Sonstige
Because of actual developments and the continuous increase in the field of drive assistant systems, representative and detailed investigations of accident databases are necessary. This lecture describes the possibility to estimate the potential of primary and secondary safety measures by means of a computerized case by case analysis. Single primary or secondary safety measures as well as a combination of both are presented. The method is exemplarily shown for the primary safety measure "Brake Assist" in pedestrian accidents. Regarding accident prevention only the primary safety measure is determined.
Although the number of road accident casualties in Europe (EU27) is falling the problem still remains substantial. In 2011 there were still over 30,000 road accident fatalities. Approximately half of these were car occupants and about 60 percent of these occurred in frontal impacts. The next stage to improve a car's safety performance in frontal impacts is to improve its compatibility. The objective of the FIMCAR FP7 EU-project was to develop an assessment approach suitable for regulatory application to control a car's frontal impact and compatibility crash performance and perform an associated cost benefit analysis for its implementation. This paper reports the cost benefit analyses performed to estimate the effect of the following potential changes to the frontal impact regulation: • Option 1 " No change and allow current measures to propagate throughout the vehicle fleet. • Option 2 " Add a full width test to the current offset Deformable Barrier (ODB) test. • Option 3 " Add a full width test and replace the current ODB test with a Progressive Deformable Barrier (PDB) test. For the analyses national data were used from Great Britain (STATS 19) and from Germany (German Federal Statistical Office). In addition in-depth real word crash data were used from CCIS (Great Britain) and GIDAS (Germany). To estimate the benefit a generalised linear model, an injury reduction model and a matched pairs modelling approach were applied. The benefits were estimated to be: for Option 1 "No change" about 2.0%; for Option 2 "FW test" ranging from 5 to 12% and for Option 3 "FW and PDB tests" 9 to 14% of car occupant killed and seriously injured casualties.
The number of injured car occupants decreases constantly. Nevertheless, they account for nearly 50% of all fatalities and about 44% of all seriously injured persons in German traffic accidents. Further reductions of casualties require multiple efforts in all parts of traffic safety. In this paper a detailed analysis of the important pre-hospital rescue phase was done. The basis for future improvements is the knowledge about injury causation of car occupants in combination with other corresponding influence factors. For that reason more than 1.200 severe (AIS3+) injuries of frontal car occupants were analyzed. For the most relevant injuries of car occupants multivariate analysis models were created to predict the probability of these injuries in a real crash scenario. In addition to the collision severity different influence factors like impact direction, seat belt usage, age of the occupant, and gender were analyzed. Furthermore, the models were checked regarding the goodness of fit and all results all results were checked concerning their robustness. The prediction models were created on the basis of 5.000 car accidents. Afterwards, the models were validated using 4.000 different car accidents. The prediction of the probability of severe injuries could be used for different applications in the field of traffic safety. One possibility is the implementation of the models in a tool for the on-the-spot diagnosis. The background for the development of such applications is the fact, that there are only limited diagnostic possibilities available at the accident scene. Nevertheless, the rescue forces have to make essential decisions like the alerting of the necessary medical experts, appropriate treatment, the type of transportation and the choice of an adequate hospital. These decisions quite often decide between life and death or influence the long-term effects of injured persons. At this point, indications of expectable injuries could help enormously. To enable even persons with limited technical knowledge to use the tool, a procedure was developed that facilitates the assumption of the given crash severity. Another important possibility for the application of the prediction models is the use for the qualification of information sent by e-call systems.
The paper aims to study the injury risk and kinematics of pedestrians involved in different passenger vehicle collisions. Furthermore, the difference of pedestrian kinematics in the accidents involved minivan and sedan was analyzed. The 18 sample cases of passenger car to pedestrian collisions were selected from the database of In-depth Investigation of Vehicle Accident in Changsha of China (IVAC),of which the 12 pedestrian accidents involved in a minivan impact for each case, and the 6 accidents in a sedan impact for each. The selected cases were reconstructed by using mathematical models of pedestrians and accident vehicles in a multi-body dynamic code MADYMO environment. The logistic regression models of the risks for pedestrian AIS 3+ injuries and fatalities were developed in terms of vehicle impact speed by analyzing the minivan-pedestrian and sedan-pedestrian accidents. The difference of pedestrian kinematics was identified by comparing the results from reconstructed pedestrian accidents between the minivans and sedans collisions. The result shows that there is a significant correlation among the impact speed and the severity of pedestrian injuries. The minivan poses greater risk to pedestrian than sedan at the same impact speed. The kinematics of pedestrian was greatly influenced by vehicle front shape.
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.
The main focus of the benefit estimation of advanced safety systems with a warning interface by simulation is on the driver. The driver is the only link between the algorithm of the safety system and the vehicle, which makes the setup of a driver model for such simulations very important. This paper describes an approach for the use of a statistical driver model in simulation. It also gives an outlook on further work on this topic. The build-up process of the model suffices with a distribution of reaction times and a distribution of reaction intensities. Both were combined in different scenarios for every driver. Each scenario has then a specific probability to occur. To use the statistical driver model, every accident scene has to be simulated with each driver scenario (combinations of reaction times and intensities). The results of the simulations are then combined regarding the probabilities to occur, which leads to an overall estimated benefit of the specific system. The model works with one or more equipped participants and delivers a range for the benefit of advanced safety systems with warning interfaces.
The study aimed at estimating the impact of pedelecs (with an assumed higher speed than bicycles) on the traffic accident severity in Germany for different penetration rates. The analysis shows that in many real situations (68%) an electrical support of bicycles has no influence on the sequence of accident events. Taking into account a number of unreported "single bicycle accidents", the adoption of similar traffic behavior and similar age distribution, the authors determined a shift of 400 former slightly to seriously injured cyclists in Germany per year. Overall this would be an increase of approximately 2.3% in case of 10% of pedelec penetration with the pessimistic assumption of 10 km/h speed increase although first natural driving studies predict a much lower average speed increase of pedelecs. The hypothesis verbalized in the initial question whether a higher distribution of pedelecs will result in more severe accidents in Germany is not verified. The study shows that electrical support didn"t result in higher collision speed in general. In many accident situations, the speed of pedelecs has only a minor influence on the accident severity. Further research focusing on a possible change of driver behavior especially in new target groups (elderly people) will be needed.
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.
This study aimed at developing an injury estimation algorithm for AACN technologies for Germany and compared them to findings based on Japanese data. The data to build and to verify the algorithm was obtained from the German in-depth Accident Database (GIDAS) and split into a training and a validation dataset. Significant input variables and the generalized linear regression model to predict severe injuries (ISS>15) were selected to maximize area under the receiver operating characteristic curve (AUC). Probit regression with the input parameter multiple impact, delta v, seatbelt use and impact direction gave the largest AUC of 0.91. Sensitivity of the algorithm was validated at 90% and specificity at 76% for an injury risk threshold of 2%. It appears that no major differences between Japan and Germany exist for injury estimation based on delta v and impact direction. However, far side impact and multiple crash events appear to be associated with a larger risk increase in the German data.