Efficient deep neural networks for micromobility vision applications

Today, with the evolution of social behavior models, European regulations on emissions due to climate change and new transport alternatives available on the market, there has been a massive increase in the so-called personal mobility vehicles (PMV) in large cities. The fact that this increase has been so fast has meant that in many cases these vehicles do not have their own lanes or detailed regulations. This means that the public highway is often a shared space between pedestrians and PMV, in the case of sidewalks. Many of these PMVs have in their design a traffic mode that limits their speed to circulate on roads shared with pedestrians, but the reality is that in most cases the PMVs users circulate at a higher speed, putting the rest of the users at risk. This thesis aims to take a first step towards being able to create, in the future, PMVs capable of adapting their speed autonomously depending on the road they travel on.