Exploration of Machine Learning and Neural Networks for ADAS and L4 Vehicle Perception C2205
Topics: Advanced Technologies
Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.
In this course participants examine an existing convolutional network to understand how it is constructed, trained and then using open-source python tools you’ll create your own convolutional neural network. You’ll train your network using a supplied training data set and then test your networks performance.
Learning Objectives
By attending this course, you will be able to:
- Describe the differences between a convolutional and other types of neural networks and where each should be used in the context of ADAS and L4 vehicle development
- Explain how a convolutional network performs the perception and classification task on raw camera radar or lidar images to identify objects of interest impacting the ADAS or L4 vehicle operation
- Select test cases for evaluating the performance of neural network-based perception systems
- Identify potential problems in the network performance caused by training problems due to bias or other problems with the training data sets
Who Should Attend
This course is designed for ADAS and L4 development engineers working on perception using camera, lidar and radar sensors, and validation and test engineers responsible for functional safety (ISO 26262) and SOTIF (ISO 21448) where the perception systems are using AI, neural networks, or other machine learning techniques.
Policy makers responsible for regulations regarding public highway testing of autonomous vehicles where perception is based on AI, neural networks or other machine learning techniques will also benefit from this course.
Prerequisites
A bachelor's degree in a technical discipline and familiarity with basic ADAS functions is recommended.
Familiarity with the python programming language (or other scripting language such as visual basic or Matlab) is recommended.
You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.
Jeffery Blackburn or Hasan Ferdowsi
Jeff Blackburn is the Senior Product Sales Manager for Ansys Autonomy, the world’s largest supplier of simulation software. Prior to joining Ansys, Jeff worked on developing autonomous vehicle research platforms at Dataspeed, was a founding member of Metamoto who developed a massively scalable cloud-based simulation platform, and was the North American ADAS and Autonomous Vehicle subject matter expert for Siemens/Tass PLM Software, Inc. He has also held positions in controls and systems engineering with National Instruments, Takata, Fanuc Robotics, and Rockwell Automation. Jeff has organized and presented at numerous technical forums. He has been issued twenty-one U.S. patents, primarily in the area of occupant safety. Jeff holds a B.S. in Engineering and a J.D. from the University of Akron.
Hasan Ferdowsi is an Assistant Professor of Electrical Engineering at Northern Illinois University. He earned his PhD in Electrical Engineering from Missouri University of Science and Technology in 2013. Since then, he has been in academia and involved in various teaching and research activities as well as sponsored industry projects. Hasan has taught many courses in both undergraduate and graduate levels, including Linear Control Systems, Electronics, Power Systems, Electromagnetics, Modern Control Systems, Nonlinear Control, Adaptive Control, Mechatronics, Neural Networks, and Digital Control. His research work has been mainly focused in the areas of Robotics, Autonomous Vehicles, ADAS, Machine Learning, and Fault Diagnostics. He is currently working on interdisciplinary projects, especially related to autonomous vehicles, in collaboration with experts in different fields including Mechanical Engineering and Computer Science.
