planning, and controls components
- Testing by simulating driving scenarios and sensor models
- Deploying by generating C/C++ code
You will learn about these topics through examples that you can reproduce when you return to your office.
An automated lane change maneuver (LCM) system enables a vehicle to automatically move from one lane to another lane. The LCM system identifies objects surrounding the vehicle, plans an optimal trajectory that avoids these objects, and steers the ego vehicle along this trajectory. In this session, you will learn how you can use MATLAB® and Simulink® to:
- Model the planning and controls components
- Model scenarios and vehicle dynamics to test components
- Simulate and assess behavior with traffic on straight and curved roads
AI in Engineering
11:15 a.m.-12:00 p.m.
Do you have a strategy to analyze the data from your connected test vehicle fleet? How fast are you able to develop and apply analytics on huge sets of data to find desired events or find trends that were previously unknown? Are you able to work with all of your data instead of a subset? In this talk, Will Wilson demonstrates how to implement a workflow with MATLAB® that addresses these issues. Topics include:
- Exploring the types of questions you can ask of your fleet data
- Preparing your data for efficient analytics
- Developing analytics that execute on a “per unit” or “across all” basis
- Deploying analytics to keep up with the continuous intake of test data
Machine learning is a hot topic in the automotive industry. Deploying machine learning algorithms to electronic control units (ECUs) is often a bottleneck because of the memory, CPU throughput, and software development and integration techniques required to support machine learning algorithms. In this presentation, Gokhan Atinc provides an overview of machine learning technologies and deployment workflows for embedded processors. They will also discuss advanced capabilities that are of interest for automotive and adjacent industries, including in-place modification support, Simulink® support. and fixed-point conversion.
MATLAB® has scaled up to support cluster and cloud-based data and computing frameworks. In the meantime, data and computing framework technologies continue to evolve rapidly. In this presentation, Arvind Hosagrahara provides an example of how an enterprise customer integrated MATLAB with their existing framework. The integration enables engineers to slice and dice very large datasets using Apache Spark™ and extract forensic slices to develop analytics that can then be pushed down to execute at scale on the cluster. The integration with MATLAB also supports workflows that conform to enterprise-level security, governance, and access controls requirements while enabling users to make the results of their analytics easily accessible across the organization.