首页|Autonomous Vehicle Platoons In Urban Road Net-works:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
Autonomous Vehicle Platoons In Urban Road Net-works:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
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In this paper,platoons of autonomous vehicles oper-ating in urban road networks are considered.From a method-ological point of view,the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria.To this end,a novel distributed control architecture is conceived by taking advantage of two methodologies:deep reinforcement learning and model predictive control.On one hand,the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction.On the other hand,a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle.Such tasks are here combined into a single framework:the deep reinforcement learning output(action)is translated into a set-point to be tracked by the model predictive controller;con-versely,the current vehicle position,resulting from the applica-tion of the control move,is exploited by the deep reinforcement learning unit for improving its reliability.The main novelty of the proposed solution lies in its hybrid nature:on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes;on the other hand,time-varying hard con-straints are always satisfied during the dynamical platoon evolu-tion imposed by the computed routing decisions.To efficiently evaluate the performance of the proposed control architecture,a co-design procedure,involving the SUMO and MATLAB plat-forms,is implemented so that complex operating environments can be used,and the information coming from road maps(links,junctions,obstacles,semaphores,etc.)and vehicle state trajecto-ries can be shared and exchanged.Finally by considering as oper-ating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models,several simula-tions have been performed with the aim to put in light the main features of the proposed approach.Moreover,it is important to underline that in different operating scenarios the proposed rein-forcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
Distributed model predictive controldistributed reinforcement learningrouting decisionsurban road networks
Luigi D'Alfonso、Francesco Giannini、Giuseppe Franzè、Giuseppe Fedele、Francesco Pupo、Giancarlo Fortino
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Department of Computer Engineering,Modeling,Electronics and Systems,Universita della Calabria,Via Pietro Bucci,Cubo 42-C,Rende(CS),87036,Italy
Department of Mechanical Engineering,Energy Engineering and Management,Universita della Calabria,Via Pietro Bucci,Cubo 42-C,Rende(CS),87036,Italy
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