A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

Siegel, Joshua E.; Erb, Dylan C.; Sarma, Sanjay E.

doi:10.1109/tits.2017.2749459

Cited by 350 publications

(197 citation statements)

References 142 publications

(202 reference statements)

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“…The same results would be obtained for other values of x. If we consider that the maximum CBR that can be experienced with a given radio access technology r is max r CBR , then the maximum traffic density that could be supported by this technology is: 1 Each RAT utilizes a single and pre-defined channel.…”

Section: Heterogeneous V2v Communications: Framework and Motivationmentioning

confidence: 52%

“…To this aim, we propose a distributed heterogeneous V2V communications algorithm that allows each vehicle to dynamically select the RAT that is more suitable at each point in time. This study considers a multi-link and multi-RAT vehicular scenario where all vehicles are equipped with different RATs operating in different bands 1 . In line with 5G-PPP [2], 3GPP [4] and ETSI [9], we consider that vehicles are able to simultaneously use different RATs for data transmission and/or reception.…”

Section: Heterogeneous V2v Communications: Framework and Motivationmentioning

confidence: 99%

“…emergency electronic brake lights, intersection collision avoidance or lane change warning) generally require the periodic transmission and reception of broadcast messages that include basic positioning and status information; these messages are known as CAMs (Cooperative Awareness Messages) in Europe and BSMs (Basic Safety Messages) in the US. They can be transmitted using IEEE 802.11p, also known as ITS-G5 (Intelligent Transportation Systems-G5) in Europe and DSRC (Dedicated Short-Range Communications) in the US [1].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Sepulcre

Gozálvez

2021

IEEE Trans. on Mobile Comput.

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Connected and automated vehicles will enable advanced traffic safety and efficiency applications thanks to the dynamic exchange of information between vehicles, and between vehicles and infrastructure nodes. Connected vehicles can utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, a widespread deployment of connected vehicles and the introduction of connected automated driving applications will notably increase the bandwidth and scalability requirements of vehicular networks. This paper proposes to address these challenges through the adoption of heterogeneous V2V communications in multi-link and multi-RAT vehicular networks. In particular, the paper proposes the first distributed (and decentralized) context-aware heterogeneous V2V communications algorithm that is technology and application agnostic, and that allows each vehicle to autonomously and dynamically select its communications technology taking into account its application requirements and the communication context conditions. This study demonstrates the potential of heterogeneous V2V communications, and the capability of the proposed algorithm to satisfy the vehicles' application requirements while approaching the estimated upper bound network capacity.

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Section: Heterogeneous V2v Communications: Framework and Motivationmentioning

confidence: 52%

Section: Heterogeneous V2v Communications: Framework and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Sepulcre

Gozálvez

2021

IEEE Trans. on Mobile Comput.

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“…The advancement of on-board sensing and communication technologies provides on-road vehicles with the capability to connect with surrounding vehicles and infrastructure, which is known as the connected vehicle paradigm [1]. The empowered connectivity shifts the vehicle's role from a transport tool to an intelligent platform that can provide a wide range of services [2]. For example, inter-vehicle connectivity provides vehicles with non-line-of-sight information to inform danger ahead, improve transportation safety [3].…”

Section: Introductionmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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“…[9]. The traffic states can be exchanged among intelligent vehicles, transport infrastructure, and road users, which is a typical application of IoT-based transportation system [10].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

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A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

Cited by 350 publications

References 142 publications

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks

Energy and Information Management of Electric Vehicular Network: A Survey

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

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