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Unfälle aufgrund von Falschfahrten sind sehr seltene Ereignisse, welche aber in der Regel eine besonders hohe Unfallschwere aufweisen. Aktuelle Studien aus Deutschland zeigen, dass häufig falsches Linksabbiegen an Anschlussstellen den Ausgangspunkt von Falschfahrten bildet. Im vorliegenden Forschungsvorhaben wurde durch empirische Erhebungen und Fahrten im Fahrsimulator an umgestalteten und nicht umgestalteten Anschlussstellen die Maßnahmenwirkung unterschiedlicher Markierungsvarianten evaluiert. Aus den Ergebnissen wurden Empfehlungen für die optimierte Knotenpunktmarkierung abgeleitet. Im Untersuchungskollektiv waren sowohl signalisierte als auch nicht-signalisierte Anschlussstellen vertreten. An allen empirisch untersuchten Anschlussstellen wurden videogestützte Verkehrserhebungen durchgeführt. Aus den erhobenen fahrer- und umfeldspezifischen Merkmalen konnte das Orientierungs- und Abbiegeverhalten der Linksabbieger analysiert werden. Im Fahrsimulator wurde zusätzlich überprüft, inwieweit gruppenbezogene Ausprägungen bzw. Unterschiede bzgl. des objektiven Fahrverhaltens und der subjektiven Fahrempfindungen auftreten. Im Ergebnis der empirischen Untersuchungen und der Probandenversuche im Fahrsimulator wurde für nicht signalisierte Anschlussstellen eine Markierungsvariante favorisiert, bei der die Wartelinie weiter innen im Knotenpunkt liegt als bisher. Zusätzliche Richtungspfeile und eine innere Abbiegeleitlinie in Verbindung mit weiteren Anpassungen (Sonderform des Zeichens 296 StVO mit Breitstrich, Zeichen 222 StVO eingedreht und durch Leitplatte Zeichen 626 StVO ergänzt) unterstützen den Verkehrsteilnehmer, sich beim Abbiegeprozess vom nachgeordneten Straßennetz auf die Autobahn intuitiv richtig zu verhalten. An signalisierten Anschlussstellen wird ebenfalls der Versatz der Haltlinie in Richtung Knotenpunktmitte, in Verbindung mit den bereits für nicht-signalisierte Anschlussstellen genannten Anpassungen favorisiert. Die Standorte der Signalgeber müssen aber in jedem Fall im Hinblick auf die Bestimmungen der RiLSA (2010) mit der zuständigen Straßenverkehrsbehörde abgestimmt werden. Das Fahrverhalten an den untersuchten Anschlussstellen hat gezeigt, dass die empfohlenen Markierungsvarianten ein intuitiv richtiges Verhalten beim Abbiegen unterstützen und dadurch Falschfahrten vermieden werden.
Beside numerous information about vehicles injuries and environmental data the GIDAS database contains detailed reconstruction data. This data is calculated by a reconstruction engineer who handles about 1000 accidents per year. The spectrum of one reconstruction ranges from simple crossing accidents to complex run-off accidents with rollover events. Especially for complex accident scenarios there is a large effort to design the environment of the accident scene within PC-Crash ®. To reduce the reconstruction time by maintaining the high quality of reconstruction 3D-geodata can be useful. Geodata is available for nearly every area in Germany and can be used for a fast and detailed creation of complex accident environments. In combination with the accident sketch areal images of the accident scene can be created and the participants are implemented in the new-built 3D-reconstruction environment. As a consequence, the characteristics of the terrain can be considered within the reconstruction which is especially important for run-off accidents.
The focus of the technical innovation in the automobile industry is currently changing to sensor based safety systems, which are operating in the pre-crash phase of an accident. To get more information about this pre-crash phase for real accidents a simulation of this phase using the GIDAS database is done. The basics for this simulation are geometrical information about the accident location and the exact accident data out of the GIDAS database. This aggregated information gives the possibility to simulate an exact motion for every accident participant, using MATLAB / SIMULINK, in the pre-crash phase. After the simulation the information about the geometrical positions, the velocities and maneuvers of the drivers to an individual TTC (time to collision) are available. With those results it is possible to develop new useful sensor geometries using pre-crash scatter plots or estimate the efficiency of implemented active safety systems in combination with sensor characteristics. This simulation can be done for every reconstructed accident included in the GIDAS database, so these results can represent a wide spread basis for the further development of active safety systems and sensor geometries and characteristics
The sequence of accident events can be classified by three essential phases, the pre-crash-sequence, the crash-sequence and the post-crash-sequence. The level of reliability of the information in the GIDAS-database (German In Depth Accident Study) is provided predominantly on the passive side. The period to evaluate active safety systems begins already in the pre-crash-sequence. The assessment of the potential of sensor- or communication-based active safety systems can only be accomplished by a detailed analysis of the pre-crash-phase. Hence the necessity to analyze the early period of the accident event in detail arises. This is possible with the help of the digital sketches of the accident site and the simulation of the accident by a simulation method of the VUFO GmbH. After simulating the pre-crash scenario it is possible to generate additional and standardized data to describe the pre-crash-sequences of an accident in a very high detail. These data are documented in a second database called the GIDAS Pre-Crash-Matrix (PCM). The PCM contains various tables with all relevant data to reproduce the pre-crash-sequence of traffic accidents from the GIDAS database until 5 seconds before the first collision. This includes parameters to describe the environment data, participant data and motion or dynamic data. This paper explains the creation of the PCM, the simulation itself and the contents and structure of the PCM. With this information of the pre-crash-sequence for various accident scenarios an improved benefit estimation and development of active safety systems can be made possible.
Millions of kilometers are driven and recorded by car manufacturers and researchers every year to gather information about realistic traffic situations. The focus of these studies is often the recording of critical situations to create test scenarios for the development of new systems before introducing them into the market. This paper shows a novel Analysis and Investigation Method for All Traffic Scenarios (AIMATS) based on real traffic scenes. It also shows how to get detailed information about speeds, trajectories and behavior of all participants without driving thousands of kilometers at the example of conflict situations with animals. Basis of the AIMATS is the identification of the most relevant locations as "Points of Interest" (POI), the recording of the critical situations and their "base lines" at these POI. This paper presents a new method to identify critical scenarios involving both vehicles and animals as well as preliminary results of a study done in Saxony using this new method.
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.
