Inferring the actual urban road environment from traffic sign data using a minimum description length approach

In our paper, we focus on a group of traffic signs and use traffic sign logs to statistically infer the type of urban environment in which a car is being driven. The traffic signs are either perceived and logged by a human data entry assistant, or preferably automatically detected and logged by an on-board traffic sign recognition and logging system. An entry in the log-file records the traffic sign type and the along-the-route location of the sign. Furthermore, in case of collecting training data it records also the actual road environment category entered by the data entry assistant. The logs are seen as realizations of an inhomogeneous marked Poisson process, and the minimum description length (MDL) principle is applied to infer the actual environment. The aim of this approach is to encode the current data in the shortest possible way ‒ assuming stochastic models derived from data collected earlier ‒ and thereby accept the corresponding model and environment as actual. To evaluate the quality of classification, the inferred environment categories are compared to the ground truth data.