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Methods for analyzing the efficiency of primary safety measures based on real life accident data
(2009)
Primary safety measures are designed to help to avoid accidents or, if this is not possible, to stabilize respectively reduce the dynamics of the vehicle to such an extent that the secondary safety measures are able to act as good as possible. The efficiency of a primary safety measure is a criterion for the effectiveness, with which a system of primary safety succeeds in avoiding or mitigation the severity of accidents within its range of operation and in interactionwith driver and vehicle. Based on Daimler-´s philosophy of the "Real Life Safety" the reflection of the real world accidents in the systems range of operation is both starting point as well as benchmark for its optimization. This paper deals with the methodology to perform assessments of statistical representative efficiency of primary safety measures. To be able to carry out an investigation concerning the efficiency of a primary safety measure in a transparent and comparable way basic definitions and systematics were introduced. Based on these definitions different systematic methods for estimating efficiency were discussed and related to each other. The paper is completed by presenting an example for estimating the efficiency of actual "single" and "multi" connected primary safety systems.
While accident statistics on a national level are provided by many countries, there is a need for international data that includes more detailed information about the accident, so called in-depth data. As a consequence, accident data projects have been emerging in different regions of the world. This creates a need for comparable and mergeable data from different countries, enabling the use of already existing accident data resources and helping to expedite the improvement of global road safety. While existing approaches focus that mostly on building a comprehensive accident database from scratch, the iGLAD project (Initiative for the Global Harmonization of Accident Data) attempts a more pragmatic approach by building on top of the work already accomplished in this area and complementing it. The target of iGLAD is to help setting up an additional dataset as a compatibility layer between already existing world wide data sets and integrating the structure of these by defining a common data scheme. This dataset is limited to the common denominator between the existing data sets and is inherently rather small and simple. Eventually, an individual converter for each participating accident investigation group will be built that enables pooling all data sets in a common repository. This not only saves costs and time, and hence makes such a target more feasible, but also creates data that is usable right from the start. This paper gives an overview of the current status of iGLAD and first steps taken. Additionally, some methodological aspects are discussed, next to a glance at other projects working currently on related issues, providing additional input for iGLAD. Finally, an overview of next steps and intended future work is given.