Descripción del proyecto
Allanar el camino para reducir el consumo energético y las emisiones de gases de efecto invernadero
La acumulación de vehículos y la conducción autónoma se consideran el futuro del transporte por carretera, el cual promete impulsar la seguridad y la eficiencia del combustible, así como reducir la congestión del tráfico. Sin embargo, la investigación en este ámbito afronta complejos escenarios que implican interacciones humanas y desafíos en el diseño del control jerárquico. Para abordar esta cuestión, el proyecto DiLeBaCo, financiado con fondos europeos, tiene como objetivo desarrollar nuevos algoritmos para el control de sistemas multiagentes con un nivel crítico de seguridad en escenarios reales, así como arrojar luz sobre el papel de los límites informativos locales en el rendimiento y la seguridad de dichos sistemas. El proyecto también diseñará incentivos para agentes individuales que llevarán a la coordinación óptima de una flota y allanarán el camino para la acumulación de vehículos en escenarios complejos.
Objetivo
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.
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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
100 44 Stockholm
Suecia