It has been the purpose of INTRO Task 2.2, to link pavement skid-resistance to vehicle stopping-distance. The field measurements required for determining parameters and for validation of the model, were performed within INTRO Task 2.1. It should be noted that even if the relationship between skid-resistance and stopping distance is intuitive, its quantification is not straightforward. Actually, stopping distance depends on many factors other than pavement skid-resistance: vehicle type, braking system, tyre, etc. A realistic model had then to be developed. It should be nevertheless simple enough to be easily implemented in a warning system. Pavement skid-resistance is usually measured by monitoring devices, which comprise a vehicle equipped with a 5th wheel. This practice, widely employed by road administrators, has one drawback since monitoring devices are (or can) not always be present on roads to deliver real-time information, especially when weather conditions are bad. An alternative solution would be to estimate the pavement skidresistance using normal vehicles as friction probes although these will not be as accurate as using a standard 5th wheel. This leads to three different ways an ordinary non-probe vehicle may receive and process the information thus made available by the road/tyre skid-resistance measuring devices (5th wheel as well as car probes). The first two methods require that skid-resistance data measured by a different vehicle (or vehicles) need to be translated into valid skid-resistance values relevant for the receiving vehicle. For that, information about this vehicles´ own tyres, weight and braking system need to be taken into account, and in practice this means that the entire vehicle needs to be tested together with the reference monitoring device(s) or probe vehicle(s) on surfaces with various friction levels in order to determine true conversion parameters. However, for such a method to be useful, one must be able to change tyres of the car and it must be possible to use worn tyres, without having to redo these tests. The third method does not suffer from problems with different tyres or tyre wear. Whether it is affected by changing the number of passengers or load is not clear, but that should depend on the particular algorithm used for the friction estimation. Presently there are no commercial systems available for the private consumer, and the accuracy of such systems under development is unclear. The main drawback of method 3 is that no information about friction levels on the road ahead of the vehicle is received. In this study we have focused on the first two methods, using the Arsenal ROADSTAR and the VTI BV12 measurement vehicles as standard monitoring devices. The probe vehicles driving on the road, equipped with a skid-resistance measuring system is represented by a solution from a Swedish Company, NIRA Dynamics AB. The test vehicle itself was represented by four different vehicles: a Renault Clio, a Peugeot 306, a Peugeot 406 and a NIRA dynamics equipped Audi A3. Due to the restricted budget of the INTRO Task 2.1 we could only use one kind of tyre for the braking tests We chose an all season tyre, the Vredestein Quatrac. This deliverable deals with the model development and its validation through two test campaigns performed on test tracks. The work reported is strongly relying on the results from task 2.0 [1] dealing with the system assembly and methods of friction estimation from sensor-equipped cars (also called probe cars), and task 2.1 [2] comprising the test campaigns in Nantes (summer conditions) and Arjeplog (winter conditions).