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Integrated environment for automotive multisensor data fusion system

This paper describes an approach we used for designing an automotive obstacle detection system. This system will be able to detect hazardous situations in road traffic by the data fusion of a set of sensors and to warn the driver when such situations occur. Our approach can be summarized in three points: (1) We used a distributed architecture of smart sensors which are binded by a dedicated data network. (2) Since the experiments on a test tack are often difficult, our system makes possible the recording of all sensor data, through the network, in real time. These data are time stamped and digital video images are also recorded at a frequency of 2 Hz. Then, it becomes possible to make long recording sessions of different road scenarios and to have a pool of data at our disposal for testing and validating obstacle detection algorithms. (3) We use a classification of the sensors to make connection of new sensors to the network easier.