Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Quentin Goss, Graduate Yara Alrashidi, Senior Mustafa Ilhan Akbas, Faculty
Lead Presenter's Name
Quentin Goss
Faculty Mentor Name
Mustafa Ilhan Akbas
Loading...
Abstract
Autonomous vehicle (AV) technology is positioned to have a significant impact on various industries. Hence, artificial intelligence powered AVs and modern vehicles with advanced driver-assistance systems have been operated in street networks for real-life testing. As these tests become more frequent, accidents have been inevitable and there have been reported crashes. The data from these accidents are invaluable for generating edge case test scenarios and understanding accident-time behavior. In this paper, we use the existing AV accident data and provide a methodology to identify the atomic blocks within each accident, which are modular and measurable scenario units. Our approach formulates each accident scenario using these atomic blocks and defines them in the Measurable Scenario Description Language (M-SDL). This approach produces modular scenario units with coverage analysis, provides a method to assist in the measurable analysis of accident-time AV behavior, and generates accident scenarios and their cousin scenarios.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Generation of Modular and Measurable Validation Scenarios for Autonomous Vehicles Using Accident Data
Autonomous vehicle (AV) technology is positioned to have a significant impact on various industries. Hence, artificial intelligence powered AVs and modern vehicles with advanced driver-assistance systems have been operated in street networks for real-life testing. As these tests become more frequent, accidents have been inevitable and there have been reported crashes. The data from these accidents are invaluable for generating edge case test scenarios and understanding accident-time behavior. In this paper, we use the existing AV accident data and provide a methodology to identify the atomic blocks within each accident, which are modular and measurable scenario units. Our approach formulates each accident scenario using these atomic blocks and defines them in the Measurable Scenario Description Language (M-SDL). This approach produces modular scenario units with coverage analysis, provides a method to assist in the measurable analysis of accident-time AV behavior, and generates accident scenarios and their cousin scenarios.