While great progress has been made in recent years, there is still much room for improvement in ADAS systems. The frontal collision warning systems and the pedestrian detection systems (and also of cyclists, motorcyclists etc.) are a good example of this, since these systems present difficulties when they have to operate in certain driving conditions (high-speed driving, environments with poor or changing lighting or visibility etc). In addition, most advanced current systems use sensors with reduced or very specialized functionality (RADAR, LIDAR, LASER), which aditionally become relatively expensive. As an alternative, several consolidated companies and start-ups are currently working on improving the functionalities of image-based systems (cameras), foccusing in new algorithms and developing more efficient and capable processors, hence trying to improve their robustness and expanding their functionalities. Such new systems -if achieved- could dominate in the future the ADAS because of its extensive functionality and reduced cost. The present project is proposed in this context but with a different approach: the use of spectrometers (multispectral cameras) of small size for the development of ADAS systems of wide functionality and greater robustness. There are several motivations that encourage us to this approach at this precise moment. On the one hand, the spectacular results that the use of the rich information acquired from hyperspectral sensors (data on hundreds of spectral bands of reflected light), has provided in various fields of science and technology, as already mentioned in previous sections of this memory. It seems clear that the application of this technology to the ADAS, if carried out successfully, can lead to a small revolution in the sector in the near future, and this is precisely the first point that this project wants to influence: explore how and to what extent the use of accurate multispectral information can be useful in ADAS applications and more specifically in the detection, classification and tracking of moving objects, since to date this has not been properly investigated.