Introduction to Highly Automated Vehicles C1603
Topics: Advanced Technologies
This course highlights the technologies enabling ADAS and how they integrate with existing passive occupant crash protection systems, how ADAS functions perceive the world, make decisions, and either warn drivers or actively intervene in controlling the vehicle to avoid or mitigate crashes. Examples of current and future ADAS functions, and various sensors utilized in ADAS, including their operation and limitations, and sample algorithms, will be discussed and demonstrated. The course utilizes a combination of hands-on activities, including computer simulations, discussion and lecture.
The course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 13 Continuing Education Units (CEUs). Upon completion of this course, accredited reconstructionists should mail a copy of their course certificate of achievement and the $5 participant CEU fee to ACTAR, PO Box 1493, North Platte, NE 69103.
Learning Objectives
By attending this course, you will be able to:
- Explain the SAE Levels of Automation and where different HAV functions fit in the hierarchy
- Explain the HAV functions and articulate their limitations
- Identify different sensors used in HAV systems, how they operate, and their limitations
- Analyze how different sensors can be combined to improve overall system performance
- Describe the current and future methodologies used in developing HAV algorithms
- Articulate how ROC curves, DOE and Monte Carlo techniques can be used to measure and improve algorithm performance
- Critically examine the proposed federal rules and validation methods for HAV systems
- Analyze how HAV's may affect the performance of existing passive occupant safety systems and how integration of the systems might be accomplished
- Describe liability and policy considerations for OEM's and Tier suppliers working on HAV technologies
Who Should Attend
This course is designed for all professionals - technical or managerial - who are involved either directly or indirectly with vehicle safety performance. Professionals in legal, regulatory and compliance areas concerned with proposed NHTSA rulemaking, and insurance industry analysts developing coverage standards for vehicles with active safety technologies will also find this course useful.
Prerequisites
An engineering undergraduate degree in any discipline would be beneficial.
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.
