An evidential fusion architecture for advanced driver assistance

In this paper, we deal with an original advanced driver assistance system (ADAS) based on the use of omnidirectional vision and an evidential fusion architecture. The panoramic perception solution permits us to address efficiently the problem of close vehicles detection but also the monitoring side traffic system. The fusion and integration of this sensorial data stream is assumed by a credibilist architecture based on the transferable belief model (TBM) of SMETS. This paradigm permits the filtering of false alarms efficiently by an optimal management of the uncertainties estimation.