Defining Preventable Boundaries in Automated Driving Systems: A Driver Behavior Model for Scenario-Based Assessments

This study presents a quantitative framework for defining preventable boundaries in scenario-based assessments of Automated Driving Systems (ADS) grounded in a novel driver behavior model. This approach integrates experimental data from two driving simulation studies focusing on cut-in maneuvers on highways. In the first experiment, the evasive responses and decision times of drivers were statistically modeled to characterize the role of cut-in responders, highlighting the behavioral differences between experienced and inexperienced drivers. The second experiment analyzed the behavior of the maneuvering initiator in scenarios in which an ADS performed a cut-in maneuver, capturing reaction times, braking strategies, and risk perception. Statistical comparisons and regression analyses were used to derive preventability criteria, revealing that human drivers prioritize steering over braking in imminent-risk situations, and that experience level significantly influences response timing and choice. By combining empirical data, systematic reviews, and scenario-specific statistical modeling, this study quantifies the variability in human responses, challenging the sufficiency of the fixed-threshold safety benchmarks currently used in ADS validation. Additionally, cross-validation with real-world lane-change datasets demonstrated discrepancies with the prevailing safety models, suggesting overconservatism in the current assumptions. The results provide methodological insights for refining ADS testing standards, and contribute to data-driven regulatory guidance grounded in human performance analytics.