Fuzzy similarities for road environment-type detection by a connected vehicle from traffic sign probabilistic data

The road environment recognition using embedded technologies has been researched intensively recently. Several papers deal with the detection of the urban road environment-type (RET), such as downtown, residential area, and business/industrial area. These RETs can characterize the road environment around an ego-car. A RET detection approach taking into account relevant traffic signs (TSs) that are visible from the ego-car – along its route – was also proposed. It was assumed that the TS data, namely the type and the location of each detected TS along the route, was made available for the purpose by an on-board TS recognition system (TSR). The TS data is constantly updated, aggregated and evaluated in a multi-scale manner by a RET detection system, so one can produce a probability series of occurrence of each TS type with respect to each of the considered urban RETs. In the present paper, we develop a heuristic for dynamic RET detection using fuzzy similarities on a special graph. We also propose the generic process of this approach, which will be the subject of further development and testing.