Overview of Highly Automated Vehicles

Automated vehicles continue to be the hot topic in automotive development. A wide variety of autonomous functions are under development and will be implemented within the next 3 to 7 years. All the OEM's and Tier I suppliers continue to struggle with establishing labs, staff and development efforts in this area. This course is designed to familiarize participants with the rapidly developing field of highly automated vehicles. You will learn how the HAV perceives the world, makes decisions, and controls the vehicle.

The course covers behavioral competencies, like autonomous braking, steering, and merging, reviews various sensors, and their strengths and weaknesses, and discusses the HAV software stack and how the various pieces work together to perceive the world and move through it. Various testing approaches are discussed, for software, sensors, cybersecurity and the vehicle itself. The course also covers liability and ethical considerations for HAV’s and reviews current state and federal regulations, as well as potential future regulatory actions.

There is a half day of hands-on experience with a by-wire research vehicle and sensors. You will have the rare opportunity to ride in a by-wire car provided by Dataspeed Inc., of Rochester Hills, MI and learn about self-driving technologies. There is no better experience than this to understand the technology behind self-driving cars and their potential benefits: increased safety, expanded mobility for those whose ability to drive a traditional vehicle is limited and enhanced environmental sustainability.

What Will You Learn

After attending this course, you will be able to:
  • Explain the SAE Levels of Automation and where different HAV functions fit in the hierarchy
  • Realize the HAV functions and understand their limitations
  • Identify and analyze different sensors used in HAV’s, how they operate and how different sensors can be combined to improve overall system performance
  • Describe the current and future methodologies used in developing HAV algorithms
  • Comprehend how ROC curves, DOE and Monte Carlo techniques can be used to measure and improve algorithm performance
  • Review proposed federal rules and validation methods for HAV systems
  • Analyze how HAV systems may affect the performance of existing passive occupant safety systems
  • Understand liability and policy considerations for OEM's and Tier suppliers
  • Appreciate the technology behind self-driving cars with a hands-on experience in a by-wire research vehicle

Is This Course For You

This course is designed for all professionals - technical or managerial - who are involved either directly or indirectly with HAV development. An engineering undergraduate degree in any discipline would be beneficial. Professionals in legal and regulatory and compliance areas concerned with proposed NHTSA rulemaking, and insurance industry analysts developing coverage standards for HAV’s will also find this course useful.

Materials Provided

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Course Requirements

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  • Why highly automated vehicles?
  • HAV benefits
  • HAV timeline
  • SAE Levels of Automation
  • Level 3 re-engagement problem
  • Warnings
    • Blind spot warning
    • Lane departure warning
    • Forward collision warning
    • Do not pass warning
    • Electronic emergency brake light
  • Driver Assist
    • Lane keep assist
    • Adaptive cruise control
    • Cooperative adaptive cruise control
    • Automatic emergency braking
    • Collision imminent steering
    • Left turn assist
    • Traffic jam assist
    • Auto park / park assist
  • Platooning
  • GPS & Glonass
  • IMU
  • HD maps
  • Camera
  • Ultrasonic
  • Microphone arrays
  • Radar
  • Lidar
  • DSRC
  • Kalman filter
  • Particle filter
  • Neural nets
  • Machine learning
  • Supervised training
  • Gradient descent
  • Unsupervised learning
  • SLAM
  • ROC analysis
  • HAV AI hardware
  • How safe is safe enough?
  • Halting problem
  • Software bugs
  • White box testing
  • Black box testing
  • Dynamic code testing
  • Software in the loop (SiL)
  • Cloud based large scale testing
  • Hardware in the loop (HiL)
  • Tesla approach to software testing
  • Closed track testing
  • On road testing
  • Waymo approach to testing
  • Automotive software complexity
  • Exploitable security vulnerabilities
  • Hacking a car
  • Attack surfaces
  • OBD vulnerabilities
  • Defending against attacks