Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach
文献类型：期刊论文
| 作者 | Luigi D’Alfonso; Francesco Giannini; Giuseppe Franzè; Giuseppe Fedele; Francesco Pupo; Giancarlo Fortino |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2024 |
| 卷号 | 11期号:1页码:141-156 |
| ISSN号 | 2329-9266 |
| 关键词 | Distributed model predictive control distributed reinforcement learning routing decisions urban road networks |
| DOI | 10.1109/JAS.2023.123705 |
| 英文摘要 | In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological 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; conversely, the current vehicle position, resulting from the application 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 constraints are always satisfied during the dynamical platoon evolution 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 platforms, 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 trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations 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 reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54499]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Luigi D’Alfonso,Francesco Giannini,Giuseppe Franzè,et al. Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(1):141-156. |
| APA | Luigi D’Alfonso,Francesco Giannini,Giuseppe Franzè,Giuseppe Fedele,Francesco Pupo,&Giancarlo Fortino.(2024).Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach.IEEE/CAA Journal of Automatica Sinica,11(1),141-156. |
| MLA | Luigi D’Alfonso,et al."Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach".IEEE/CAA Journal of Automatica Sinica 11.1(2024):141-156. |
入库方式： OAI收割
来源：自动化研究所
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