Benefit Evaluation of Crash Avoidance Systems

Abstract

A five-layer hierarchy to integrate models, data, and tools is proposed for benefits assessment and requirements development for crash avoidance systems. The framework is known as HARTCAS: Hierarchical Assessment and Requirements Tools for Crash Avoidance Systems. The analysis problem is multifaceted and large-scale. The driving environment is diverse and uncertain, driver behavior and performance are not uniform, and the range of applicable collision avoidance technologies is wide. Considerable real-world data are becoming available on certain aspects of this environment, although the collection of experimental data on other aspects is constrained by technological and institutional issues. Therefore, analyses of collision avoidance systems are to be conducted by collecting data on nominal operating conditions to the greatest extent possible and by using such data to build models for analysis of the rare, abnormal conditions. HARTCAS provides a framework within which to structure the collection and use of such knowledge. It is described in general terms, and its use is illustrated by analysis of a forward collision warning system. How to quantify the relationships between the effectiveness of a warning and the probability that the warning is a nuisance is shown. System benefits are also quantified.