Detecting Change in the Urban Road Environment Along a Route Based on Traffic Sign and Crossroad Data

Occurrences of traffic signs that belong to certain sign categories and occurrences of crossroads of various topologies are utilized in detecting change in the urban road environment that moves past an ego-car. Three urban environment types, namely downtown, residential and industrial/commercial areas, are considered in the study and changes between these are to be detected. In the preparatory phase, the ego-car is used for traffic sign and crossroads data collection. In the application phase, the ego-car hosts an advanced driver assistance system (ADAS) that captures and analyzes images of the road environment and computes the required input data to the proposed road environment detection (RoED) subsystem. A statistical inference method relying on the minimum description length (MDL) principle was applied to the change detection problem at hand. The above occurrences along a route are seen as a realization of an inhomogeneous marked Poisson process. Page-Hinkley change detectors tuned to empirical data were set to work to detect change in the urban road environment. The process and the quality of the change detection are demonstrated via examples from three urban settlements in Hungary.