The HUMATONOMOUS project was a scientific and technical research initiative co-led by Carlos III University of Madrid, publicly funded by Spain’s Ministry of Science, Innovation, and Universities and the European Regional Development Fund (ERDF) under the State Plan’s Research Challenges call for proposals (RTI2018-096036-B-C22). Its main purpose was to define technological strategies to facilitate the harmonious and optimized coexistence of future autonomous vehicles and traditional drivers within complex urban environments, as part of the i-Urbe sector-coordinated project.

From a technical standpoint, the project was structured around the development of advanced algorithms for controlling and simulating mixed traffic, using digital twins of road environments to predict the behavior of human drivers in the presence of automated vehicles. The technical architecture integrated sensor-based cooperative perception systems (LIDAR, cameras, and radar), V2X (vehicle-to-environment) communication protocols, and machine learning mathematical models aimed at improving the autonomous vehicle’s decision-making in high-risk scenarios and at critical intersections, thereby mitigating the unpredictability of the human factor.