## 1 Introduction  
Recent decades have seen an enormous growth in mobility, a large part of which has been absorbed by road transport [1]. This situation has given rise to various negative effects, such as accidents, congestion and contamination, _etc._ Throughout the years numerous measures have been applied to improve road transport. However, in some contexts, it has now become practically impossible to improve on the classic solutions and any progress may seem little. This has led to the introduction of alternatives under the name of Intelligent Transport Systems (ITS), which is the name given to applications that integrate communications and information control and processing into transport systems. Their fundamental objectives, among others, are to reduce accidents, save energy, reduce pollution and increase the efficiency of the transport system [2]. The classic way of classifying safety systems makes a distinction between active safety systems (aimed at reducing the number of accidents) and passive safety (aimed at reducing the consequences of accidents). However, more complex models denominate these groups of systems as primary and secondary systems, respectively, adding other groups, such as driver assistance and tertiary safety, which give rise to pre-collision systems [3, 4]. These systems use information captured by the sensors so that they can act on the control and protection systems in order to reduce the probability and consequences of the accident. They allow taking measures some seconds in advance, allowing new measures to be taken and/or increasing their effectiveness. Some of the actions are automatic braking, automatic action on the steering system to improve the angle of impact, pretensioner activation, preparation of airbags and measures to improve the compatibility between vehicles, such as extendable bumpers, suspension height control, _etc._, or the deployment of measures to minimize the effects of pedestrian accidents. There are two critical aspects in these systems: detecting and interpreting the surroundings of the vehicle and the decision to take action. Regarding the former, it must be analyzed whether there are obstacles that may become potential obstacles in the path of the vehicle. The three technologies that are commonly used for long-range vehicle surroundings detection are computer vision (e.g., [5, 6]), radar (e.g., [7, 8]) and laser scanner (e.g., [9, 10, 11, 12]). Sensor fusion is used to enhance the possibilities of understanding and representing the environment as well as for mitigating the deficiencies of each sensor, and several algorithms have been proposed in the past (e.g., [13, 14, 15, 16, 17, 18]). There is a large set of systems and research projects that uses the information supplied by these sensors and integrate it using obstacle detection and tracking algorithms. This way in [19, 20] a stereovision system that detects pedestrians has been presented, including the necessary algorithms to detect and track a pedestrian, considering whether he is in the trajectory of the car and acting on the car to stop or deviate it if necessary. Similarly in [21, 22] this data fusion has been complemented with GPS-based autonomous driving. On the other hand, in [23] a complete system for detecting and tracking to avoid collisions has been presented but, only in simulation. Moreover, the popularity of digital maps means they can be used as an additional sensor [24, 25, 26, 27, 28, 29], positioning the obstacles in the digital map and interacting with GPS navigation (for example, in the EU-funded project Safespot). The second aspect is how the collision avoidance system warns the driver or acts, since in the case of systems that warn the driver, this is critical to the design of the interface [30, 31]. A more advanced solution considers action on the brake pedal and the steering wheel, which lies within the field of autonomous vehicles.