MATLAB and Simulink Seminars

ADAS and Automated Driving Development Using MATLAB and Simulink


Do you have an open and flexible visualization tool to gain insight from vision, radar, and lidar data?
How quickly can you apply the latest deep learning research to vision perception development?
Are you able to design sensor fusion and control in simulation, before going to the test vehicle?

In this seminar, MathWorks engineers will demonstrate several new technologies to accelerate ADAS and automated driving development with MATLAB and Simulink.   


They will introduce the latest ADAS and automated driving development tools from MathWorks, including

  • Visualize recorded and live sensor data
  • Framework for sensor fusion algorithm design and test
  • Deep learning for vision object detection
  • Control design in simulation

They will demonstrate new products and ADAS-extensions of existing MathWorks products, including

  • Automated Driving System Toolbox
  • Model Predictive Control Toolbox
  • Vehicle Network Toolbox
  • Robotics Systems Toolbox
  • GPU Coder

Who Should Attend

Engineers and managers working on ADAS and automated driving system, algorithm, and software development.

About the Presenter

Mark Corless - is a principal application engineer at MathWorks, focusing on embedded controls and signal processing applications. Mark regularly interacts with automotive customers to integrate simulation and code generation tools into their production process. Before joining MathWorks in 2004, Mark was a DSP engineer at Visteon, where he designed automotive audio and receiver systems. Mark has an M.S. in electrical engineering from the University of Michigan, Dearborn.

Seo-Wook Park - is a principal application engineer at MathWorks, focusing on advanced driver assistance systems (ADAS) and automated driving. He is working on ADAS algorithm development including vision and radar sensor fusion algorithms for forward collision warning and AEB, lidar 3D point cloud signal processing for autonomous driving, ground-truth labeling for vision data, and deep learning for computer vision. Before joining MathWorks, he worked in passive and active safety electronics development at Autoliv, Bosch, and Hyundai Autonet for over 20 years. He has a Ph.D. in robotics and control system from the Korea Advanced Institute of Science and Technology (KAIST).

Brett Shoelson - holds a B.A. degree in anthropology from the University of Florida, a B.S. in biomedical engineering from Mercer University (Macon, GA), and an M.S. and Ph.D. in biomedical engineering from Tulane University. Brett owned and operated a publishing company before returning to school for a second round of education focusing on engineering. Following his doctoral work, he did post-doctoral research at Harvard Medical School, and spent five years doing research at the National Institutes of Health. The 13 years prior to his employment at The MathWorks were spent focused on process automation with MATLAB (with a strong focus on medical image processing) in the biomedical arena. He started working for MathWorks in 2005.


Time Title
08:30 Registration and free continental breakfast
Welcome and Introduction

Case Studies: How MATLAB and Simulink Accelerate ADAS and AD Development


Deep Learning: Apply Deep Learning to Vision Object Detection

Sensor Fusion Example: Develop Algorithms Using Recorded and Live Data

Adaptive Cruise Control Example: Apply Model-Predictive Control to Control Design

Closing Remarks

Product Focus

Registration closed