Road regulation sensing with in-vehicle sensors

The purpose of this research work is twofold. The first problem it attempts to address is the inference of regulators that control the traffic (i.e. traffic signs, traffic lights) for enriching maps with new features. The second is related with how we can assist the drivers by communicating the previously mined information in the context of driving safety. As data sources, we use in-car sensors which are accessed through CAN-Bus (spatial data and dynamic features of motion). We introduce the notion of road-regulation sensing and propose unsupervised methods for mining a subset of common traffic regulators. For detecting anomalous driving behaviour resulted from the violation of the valid local regulations, a probabilistic spatio-temporal approach is proposed based on modelling the routes at the places of interest (here, traffic regulators).