Information sharing among autonomous vehicles crossing an intersection

Makarem, Laleh; Gillet, Denis

doi:10.1109/icsmc.2012.6378131

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…A different approach by Lin et al [10] utilizes fuzzy logic to detect and escape distinct local minimum trap cases in a complex and known environment. Additionally, information sharing among agents was used to resolve intersection and collision conflicts by Makarem et al [11], and to efficiently navigate an environment while avoiding collisions by Subramanian et al [12]. Information sharing has also been utilized for creating a virtual map of an initially unknown environment by Wang et al [13].…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Dundar,

Bremdal,

Hakansson

et al. 2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Section: Related Workmentioning

confidence: 99%

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Dundar,

Bremdal,

Hakansson

et al. 2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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“…The systems were more energy efficient compared to the traffic light. With a similar concept in [287], they added inertia and intention of the vehicles to the shared information in order to alleviate the system performance from intersection capacity, fuel consumption, average speed, and traffic smoothness perspectives. Kamal et al [288] utilized a centralized approach based on MPC to safely coordinate the AVs.…”

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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“…It used V2V to share the state information of two vehicles to find the potential collision area and then control the longitudinal velocities of both vehicles to prevent them from reaching the collision area at the same time. Similarly, [13] proposed the method of sharing the state of vehicle inertia to create the navigation function that creates a safe and smooth path without fully stopping at the intersection. Sheng et al [14] propose the method of intersection collision groups: each vehicle broadcasts its collision situation based on the path selection.…”

Section: Introductionmentioning

confidence: 99%

Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication

Wuthishuwong

Traechtler

Bruns

2015

J Wireless Com Network

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The development of autonomous vehicle or self-driving car integrates with the wireless communication technology which would be a forward step for road transportation in the near future. The autonomous crossing of an intersection with an autonomous vehicle will play a crucial role in the future of intelligent transportation system (ITS). The fundamental objectives of this work are to manage autonomous vehicles crossing an intersection with no collisions, maintaining that a vehicle drives continuously, and to decrease the waiting time at an intersection. In this paper, a discrete model of the one-way single intersection is designed. The vehicle-to-infrastructure (V2I) communication is implemented to exchange information between a vehicle and an intersection manager which is the roadside infrastructure. The safe trajectory of autonomous vehicles for the autonomous intersection management is determined and presented by using discrete mathematics.

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Information sharing among autonomous vehicles crossing an intersection

Cited by 9 publications

References 8 publications

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

Multi-Agent Collision Avoidance Method Using Fuzzy Risk Estimation and Information Sharing in Unknown Environments

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication

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