Introduction to Highly Automated Vehicles     New!

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I.D.# C1603Printable Description
Duration: 2 Days
  Delivered in
April 3-4, 2017 (8:30 a.m. - 4:30 p.m. ) - Detroit, Michigan    
July 26-27, 2017 (8:30 a.m. - 4:30 p.m. ) - Troy, Michigan    
November 8-9, 2017 (8:30 a.m. - 4:30 p.m. ) - Troy, Michigan    

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Every year, the U.S. experiences more than 32,000 traffic deaths and over 3.8 million crash injuries. While the trend in traffic deaths has been 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. Advanced driver assistance systems (ADAS) now offer the potential to significantly reduce or eliminate most vehicle crashes by perceiving a dangerous situation before the crash has occurred and taking action to avoid or mitigate the crash.

This course is designed to familiarize participants with the technologies enabling advanced driver assistance systems and how they integrate with existing passive occupant crash protection systems. You will learn 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, 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.

Learning Objectives
By attending this seminar you will be able to:

  • Explain the SAE Levels of Automation and where different ADAS functions fit in the hierarchy
  • Explain the ADAS functions and articulate their limitations
  • Identify different sensors used in advanced driver assistance 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 ADAS 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 advanced driver assistance systems
  • Analyze how active safety systems 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

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.

Prerequisites
An engineering undergraduate degree in any discipline would be beneficial.

Topical Outline
Day One

  • SAE Levels of Automation
  • ADAS Applications
    • Adaptive cruise control (ACC)
    • Cooperative adaptive cruise control (CACC)
    • Lane keeping assist (LKA)
    • Lane departure warning (LDW)
    • Blind spot warning (BSW)
    • Backup assist
    • Pedestrian detection (PED)
    • Automatic emergency braking (AEB)
    • Collision imminent braking (CIB)
    • Forward collision warning (FCW)
    • Do not pass warning (DNPW)
    • Auto park
    • Traffic jam assist
    • Brake light and traffic signal recognition
    • Left turn assist (LTA)
    • Intersection movement assist (IMA)
    • Emergency electronic brake light (EEBL)
    • Lane change warning (LCW)
  • Autonomous Behavior Liability Considerations
    • Shifting of liability from driver/occupant to OEM / Supplier
    • Ethical behavior of autonomous vehicles
DAY TWO
  • Sensors
    • GPS
    • Ultrasonic
    • RADAR
    • LiDAR
    • Camera
    • 3D Camera
    • DSRC radio
  • Algorithm Development
    • Automatic emergency braking
    • Lane departure warning
    • Pedestrian detection
    • Emergency electronic brake light
  • Measuring Algorithm Performance
    • ROC curves
  • Algorithm Testing and Validation
    • Monte Carlo techniques
    • Design of experiment techniques
    • Physical performance testing
    • Interoperability testing
  • Integrated Safety Considerations
    • Out of position occupants

Instructor(s): Jeffery Blackburn
Jeff Blackburn is the North American Sales Manager for Tass International, a subsidiary of Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO), the Dutch Organization for Applied Scientific Research. In this role, Jeff is responsible for educating customers in the use of software simulation tools used in the development of advanced driver assistance systems and autonomous vehicle control algorithms. Prior to joining Tass, Jeff held positions in controls and systems engineering with National Instruments, Takata, Fanuc Robotics, and Rockwell Automation. He has extensive professional experience in biomechanics, injury mechanisms and causation, occupant protection, testing, and regulatory requirements and regulatory process. Mr. Blackburn 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.

Fees: $1370.00 ; SAE Members: $1096.00 - $1233.00

1.3 CEUs
You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.

To register, click Register button at the top of this page and submit the online form, or contact SAE Customer Service at 1-877-606-7323 (724/776-4970 outside the U.S. and Canada) or at CustomerService@sae.org.

For a quote on bringing this course to your company site, fill out a Corporate Learning Solutions Request Form