Ali, Gibran (Virginia Tech Transportation Institute), Sullivan, Kaye (VTTI), Herbers, Eileen (Virginia Tech Transportation Institute), Williams, Vicki (Virginia Tech Transportation Institute), Holley, Dustin (GCAPS), Antona-Makoshi, Jacobo (Virginia Tech Transportation Institute), Kefauver, Kevin (Virginia Tech Transportation Institute)

Integrated Scenario Based Analysis: A Data Driven Approach to Support Automated Driving Systems Development and Safety Evaluation

Scheduled for presentation during the Regular Session "Generating driving scenarios I" (ThBT11), Thursday, September 26, 2024, 15:50−16:10, Salon 19/20

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Several scenario-based frameworks exist to aid in vehicle system development and safety assurance. However, there is a need for approaches that combine different types of datasets that offer varying levels of case severity, data richness, and representativeness. This study presents an integrated scenario-based analysis approach that encompasses scenario definition, fusion, parametrization, and test case generation. For this process, ten years of fatal and non-fatal national crash data from the United States are combined with over 34 million miles of naturalistic driving data. An illustrative example scenario, ``turns at intersection'', is chosen to demonstrate this approach. First, scenario definitions are established from both record-based and continuous time series data. Second, a frequency analysis is performed to understand how often events from the same scenario occur at different severities across datasets. Third, an analysis is performed to show the key factors relevant to the scenario and the distribution of various parameters. Finally, a method to combine both types of data into representative test case scenarios is presented. These techniques improve scenario representativeness in two major ways: first, they populate an entire spectrum of cases ranging from routine events to fatal crashes; and second, they provide context-rich, multi-year data by combining large-scale national and naturalistic datasets.