Field Testing of ADAS Technologies in Naturalistic Driving Conditions

This paper evaluates Advanced Driver Assistance Systems (ADASs) in test scenarios derived from naturalistic driving and crash data, mapped to ISO 26262, ISO/PAS 21448 (SOTIF), and ISO 34502. From eight high-risk scenarios, it is validated for left turns across oncoming traffic on a proving ground using a Škoda Superb iV against a soft Global Vehicle Target. ODD and spatiotemporal thresholds are parameterized and speed/acceleration profiles from GNSS/IMU data are analyzed. AEB and FCW performance varies across nominally identical runs, driven by human-in-the-loop variability and target detectability. In successful interventions, peak deceleration reached −0.64 g, meeting UNECE R152 criteria; in other runs, late detection narrowed TTC below intervention thresholds, leading to contact. Limitations in current protocols are identified and argue for scenario catalogs with realistic context (weather, surface, masking) and latency-aware metrics. The results motivate extending validation beyond standard tracks toward mixed methods linking simulation, scenario databases, and instrumented field trials.