Introduction to Highly Automated Vehicles C1603

Topics: Advanced Technologies

Every year, the U.S. on average, experiences more than 34,000 traffic deaths and over 5 million vehicle crashes. While the trend in traffic deaths has been generally downward for the past decade, most of this reduction has been the result of optimizing passive occupant crash protection systems such as seatbelts and airbags. Highly automated vehicle's (HAV's) offer the potential to significantly reduce vehicle crashes by perceiving a dangerous situation before the crash has occurred and supporting the human driver with proactive warnings and in some cases active interventions to avoid or mitigate the crash. Fully autonomous vehicles promise even greater benefits, such as increased mobility for elderly, visually-impaired, and other physically challenged individuals, reduced public infrastructure needs such as parking decks, and reduced environmental impact. 

This course is designed to familiarize participants with the technologies enabling highly automated vehicles, and how they integrate with existing passive occupant crash protection systems. You will learn how HAV's 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 HAV functions, various sensors used, including their operation and limitations, and sample algorithms, will be discussed and demonstrated. The course also looks at the ethics driving HAV behavior, liability considerations and reviews the current and future regulatory landscape. The course uses a combination of lectures, class discussions, computer simulations, and videos. 

Learning Objectives

By attending this class, 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 and 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.


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

Rev Jeff BlackburnJeff 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.

Rev Hasan FerdowsiHasan 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.

Duration: 2 Days
CEUs: 1.3

Format: Virtual

Event ID: S-00217

Location: Live Online

Session Info:

  • Session 1 - September 18 (8:30 a.m. - 4:30 p.m. ET)
  • Session 2 - September 19 (8:30 a.m. - 4:30 p.m. ET)
    2 Sessions

  • Fees: $1,299.00

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