Description du projet
Favoriser une réduction de la consommation d’énergie et des émissions de gaz à effet de serre
La conduite autonome et en peloton est considérée comme l’avenir du transport routier car elle promet d’améliorer le rendement et la sûreté du carburant et de réduire la congestion du trafic. La recherche dans ce domaine est toutefois confrontée à des scénarios complexes comportant des interactions humaines et des défis de conception liés au contrôle hiérarchique. Pour y remédier, le projet DiLeBaCo, financé par l’UE, vise à mettre au point de nouveaux algorithmes de contrôle des systèmes multi-agents critiques sur le plan de la sécurité dans des scénarios réels, ainsi qu’à mettre en évidence le rôle des limites locales en matière d’information relatives aux performances et à la sécurité de ces systèmes. En outre, le projet prévoira des incitations pour les agents qui assureront la coordination optimale d’une flotte et ouvriront la voie à la conduite en peloton dans des scénarios complexes.
Objectif
Multi-agent systems offer a great potential to improve the quality of modern society life. In the near future fleets of autonomous cars will be able to reduce traffic congestion and fuel consumption while increasing road safety. With almost half of all freight being transported by road, it makes up approximately a quarter of the total EU energy consumption and accounts for 18% of the greenhouse emissions. Fuel reduction in this area will have a significant impact on the environment. One way to achieve such reductions is through platooning where heavy-duty vehicles drive close to each other to reduce their aerodynamic drag and thus increase their fuel efficiency. While autonomous driving and platooning are areas of active research, open challenges arise in complex traffic scenarios with human interactions. Another challenge is that hierarchical control design with several different layers is required. The specific goals of the project are to develop novel algorithms for the control of safety-critical multi-agent systems in real-world scenarios, to understand the role of local informational constraints on the performance and safety of such systems and to design incentives for the individual agents that lead to a desired coordination of a fleet. This way global objectives will be optimized while accounting for complex traffic situations. The scientific contribution lies in combining and extending recent results from distributed predictive control, statistical learning and game theory as well as understanding the role of informational constraints in distributed learning-based control of multi-agent systems. The developed methods will have a high impact on both industry and society. In particular, the project will enable platooning in more complex scenarios, which has the potential to reduce fuel consumption of the transportation sector by up to 10% and thus make a significant contribution to the overall energy consumption and greenhouse emissions of the EU.
Champ scientifique
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
100 44 Stockholm
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