Abstract

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.

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