Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs

Yu, Chao; Wang, Xin; Xu, Xin; Zhang, Minjie; Ge, Hongwei; Ren, Jiankang; Sun, Liang; Chen, Bingcai; Tan, Guozhen

doi:10.1109/tits.2019.2893683

Cited by 92 publications

(53 citation statements)

References 40 publications

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“…As discussed earlier, centralized control could be a solution, though it has an exponential growth in terms of complexity, as the agent number grows. In [104], [105] the authors propose the utilization of the so-called Coordination Graph (CG) technique, which decomposes the global payoff function into a linear combination of local payoff functions. As an example, the I-DCG and P-DCG, an identitybased and a position-based coordination graph separation is shown, where the edges of the graph only deal with the cartesian product of the corresponding agents' actions.…”

Section: Driving In Trafficmentioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi

2022

IEEE Trans. Intell. Transport. Syst.

343

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Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as carfollowing, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

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Section: Driving In Trafficmentioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi

2022

IEEE Trans. Intell. Transport. Syst.

343

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“…It could be very beneficial in high-level decision-making and coordination between autonomous vehicles. There have been some prior works that used MARL for AVs [140,141,142].…”

Section: Autonomous and Semi-autonomous Vehiclesmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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“…One of the most prominent examples of this prescription mode deals with routing problems, since they often use simulation tools or predictive models to assess the travel time, pollutant emissions or any other optimization objective characterizing the fitness of the tested routes [ 46 , 47 ]. Other examples of prescription based on data emerge in tactical and strategic planning, such as the modification of public transportation lines [ 48 ], the establishment of special lanes (e.g., taxi, bike) [ 49 ], the improvement of road features [ 50 ], the adaptive control of traffic signaling [ 51 ], the identification of optimal delivery (or pickup) routes for different kinds of transportation services [ 52 ], the incident detection and management [ 53 ], learning for automated driving [ 54 ], or the design of sustainable urban mobility plans based on the current and future demand or the drivers’ behavior [ 55 , 56 ].…”

Section: From Data To Actions: An Actionable Data-based Modeling Wmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs

Cited by 92 publications

References 40 publications

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

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