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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.
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.
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 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