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.
