Integrated Scenario-based Analysis: A data-driven approach to support automated driving systems development and safety evaluation

TL;DR

This paper introduces an integrated, data-driven scenario analysis method that combines diverse datasets to enhance the development and safety evaluation of automated driving systems, covering a broad spectrum of real-world cases.

Contribution

It presents a novel approach that fuses record-based and continuous data for comprehensive scenario definition, analysis, and test case generation in automated driving safety assessment.

Findings

Combines ten years of crash data with 34 million miles of naturalistic driving data.

Creates representative test scenarios covering routine to fatal events.

Enhances scenario diversity and realism for system testing.

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.