Abstract: A method for predicting a weather-related surface condition of a particular road segment including partitioning the geographical area into a plurality of weather cells, subdividing the geographical area into regions composed of weather cells sharing similar climatic characteristics, and for each defined region, training at least one prediction model on variables derived from weather observations associated with surface conditions observed in the region in question, and associating each road segment of the network with at least two particular prediction models.

Abstract: A method for predicting a surface condition of a road segment. The method including, for each system in a set of road weather information systems, training a predictive model on the basis of data originating from the system, applying each model trained for a particular system to the data originating from the other systems in the set of systems in order to determine a prediction error, grouping the systems according to a similarity in prediction error, associating a particular context with each group of systems and training a predictive model for each group of systems to which a context was associated. Also disclosed is a device for implementing the prediction method.

Abstract: Computer systems and methods for updating and/or supplementing a digital road map through crowdsourcing, based on the generalization of geolocation systems that are integrated in the majority of modern road vehicles. The signals collected by these geolocation systems are used to update and/or supplement a digital road map through crowdsourcing.

Abstract: Computer systems and methods for updating and/or supplementing a digital road map through crowdsourcing, based on the generalization of geolocation systems that are integrated in the majority of modern road vehicles. The signals collected by these geolocation systems are used to update and/or supplement a digital road map through crowdsourcing.