A multi-agent based cooperative control model applied to the management of vehicles-trains

Abstract : The use of individual vehicles is becoming more and more important in inner cities, leading to many side problems such as traffic jam, air pollution and accidents. Intelligent vehicles have been studied so as to overcome these problems. Some solutions provided by these works are already available to the general public market such as city safety systems, lane assists,... Many research works are focusing on finding suitable and acceptable solutions to these problems and dealing with traffic jam management. The proposal can tackle the traffic jam problem whether on system level or on individual vehicle providing better control and perception systems aimed at reducing time response and/or at increasing the road capacity.On the vehicle side, one promising solutions is vehicles platoons (vehicles-train) making possible a huge reduction of the longitudinal distance between vehicles and thus allowing an increase of roads capacity. Basically, two main trends can be found literature. On one side, global approaches are based on a common reference frame, generally tied to the vehicles playground, shared by all vehicles of the train. Then, each vehicle behaves according to this shared reference which can be either the trajectory of the first vehicle of the train or a reference trajectory built offline. On the other side, local approaches are based on vehicle local perception abilities. Some methods, based on classical control algorithms or physical-inspired and inter-vehicular interaction link, are developed.Despite numerous research works on this subject, which are focusing on individual vehicle control, few of them consider the platoon control solution on the system point of view. However, this system point of view is particularly important when several platoons have to share the road network and meet at critical nodes such as crossroads and roundabouts. This introduces new issues such as (1) how to organize vehicles and trains of vehicle, (2) the interactions between trains of vehicles and (3) the strategies of sharing the road infrastructure efficient, reliable and safe. Solving these problems will then allow considering vehicles-train solution as a good candidate for solving traffic flow issues at transportation system level.The goal of this thesis is to propose an approach, based on multi-agent paradigm, which aims at dealing with systems level issues focusing mainly on intersections between vehicles-trains of vehicles. Thus, we propose a cooperative control system which relies on multi-level decision processes aimed at dealing with the interaction of platoons at road network nodes. This cooperative control system allows both to maintain the coherence and the safety condition of each involved train of vehicles and to adapt each train components behavior so as to make train shared the road, and especially roundabouts and crossroads, efficiently (i.e. without stopping any vehicle). This cooperative control system is divided into three different levels. The global train state is managed at the train-level decision process based on the train level perceptions. The vehicle-level process makes the decision concerning each individual vehicle according to data provided by the train-level and to the interaction between vehicles. Finally, the motor-level process makes the link between the vehicle-level command and hardware level of vehicles. In this thesis, we focus on the train-level and vehicle-level. When encountering, trains exchange information such as one part of their perceptions.Besides the goal of having an efficient approach so as to enable several vehicles-trains to share the road infrastructure, we also strategies to transform the meetings of vehicles-trains at road nodes into reconfiguration spots where trains can reconfigure and recombine.The developed algorithm are tested in simulation so as to obtain proper evaluation of our proposal using suitable indicators.
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Bofei Chen. A multi-agent based cooperative control model applied to the management of vehicles-trains. Automatic Control Engineering. Université Bourgogne Franche-Comté, 2017. English. ⟨NNT : 2017UBFCA002⟩. ⟨tel-01870536⟩

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