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This article describes an intelligent system that enables the automatic recognition of road signs from image sequences in road environments. The main difficulties the system has to deal with are related to changes in lighting conditions, obstacles blocking the view, the presence of objects with geometric and chromatic similarities and the absence of previous knowledge about their position and orientation. The application of different techniques allows the system to overcome this variety of problems. Therefore, the road sign recognition system is based on a first pre-processing of images, making use of information about road geometry to isolate those areas of the image where road signs may appear. The detection step uses colour and shape analysis to determine the regions of the image where potential road signs may be located. A third step focuses on recognition and classification using pattern matching and edge feature analysis. The proposed algorithm has been tested in different weather and lighting conditions and roads (one and two-lane roads and motorways), overall, a total of 1200 kilometres with a very high success rate of detection and classification as the experimental results show.

For a car to be able to brake, accelerate or change direction, it needs a sufficient level of friction between its tyres and the pavement surface. The tyres play an important role, but road authorities are only responsible for an adequate level of skid resistance of the pavement surface. The skid resistance of a pavement changes over time due to various processes, such as polishing, wear and weathering, but also temporary effects such as temperature or dust accumulation. Road authorities perform skid resistance measurements on a regular basis in order to be able to plan and perform timely maintenance to restore skid resistance. There are only in Europe 14 skid resistance measurement devices with different working principles and different technical characteristics, such as the type of tyre, the measurement speed, the type and amount of wheel slip, the vertical load, etc. These differences make that the measurements are comparable, but there is no unique correlation, since the response to changes in the friction conditions are different for each measurement device, and especially for each type of rubber used for the testing wheels.

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