A precrash system based on sensor data fusion of laser scanner and short range radars

The growing interest toward active safety systems leads to a rapid development of different sensors and systems. One key aspect for all systems is the knowledge about the car environment. This knowledge is essential for safety applications such as PreCrash. A PreCrash system generates information about location, moving direction and relative velocity of critical objects in the car environment immediately before an imminent and inevitable accident happens. Different types of actuators can benefit from this information for in-time deployment and the choice of the right deployment energy. In this paper a PreCrash system is proposed, which uses two short range radars and a laser scanner to obtain the required environmental data. The short range radars and the laser scanner will be mounted in the front of a test vehicle. The sensors are able to observe multiple targets within a field of view in front of the car. To develop a highly reliable environment sensing system, competitive fusion approaches using different types of sensors are appropriate. The challenge in realizing such a fusion system is the optimal utilization of the detection performance of each sensor type and the developing of a fast architecture for using it in the automotive environment. As two examples for such a fusion strategy a commonly utilized decision level fusion approach is discussed and a newly developed grid fusion approach is introduced. It is based on a virtual grid in front of the car in which the sensor readings are mapped into. The architecture of the grid is directly adapted to the requirements of the application and the sensor specifications. The data extraction out of the grid is based on simple and well known strategies. Decision strategies to increase the certainty of object identity are also proposed in this paper.