*2.3. Mechanomutable Asphalt Materials for the Guidance of Autonomous Vehicles*

According to the levels established by the US National Highways Traffic Safety Administration (NHTSA), there are five levels, ranging from zero (no automation) to four (full self-driving automation) for classifying the autonomy of vehicles [58]. Due to the fact that the majority of the new conventional vehicles have one or more specific control functions that are automated, it can be assumed that Level 1 and Level 2 are now being implemented in real life. Ongoing research by the major vehicle manufacturers is being carried out with the aim of reaching Level 3, where the drivers can concede full control of the safety-critical functions of the vehicle under certain traffic or environmental conditions. Finally, some researchers [2,59,60] consider that to fully achieve Level 3 and Level 4, that is, full self-driving automation, it will be essential to incorporate the necessary infrastructure.

If the benefits of assigning a multifunctional character to the road are also considered, the use of mechanomutable asphalt materials can be extended to encoding the road [2] (Figure 6). Encoding the road entails assigning, alongside it, a type of language that is easily read by magnetic field sensors, with the aim of processing this signal so that it can be converted into specific functions for the vehicles and the rest of the traffic signals in the road infrastructure. Therefore, studies [2] in this field have addressed this issue by evaluating the performance of MAMs to allow for encoded roads, with the aim of establishing design methodologies and guidelines for their real-life implementation.

Figure 6 shows the potential areas of application, such as tunnels (where the GPS signal can be weak) and intersections (where the interaction can also involve traffic signals, pedestrians and personal mobility vehicles).

**Figure 6.** Schema of the applications of the encoded roads.

In addition, it is important to note that, with the emerging concept of smart cities, the mentioned benefits of MAMs would greatly benefit and be used in harmony with already existing smart systems. These other systems include smart sensors (both in the road and on vehicles and traffic signals), artificial intelligence (based on machine learning), image processing, big data and computer vision, to develop better traffic management systems and manage the road network in real time [61–66] (Figure 7).

**Figure 7.** Schema of a novel traffic management system using encoded roads.
