Bøger af Mauro Bellone

The main goal of this research is to investigate new methods intended to increase the degree of driving automation of autonomous vehicles operating in realistic conditions, in both on-road and o-road environments. Safe driving in an urban, extra-urban, or, in general, uncertain environments requires the continuous combination of three important complementary functions: (i) interpretation of the surrounding environment (sense); (ii) evaluation of safe-driving plans taking into account the risk of collision or unsafe driving according to the characteristics of the scene, including any moving participants (plan); (iii) action, considering the outputs of the two previous tasks and involving an on-line reactive navigator able to avoid any choices leading to probabilistically inevitable collisions in the near future (act).

In few years, the age of intelligent machines will grow up towards a worldwide technological transformation and it will be one of the most revolutionary of the history as much as the industrial revolution. Advanced robots will accomplish complex tasks breaking up new boundaries in fields such as fully autonomous driving or personal assistance in a global diffusion. Focusing on autonomous driving, society and industry have great interest in developing such vehicles since they could enhance safety and comfort in the transportation system. While autonomous driving in realistic situations remains a challenging problem, the DARPA challenge (Buehler et al., 2007) and the Urban Challenge in 2004 and 2007 (Leonard et al., 2009) have clearly shown that such a challenge could be reasonably addressed thanks to the recent progresses in the field of perception and autonomous navigation for unmanned vehicles. Urmson et al. (2007) used a hierarchical control system for planning and sensing to win the urban challenge in 2007 with the Carnegie Mellon University Tartan Racing team.