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Impact severity is a fundamental measure for all in-depth crash investigation projects. One methodology used in the UK is based on the US Calspan software package CRASH3. The UK- in-depth crash investigation studies routinely use AiDamage3 a software package which is based on an updated version of the original CRASH3 algorithm, including enhancements to the vehicle stiffness coefficients. Real world accident-damaged vehicles are measured and their crush is correlated with a library of stiffness coefficients. These measurements are then used, along with other parameters, to calculate the crash energy and equivalent changes of velocity of the vehicles (delta-v), which is a measure of the impact severity. UK in-depth accident studies routinely validate the crash severity methodologies applied as the vehicle fleet changes. This is achieved by analysing crash test data and using the appropriate residual crush damage and other inputs to AiDamage3 and checking the program- outputs with the known crash severity parameters. This procedure checks, at least in part, the default stiffness values in the data libraries and the reconstruction methods used.
Police records about traffic accidents like used by IRTAD (International Road Traffic and Accident Database) and CARE (Community Road Accident Database) do not represent all road injuries. For instance, road accidents of bicyclists without a counterpart are usually not reported. Furthermore, IRTAD-like data contains hardly any information on injury outcome and accident circumstances. This information gap leads to an under-representation of the safety concerns of the most vulnerable road users like children and the elderly both in accident research and safety promotion. Injury registration for the European Injury Database (IDB), in turn, combines details of accident causation with diagnostic information that can be used to assess injury severity and long term consequences. The IDB is collecting data from hospital emergency department patients and is being implemented in a growing number of countries. In this article IDB results on mode of transport and injury outcome are presented from a sample of nine EU member states.
The evaluation of the expected benefit of active safety systems or even ideas of future systems is challenging because this has to be done prospectively. Beside acceptance, the predicted real-world benefit of active safety systems is one of the most important and interesting measures. Therefore, appropriate methods should be used that meet the requirements concerning representativeness, robustness and accuracy. The paper presents the development of a methodology for the assessment of current and future vehicle safety systems. The variety of systems requires several tools and methods and thus, a common tool box was created. This toolbox consists of different levels, regarding different aspects like data sources, scenarios, representativeness, measures like pre-crash-simulations, automated crash computation, single-case-analyses or driving simulator studies. Finally, the benefit of the system(s) is calculated, e.g. by using injury risk functions; giving the number of avoided/mitigated accidents, the reduction of injured or killed persons or the decrease of economic costs.
To date, the Trauma Registry (TraumaRegister DGU-® contains data of approximately 100.000 severely injured patients, 65% of which suffered from a road traffic crash. Thus, it is the world's largest data base for severely injured patients. The article describes the development of the registry and explains how it was rolled out over Germany using the established structure of the German Trauma Network (TraumaNetzwerk DGU-®). In addition, this article presents three typical use cases from the fields of quality management, policy making and system-wide interventions, clinical research and injury prevention. In conclusion, the TraumaRegister DGU-® is a well-established tool for various purposes related to the control and reduction of the burden of road injury. Its ongoing expansion to other countries will support the goal of international benchmarking of hospitals and trauma systems.
The project UR:BAN "Cognitive assistance (KA)" aims at developing future assistance systems providing improved performance in complex city traffic. New state-of-the-art panoramic sensor technologies now allow comprehensive monitoring and evaluation of the vehicle environment. In order to improve protection of vulnerable road users such as pedestrians and cyclists, a particular objective of UR:BAN is the evaluation and prediction of their behaviour and actions. The objective of subproject "WER" is development support by providing quantitative estimates of traffic collisions at the very start and predict potential in terms of optimized accident avoidance and reduction of injury severity. For this purpose an integrated computer simulation toolkit is being devised based on real world accidents (GIDAS as well as video documented accidents), allowing the prediction of potential effectiveness and future benefit of assistance systems in this accident scenario. Subsequently, this toolkit may be used for optimizing the design of implemented assistance systems for improved effectiveness.