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.

  • The Role of Vehicle Automation in Reducing Traffic Fatalities
  • Passive safety highly optimized
  • Recent increase in vehicle crash rates
  • Three Main Functions Provided by Highly Automated Vehicles (HAVs)
    • Increase situational awareness
    • Provide proactive driver warnings
    • Intervene to prevent / mitigate crashes where driver response is late or non-existent (distracted driver) 
  • Sensors Used in Highly Automated Vehicles
  • Current Development Efforts and Market Leaders
  • Levels of Automation
    • BASt
    • NHTSA
    • SAE (Prior to September 2016)
    • SAE (September 2016 Operational Driving Domain revision)
    • Comparison of the Three Versions
    • Level 3 Handoff Problem
  • Operation / Functionality of Various HAV Capabilities
    • Blind spot warning
    • Backup assist / cross traffic warning
    • Lane departure warning
    • Lane keep assist
    • Cruise control
    • Adaptive cruise control
    • Cooperative adaptive cruise control
    • Forward collision warning
    • Automatic emergency braking
    • Pedestrian detection
    • Collision imminent steering
    • Do not pass warning
    • Left turn assist
    • Intersection movement assist
    • Emergency electronic brake light
    • Traffic sign recognition
    • Traffic jam assist
    • Auto park / park assist
    • Platooning
  • SAE Level of Automation Corresponding to Each Application
  • Advantages and Limitations of Each Capability
  • Sensor(s) used in Each Capability
  • Technology Spread and Effects on Insurance Claims 
  • Sensors: Active and Passive
  • Operating Principles, Performance, Advantages and Limitations of Various Sensors Used in HAV Capabilities
    • GPS
    • IMU
    • Gyros
    • Cameras - monocular, stereo, monochrome (grayscale), color, CCD, CMOS
    • Ultrasonic
    • Radar
    • Lidar
    • Leddar
    • Matrix TOF camera
    • DSRC
  • Sensor Recalls
  • Sensor Fault Detection and Diagnostics
  • ECU Consolidation
  • Algorithms
    • Types (classifications) of Algorithms
    • Algorithm Examples - Haversine, Kalman filter, particle filter, neural net, SLAM
    • Using ROC Analysis to Measure Algorithm Performance
  • Testing Processes Used in the Development of HAV Systems
  • Statistics Driving Vehicle Testing
  • Why Software Can Never by "Fully" Tested
  • Different Testing Approaches, and Their Strengths and Weaknesses
    • White box
    • Black box
    • Grey box
    • Unit
    • Integration
    • Interface
    • System
    • Static code analysis
    • Formal methods
    • Dynamic code testing
    • SiL (Software in the loop) - Monte Carlo, DOE
    • Sensor characterization
    • HiL (Hardware in the loop) - Driving simulators, HiL Radar target simulation
    • Soft targets
    • Closed track testing - IIHS, Virginia smart road, MCity, AstaZero, Fort Monmouth, VeHIL
    • On-road testing - states, federal, international
  • Testing Requirements That Must be Met Before a Vehicle Can be Tested On-road
  • Cybersecurity
  • Basic Network Architecture of a Modern Vehicle
    • ECU's
    • Communication busses - CAN, FlexRay, LIN 
  • Probable Attack Surfaces and Their Vulnerabilities
  • Remote anti-theft system (Key fob)
  • TPMS
  • Remote keyless entry / start
  • Bluetooth
  • Radio data system
  • Telematics / Cellular / WiFi
  • OBD II Vulnerabilities
  • Possible Defenses Against Attacks
    • Minimize attack surfaces
    • CAN message injection mitigation
    • CAN message cryptography
    • Vehicle network architecture changes
    • Clock based intrusion detection
  • NHTSA October 2016 Best Practices Guidance
  • Ethical Challenges and Human Behaviors
    • Differences Between Human and Autonomous Crash Decision Making
    • Rational Decision-making Approaches; Deontology and Consequentialism
    • Artificial Intelligence Approach - Strengths and Weaknesses
  • Liability
    • Negligence
    • No Fault Liability
    • Strict Liability
    • Case Study - Tesla Crash and How Liability was Determined
  • Regulations
    • States Permitting On-Road Testing
    • Testing Requirements
    • Current Federal Regulations and Their Application to Active Safety Technologies
  • Analysis of Ongoing Rulemaking Activities and Research and Their Applicability to Active Safety Technologies
  • NHTSA "Policy on Automated Vehicle Development" May 2013
  • NHTSA "Policy on Automated Vehicle Development" update January 2016
  • NHTSA - Letters of interpretation
  • NHTSA Advance notice of proposed rulemaking August 2014 (FMVSS 150)
  • NHTSA - Federal Automated Vehicles Policy, September 20, 2016
  • NHTSA AV1 Guidance
  • NHTSA AV2 Guidance
  • NHTSA AV3 Guidance
  • NHTSA AV4 Guidance
  • Federal Lane Departure Warning and Forward Collision Warning Tests
  • IIHS Automatic Emergency Braking Tests
  • HAV Implications for Passive Safety (Integrated Safety)
  • HAV Public / Industry Misconceptions
  • Vehicle Design / Use Changes
Jeffery Blackburn

Rev Jeff Blackburn

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.

Duration: 2 Days
CEUs: 1.3

Format: Classroom

Event ID: 7163

Location: Troy, Michigan

Session Info:

8:30 a.m. - 4:30 p.m.

Fees: $1,299.00

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