SAE Level 2 ADAS Performance in Specific Crash-Imminent Scenarios

This research examined the performance of SAE Level 2 (L2) advanced driver assistance systems (ADAS) in crash-imminent scenarios (CIS), with particular attention to how vehicle configuration like body style and powertrain (internal combustion engine, plug-in hybrid, electric vehicle) influences vehicle system performance. The objectives were to (1) identify CIS relevant to L2-equipped vehicles using crash databases and naturalistic driving studies (NDSs), (2) develop scenario-based test procedures and test matrices, and (3) evaluate system and vehicle responses across configurations and conditions. Multiple crash data sources were analyzed, including NHTSA’s Standing General Order dataset of L2-related crashes, the Fatality Analysis Reporting System, the Crash Report Sampling System, and NDS data from the Second Strategic Highway Research Program and the Virginia Tech Transportation Institute L2 NDS. Coded variable analyses from the datasets identified three common CIS: lane and road departures, rear-end striking events, and intersection conflicts. Supporting variables such as speed, roadway condition, and driver actions were also extracted to characterize scenarios and inform test development. Tests were executed at a closed-track testing facility using four vehicles selected for diversity of L2 systems, body types, and powertrains. Phase 0 exploratory testing assessed vehicle kinematics and L2 responses to refine the test matrix. Phases 1 and 2 conducted controlled evaluations of selected CIS, with expansion factors reflecting real-world crash variability. The testing highlighted interactions between L2 features and active safety systems. For example, results showed that all four vehicles employed distinct hand-off strategies between L2 longitudinal control and active safety systems during rear-end striking crash scenarios, and AEB engagement was strongly correlated with TTC at the moment the vehicle identified the crash partner. This work contributes novel insights into vehicle L2 and ADAS behavior in CIS events across multiple factors and provides a structured framework to evaluate system behavior for those crash-imminent scenarios.