Decentralized traffic management: A synchronization-based intersection control

Tlig, Mohamed; Buffet, Olivier; Simonin, Olivier

doi:10.1109/icadlt.2014.6864095

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…Besides, velocities were adjusted to ensure safe passing in addition to less energy consumption and delay. Tlig et al [295] defined an agent at each intersection to locally synchronize the arrival time and also the speed of the other AVs vehicles agents thereby they could safely cross the intersection without stopping and consume less energy. In another research work [296], a novel algorithm was proposed that took control of the speed to safely and efficiently route AVs in the non-congested traffic.…”

Section: E Safety Efficiency and Environmentmentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.INDEX TERMS Vehicular ad-hoc networks (VANETs), intersection management (IM), vehicle-tovehicle (V2V), vehicle-to-infrastructure (V2I), trajectory planning (TP), spatio-temporal (ST), connected autonomous vehicles (CAVs), vulnerable road users (VRUs), connected vehicles (CVs), autonomous vehicle (AVs). II. INTERSECTION MANAGEMENT (IM) FUNDAMENTALS A. ARCHITECTURE ALTERNATIVESASHKAN GHOLAMHOSSEINIAN (Graduate Student Member, IEEE) received the bachelor's degree in computer engineering-software in Iran, the M.Sc. degree in computer network engineering from Halmstad University, Sweden, and the Post master's degree in communications for intelligent transport systems from Eurecom, France. He is currently pursuing the Ph.D. degree in electrical engineering and information technology with the Communication Networks Group, Technische Universität Ilmenau, Germany. His research interests include vehicular networks, intelligent transport systems, computer networks, and wireless and mobile networks and communications. JOCHEN...

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Section: E Safety Efficiency and Environmentmentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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“…This strategy decreased the total delay time and total power loss. Moreover, to avoid collisions, improve energy loss, and cross an intersection without stopping, Tlig et al [106] presented a synchronizationbased intersection control to provide proper vehicle speed and arrival time. Considering the worst case, the average vehicle delay of the proposed method does not exceed 6 seconds.…”

Section: Efficiency and Safetymentioning

confidence: 99%

“…As shown in Table B-6 in Appendix B, optimization (e.g., [103], [104], and [107]), rule-based (e.g., [105], [106], and [110]), and hybrid (e.g., [112]) methods have been developed to improve intersection efficiency, decrease environmental impact, and maintain traffic safety. Overall, the proposed methods outperform the base cases by 2.7-94.40%, considering different performance indicators.…”

Section: Efficiency and Safetymentioning

confidence: 99%

Intelligent Intersection Management Systems Considering Autonomous Vehicles: A Systematic Literature Review

Namazi

2019

IEEE Access

128

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Over the past several decades, the development of technologies and the production of autonomous vehicles have enhanced the need for intelligent intersection management systems. Subsequently, growing interest in studying the traffic management of autonomous vehicles at intersections has been evident, which indicates a critical need to conduct a systematic literature review on this topic. This paper offers a systematic review of the proposed methodologies for intelligent intersection management systems and presents the remaining research gaps and possible future research approaches. We consider both pure autonomous vehicle traffic and mixed traffic at four-way signalized and unsignalized intersection(s). We searched for articles published from 2008 to 2019, and identified 105 primary studies. We applied the thematic analysis method to analyze the extracted data, which led to the identification of four main classes of methodologies, namely rule-based, optimization, hybrid, and machine learning methods. We also compared how well the methods satisfy their goals, namely efficiency, safety, ecology, and passenger comfort. This analysis allowed us to determine the primary challenges of the presented methodologies and propose new approaches in this area.INDEX TERMS Autonomous vehicle, intelligent intersection management system, mixed traffic, vehicleto-infrastructure (V2I) communication, vehicle-to-vehicle (V2V) communication.

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“…In the centralized solution in [21], the information of the vehicles is used to predict their trajectories through the intersection, upon which these predictions are used to calculate the optimal crossing sequence. As a more practical approach, the distributed solution in [22] establishes the crossing sequence based on road priorities.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge

Ploeg

Semsar-Kazerooni

Medina

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension towards maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lanereduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.Index Terms-Cooperative driving, interaction protocol, controller design, vehicle platoons, wireless communications. A. Voronov and C. Englund are with RISE Viktoria,

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Decentralized traffic management: A synchronization-based intersection control

Cited by 17 publications

References 6 publications

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Intelligent Intersection Management Systems Considering Autonomous Vehicles: A Systematic Literature Review

Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge

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