A system for predicting road friction using data from a fleet of connected vehicles (V 1-n) wherein each connected vehicle in the fleet is equipped with respective positioning systems and one or more respective sensor systems capable of determining a plurality of road friction influencing parameters during travel within a road network, wherein each respective vehicle in the fleet of connected vehicles (V 1-n) comprises: a communication arrangement, arranged to communicate with a back end system to continuously report floating car data sets (D 1 -D n) including a position (P 1 -P n) of the connected vehicle within the road network, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position (P 1 -P n) within the road network; and the system for predicting road friction further being characterized in comprising: a central database, residing in the back end system, and arranged to collect the reported floating car data sets (D 1 -D n) of the connected vehicles within the fleet (V 1-n) and to aggregate in the central database a series of such reported floating car data sets (D 1 -D n) over a predetermined time period; and a neural network computer, residing in the back end system, arranged to be trained and validated, using the aggregated reported floating car data sets (D 1 -D n) from the connected vehicles within the fleet (V 1-n), to create a model to predict future road friction for a specific position (P 1 -P n) within the road network; and the neural network computer further arranged to, once trained and validated, receive further floating car data sets to obtain input data to the created model to predict future road friction, for a specific position (P 1 -P n) within the road network or segment of the road network associated with that specific position (P 1 -P n). a communication arrangement, arranged to communicate with a back end system to continuously report floating car data sets (D 1 -D n) including a position (P 1 -P n) of the connected vehicle within the road network, time data and data regarding determined road friction influencing parameters and a determined road friction associated with that position (P 1 -P n) within the road network; and the system for predicting road friction further being characterized in comprising: a central database, residing in the back end system, and arranged to collect the reported floating car data sets (D 1 -D n) of the connected vehicles within the fleet (V 1-n) and to aggregate in the central database a series of such reported floating car data sets (D 1 -D n) over a predetermined time period; and a neural network computer, residing in the back end system, arranged to be trained and validated, using the aggregated reported floating car data sets (D 1 -D n) from the connected vehicles within the fleet (V 1-n), to create a model to predict future road friction for a specific position (P 1 -P n) within the road network; and the neural network computer further arranged to, once trained and validated, receive further floating car data sets to obtain input data to the created model to predict future road friction, for a specific position (P 1 -P n) within the road network or segment of the road network associated with that specific position (P 1 -P n). a central database, residing in the back end system, and arranged to collect the reported floating car data sets (D 1 -D n) of the connected vehicles within the fleet (V 1-n) and to aggregate in the central database a series of such reported floating car data sets (D 1 -D n) over a predetermined time period; and a neural network computer, residing in the back end system, arranged to be trained and validated, using the aggregated reported floating car data sets (D 1 -D n) from the connected vehicles within the fleet (V 1-n), to create a model to predict future road friction for a specific position (P 1 -P n) within the road network; and the neural network computer further arranged to, once trained and validated, receive further floating car data sets to obtain input data to the created model to predict future road friction, for a specific position (P 1 -P n) within the road network or segment of the road network associated with that specific position (P 1 -P n).