Effects of Coordinated Formation of Vehicle Platooning in a Fleet of Shared Automated Vehicles: An Agent-based model

Wang, Senlei; Correia, Gonçalo Homem de Almeida; Lin, Hai

doi:10.1016/j.trpro.2020.03.112

Cited by 13 publications

(3 citation statements)

References 13 publications

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“…There is also a substantial body of literature in the realm of platooning. In [19], the author explores the effect of the coordinated formation of vehicle platooning, assesses the effectiveness of an autonomous mobility-on-demand system (AMoD), and employs an agent-based model (ABM) for analysis. In [20], the authors investigate the possibility of flexible platooning, utilizing different merging methods.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Highway Platooning

Kolat,

Bécsi

2023

Electronics

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The advent of autonomous vehicles has opened new horizons for transportation efficiency and safety. Platooning, a strategy where vehicles travel closely together in a synchronized manner, holds promise for reducing traffic congestion, lowering fuel consumption, and enhancing overall road safety. This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. We delve into the world of MARL, which empowers vehicles to communicate and collaborate, enabling real-time decision making in complex traffic scenarios. PPO, a cutting-edge reinforcement learning algorithm, ensures stable and efficient training for platooning agents. The synergy between MARL and PPO enables the development of intelligent platooning strategies that adapt dynamically to changing traffic conditions, minimize inter-vehicle gaps, and maximize road capacity. In addition to these insights, this article introduces a cooperative approach to Multi-Agent Reinforcement Learning (MARL), leveraging Proximal Policy Optimization (PPO) to further optimize autonomous vehicle platooning. This cooperative framework enhances the adaptability and efficiency of platooning strategies, marking a significant advancement in the pursuit of intelligent and responsive autonomous vehicle systems.

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Section: Related Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Highway Platooning

Kolat,

Bécsi

2023

Electronics

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“…It can enable them to efficiently coordinate vehicles that share similar destinations, forming platoons that maximize various benefits. These advantages include enhanced travel times and significant energy savings as reported in [14], [15], and [16].…”

Section: A Origin-destination Matricesmentioning

confidence: 99%

Mobility Analytics of Fans During the 2021 FIFA Arab Cup™ Football Tournament in Qatar

Abidi,

Filali

2023

IEEE Open J. Intell. Transp. Syst.

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The FIFA Arab Cup, a test event for the 2022 FIFA World Cup, took place in Qatar from November to December 2021. The event showcased 32 matches across six venues that will also be utilized in the World Cup. This paper presents a groundbreaking spatiotemporal analysis of traffic mobility during the event, using data collected from WaveTraf road sensors. The sensors detect and track Bluetooth and WiFi-enabled user devices, allowing for an analysis of user mobility, including the origin of spectators, the time taken to reach the stadium, dwell time inside the stadium, and the time taken to return to their origin after leaving the stadium. The study processed tens of millions of records, overcoming challenges such as filtering data anomalies and validating and preparing the data for analysis. The findings offer a comprehensive understanding of user mobility patterns during the event, which is valuable information for event organizers, city planners, and transportation providers to optimize services and enhance the overall user experience. Furthermore, the study highlights the importance of leveraging advanced technology to improve the planning and execution of large-scale events and transportation systems. The research showcases the power of data analytics in providing key insights into human mobility.

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“…The considerable amount of literature can be found on different perspectives of platooning, such as platoon formation [ 66 , 67 ], fuel consumption [ 68 , 69 ], CO 2 emission reduction [ 70 ], communication , string stability [ 71 ], control and coordination strategies [ 72 ], and security and safety [ 73 , 74 ]. However, relatively less importance has been directed to study how to elect an experienced driver, most suitable and trustworthy candidate vehicle as a platoon leader.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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Effects of Coordinated Formation of Vehicle Platooning in a Fleet of Shared Automated Vehicles: An Agent-based model

Cited by 13 publications

References 13 publications

Multi-Agent Reinforcement Learning for Highway Platooning

Multi-Agent Reinforcement Learning for Highway Platooning

Mobility Analytics of Fans During the 2021 FIFA Arab Cup™ Football Tournament in Qatar

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

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