Development of an Integrated Test Bed and Virtual Laboratory for Safety Performance Prediction in Active Safety Systems

This paper describes recent progress toward achieving representative and reliable active safety performance assessment of advanced driver assistance systems (ADAS). Because ADAS act within a complex, dynamic traffic environment, reliable evaluation of their safety benefits poses methodological challenges. For a proposed ADAS, its expected contribution to reduction of mortality and injuries as well as false positives should be predicted. To meet these challenges, our approach incorporates identification of target scenarios; calibration and validation of stochastic behavior and accident injury models; stochastic (Monte-Carlo) simulation of target scenarios in varied traffic contexts with/without ADAS; and integration of supporting and corroborating field and laboratory analyses. These include a new controlled, high-throughput approach to sensor testing and algorithm validation in camera-based ADAS using a virtual graphical test bed, which supports systematic identification of critical external conditions that could modify performance or lead to a failure mode. The methodologies introduced here are designed to ensure validity of all key links in the assessment chain, not limited to those aspects that can be assessed in a single test.