91 Fahrzeugkonstruktion
Neue Herausforderungen an die Unfallforschung durch Fahrerassistenz und automatisiertes Fahren
(2019)
Unfallrekonstruktion hat die Ableitung von Maßnahmen zur Minimierung der Unfallfolgen ermöglicht, vor allem durch Verbesserungen bei passiven Sicherheitseinrichtungen, aber auch durch die Verbesserung der Rettungskette, beispielsweise eCall. Heute können aktive Sicherheitssysteme die Unfallfolgen bereits vor der eigentlichen Kollision reduzieren oder durch Umfeldwahrnehmung und mittels Eingriff in die Fahrzeugsteuerung gegebenenfalls sogar vollständig verhindern. Funktionen, die aktiv in die Fahrzeugsteuerung eingreifen, lassen sich nach ihrer Wirkweise unterscheiden: zum einen handelt es sich um kontinuierlich automatisierende Funktionen, die meist länger aktiv bleiben (zum Beispiel ACC). Zum anderen um Funktionen, die in kritischen Fahrsituationen temporär in die Fahrzeugsteuerung eingreifen. Aufgezeigt wird, welche Konsequenzen und Risiken in Bezug auf diese Systeme sowie für bestimmte (zum Beispiel kritikale) Fahrsituationen anzunehmen sind. Zur Bewertung von aktiven Reglern, die in kritischen Fahrsituationen eingreifen, sind Unfalldaten nur noch eingeschränkt tauglich. Ähnliches gilt für die Bewertung von Ereignissen/ Zuständen im Rahmen kontinuierlicher Fahrzeugsteuerung, vor allem, wenn diese weiter vorausliegen. Wirkzusammenhänge automatisierter Fahrfunktionen müssen jedoch - gerade für den Mischverkehr mit konventionell gesteuerten Fahrzeugen - identifiziert werden. Dafür wird eine Szenariendatenbank mit relevanten Verkehrssituationen benötigt, in die Daten aus Naturalistic Driving Studies (NDS), aus Fahrversuchen oder Versuchen im Fahrsimulator eingehen können. Die zunehmende Durchdringung der Fahrzeugflotte mit kontinuierlich automatisierten Fahrfunktionen lässt eine Abnahme kritischer Fahrsituationen und eine Reduktion der Zahl der Verkehrsopfer erwarten. Allerdings verbleibt eine Restzahl an systemimmanenten Unfällen, die als unvermeidbar gelten müssen.
Bus or heavy vehicle passenger accidents are rare events, compared with car accidents, but sometimes leads to a large number of victims especially in rollover crash scenarios. Two accidents occurred in Portugal in 2007 and 2013 in which 28 people died and more than 50 are injured, shown the importance of the investigation of such accidents. For the investigation of these accidents multidisciplinary teams are constituted with engineers and police officers. All the factors involved are taken into consideration including road design, traffic signs, maintenance and hardware, human factors, and vehicle factors. In this work a methodology to an accurate collection of the data is proposed. From the information collected the accident is reconstructed using the PC-CrashTM software. From this all the contribution factors are determined and recommendations to mitigate these crashes are listed. These two accidents are rollover accidents and the analysis of the injuries and its correlation with the use of retention systems is very important. From the medical data and with the dynamics of the accident determined simulations of the occupants with biomechanical models are carried out in order to evaluate the effect of the retention systems in the injuries. This analysis is based on injury criteria (such as Abbreviated Injury Score (AIS) or Injury Severity Scale (ISS)). With this it is possible to determine if the seat belt was worn or not.
Since its creation in 2011 the Pre-Crash-Matrix (PCM) offers the possibility to observe the pre-crash phase until five seconds before crash for a wide range of accidents. Currently the PCM contains more than 8.000 reconstructed accidents out of the GIDAS (German In-Depth Accident Study) database and is enlarged continuously by more than 1.000 cases per year. Hence, a detailed investigation of active safety systems in real accident situations has been made feasible. The PCM contains all relevant data in database format to simulate the pre-crash phase until the first collision of the accident for a maximum of two participants. This includes the definition of the participants and their characteristics, the dynamic behavior of the participants as time-dependent course for five seconds before crash as well as the geometry of the traffic infrastructure. The digital sketch of the accident and information from GIDAS as well as from supplementary databases represent the main input for the simulation of the pre-crash phase of an accident with the VUFO simulation model VAST (Vufo Accident Simulation Tool). This simulation in turn embodies the foundation of the PCM. The PCM underlies continual improvements and enhancements in consultation with its users. In addition to collisions of cars with other cars, pedestrians, bicycles and motorcycles the PCM now also covers car to object and car to truck collisions. The paper illustrates car to truck collisions as a showcase and explains perspectives for further developments. In 2016 a more detailed definition of the contour of the vehicle was added. Furthermore, the geometrical surroundings of the accident site will be provided in a new structure with a higher level of detail. Thus, a precise classification of road marks and objects is possible to further improve the support of developing and evaluating ADAS. This paper gives an overview about the latest developments of the PCM with its innovations and provides an outlook to upcoming enhancements. Besides potential areas of application for the development of ADAS are shown.
For more than a decade, ADAC accident researchers have analysed road accidents with severe injuries, recording some 20,000 accidents. An important task in accident research is to determine the causative factors of road accidents. Apart from vehicle engineering and human factors, accident research also focuses on infrastructural and environmental aspects. To find out what accident scenarios are the most common in ADAC accident research and what driver assistance systems can prevent them, our first task was to conduct a detailed accident analysis. Using CarMaker, we performed a realistic simulation of accident scenarios, including crashes, with varying parameters. To begin with, we made an initial selection of driver assistance systems in order to determine those with the greatest accident prevention potential. One important finding of this study is that the safety potential of the individual driver assistance systems can actually be examined. It also turned out that active safety offers even much more potential for development and innovation than passive safety. At the same time, testing becomes more demanding, too, as new systems keep entering the market, many of them differing in functional details. ADAC will continue to test all driver assistance systems as realistically as possible so as to be able to provide advice to car buyers. Therefore, it will be essential to develop and improve test conditions and criteria.
In 2016 the seventh ESAR conference (Expert Symposium on Accident Research) was held in Hannover. ESAR is an international convention of experts, who analyze traffic accidents all over the world and discuss their results in this context, conducted at the Medizinische Hochschule Hannover every 2 years. It connected representatives of public authorities, engineers in automotive development and scientists and offers a forum with particular emphasis on In-Depth-Analyses of accident statistics and accident analyses. Special focus is placed on research on the basis of so-called "In-Depth-Accident-Investigations" [data collections at the sites of the accidents], which are characterized by extensive documentations of the sites of the accidents, of the vehicles as well as of the injuries, encompassing several scientific fields. ESAR aims at a multi-disciplinary compilation of scientific results and at discussing them on an international, scientific level. It is thus a scientific colloquium and a platform for exchanging information for all accident researchers. Experiences in accident prevention as well as in the complex field of accident reconstruction are stated and new research fields are added. Existing results of long-term research work in Europe, the US, Australia and Japan include different infrastructural correlations and give findings on population, vehicle population and driver characteristics, which offer a basis for recommendations to be derived and measures for increasing road safety.
Event data recorders (EDRs) are a valuable tool for in-depth investigation of traffic accidents. EDRs are installed on the airbag control module (ACM) to record vehicle and occupant information before, during, and after a crash event. This study evaluates EDR characteristics and aims at better understanding EDR performance for the improvement of accident reconstruction with more reliable and accurate information regarding accidents. The analysis is based on six crash tests with corresponding EDR datasets.
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.
Today's volumes of traffic require more and more responsibility from each individual road user in their interactions. Those who drive motor vehicles have the singular obligation to minimise the risk of accidents and hence the severity of injuries, particularly with a view to the most vulnerable road users such as motor bikes, bikes and pedestrians. Since responsible and pro-active driving depends first and foremost on the visual information relayed by our eyes and the visual channel this requires good command of the traffic and all-round visibility from our driver's seat. Granted that human error can never be fully excluded, improving visibility around the car is nevertheless an urgent priority. To do so, we need to rate visibility in the most realistic driving situations. Since the existing visibility metrics and methodology are not applicable to real-life driving situations, this study aimed at developing a new visibility rating methodology based on real-life accident scenarios. On the basis of the cases documented by the accident research project, this study analysed criteria indicative of diminishing visibility on the one hand and revealing some peculiarities in connection with the visibility issue on the other. Based on the above, the project set out to develop a rating methodology allowing to assess all-round visibility in various road situations taking into account both driver and road geometries. In this context, the assessment of visibility while turning a corner, crossing an intersection and joining traffic on a major road (priority through route) is of major importance. The first tests have shown that critical situations can be avoided by adapting the relevant geometries and technical solutions and that significant improvements of road safety can be derived therefrom.
For the estimation of the benefit and effect of innovative Driver Assistance Systems (DAS) on the collision positions and by association on the accident severity, together with the economic benefit, it becomes necessary to simulate and evaluate a variety of virtual accidents with different start values (e.g. initial speed). Taken into account the effort necessary for a manual reconstruction, only an automated crash computation can be considered for this task. This paper explains the development of an automated crash computation based on GIDAS. The focus will be on the design of the virtual vehicle models, the method of the crash computation as well as exemplary applications of the automated crash computation. For the first time an automated crash computation of passenger car accidents has been realized. Using the automated crash computation different tasks within the field of vehicle safety can be elaborated. This includes, for example, the calculation of specific accident parameters (such as EES or delta-V) for various accident constellations and the estimation of the economic benefit of DAS using IRFs (Injury Risk Functions).
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.