Accident Reconstruction, the Autonomous Vehicle and ADAS

After the crash, you need to know if an autonomous or driver assistance system was installed in the vehicle, if it functioned properly, and if it affected the outcome of the accident. Get a detailed look at the rapidly growing field of self-driving vehicles and driver assistance systems. What is available, how does it work, and what will happen in a crash scenario? How does it affect product liability and accident reconstruction? Some auto manufacturers are equipping the majority of their new vehicles with such technology, and it’s important for the forensic engineer to “stay ahead of the curve”. We’ll look at how to determine if the systems were installed, if they were enabled, and if they functioned as designed. The presentation will be interspersed with many videos and photos, allowing attendees to experience for themselves the implications of this exciting new field. Examples of the systems at work will be demonstrated.

Please bring your laptop and calculator with you to this course.

This course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 6 Continuing Education Units (CEUs). Upon completion of this seminar, accredited reconstructionists should mail a copy of their course certificate and the $5 student CEU fee to ACTAR, PO Box 1493, North Platte, NE 69103

What Will You Learn

By attending this seminar, participants will be able to:
  • Summarize what technology exists, both in current production and under development
  • Identify applicable state and federal regulations
  • Explain the ethical and societal implications of the technology
  • Define performance parameters based on currently available standards and protocols
  • Formulate a plan to approach accident reconstruction using the new technology

Is This Course For You

This course is designed for engineers involved in the investigation and analysis of vehicle crashes who need to understand the field of self-driving vehicles, and how to apply it in a collision reconstruction. In addition, this course can be valuable to professionals involved in risk analysis and product liability.

Materials Provided

This data is not available at this time

Course Requirements

This data is not available at this time


  • Overview of the Available Technology
    • Adaptive cruise control
    • Blind spot monitoring and cross traffic alert
    • Lane departure warning and lane keeping
    • Lane centering
    • Forward collision warning and automatic emergency braking
    • Traffic signal awareness
    • Full automation
  • SAE Levels of Self-Driving Vehicles
    • Level 1: Cruise Control
    • Levels 2-3: Adaptive cruise control and lane keeping/departure warning
    • Levels 4-5: Hands-off operation
    • The significance of fallback performance
  • The Enabling Technologies
    • Sensors
      • Sonar/ultrasound
      • Cameras, including driver monitoring
      • Radar
      • GPS
      • LIDAR
      • V2V/V2I
    • Computing capabilities
    • Actuators
      • EPAS (Electric Power Assist Steering)
      • Throttle by wire
      • ESC (Electronic Stability Control) brake control
    • Infrastructure
      • Road mapping
      • Networked signal timing
  • Breakout on above topics
    • Sonar/ultrasound
    • Radar – forward 2 stage, BLIS/CTA, and range/speed/azimuth. Reflective values
    • LIDAR – 360 degree scene mapping and need for high computing power
    • V2V/V2I – Vehicle-to-Vehicle and Vehicle-to-Infrastructure; Platooning and advance warning of road conditions; Partial adoption effects
    • Computing capabilities
      • Image classification
      • Artificial intelligence and neural nets
      • Probabilistic decision making
  • Current state of rapidly changing technology
    • Current OEM production
    • Supplier and independents
  • Standards and Protocols
    • Surrogate vehicles
  • Media and public perception
  • Liability and Litigation
    • Current status
    • Crash and injury statistics
    • Future status
    • Insurance implications
  • EDR Examples
  • Examples of ADAS – Successes and Challenges
  • Future Work
    • Exemplar EDR data
    • Fingerprinting ADAS performance