A tutorial survey on vehicle-to-vehicle communications

Zeadally, Sherali; Guerrero, Jesús; Contreras, Juan

doi:10.1007/s11235-019-00639-8

Cited by 108 publications

(47 citation statements)

References 109 publications

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“…The instantaneous PDP is used to acquire the LRS. For the discrete CIR, it is expressed as h(iT C , m∆τ min ), and the discrete instantaneous PDP (IPDP) can be derived as shown in Equation (2).…”

Section: Local Region Of Stationarity Calculationmentioning

confidence: 99%

“…In the past few years, 5G communication technology has tremendously progressed. Benefiting from the development of 5G communication technology, vehicle-to-vehicle (V2V) communication systems have attracted considerable research interest, with many achievements in this area [ 1 , 2 ]. The industry has implemented many standards for V2V communication, for instance, the IEEE 802.11 p protocol introduced by IEEE [ 3 ] and the LTE-V promoted by the Ministry of Industry [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios

Chen

Shui

2021

Sensors

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The vehicle-to-vehicle (V2V) radio channel is non-stationary due to the rapid movement of vehicles. However, the stationarity of the V2V channels is an important indicator of the V2V channel characteristics. Therefore, we analyzed the non-stationarity of V2V radio channels using the local region of stationarity (LRS). We selected seven scenarios, including three directions of travel, i.e., in the same, vertical, and opposite directions, and different speeds and environments in a similar driving direction. The power delay profile (PDP) and LRS were estimated from the measured channel impulse responses. The results show that the most important influences on the stationary times are the direction and the speed of the vehicles. The average stationary times for driving in the same direction range from 0.3207 to 1.9419 s, the average stationary times for driving in the vertical direction are 0.0359–0.1348 s, and those for driving in the opposite direction are 0.0041–0.0103 s. These results are meaningful for the analysis of the statistical characteristics of the V2V channel, such as the delay spread and Doppler spread. Small-scale fading based on the stationary times affects the quality of signals transmitted in the V2V channel, including the information transmission rate and the information error code rate.

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Section: Local Region Of Stationarity Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios

Chen

Shui

2021

Sensors

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“…To achieve the aim of collaboration, the state of a vehicle needs to include the dynamics of its adjacent vehicles. However, the dimensions of the state cannot be arbitrarily expanded, so several vehicles with the largest impact are chosen, as is shown in equation (1). e subscript m indicates the maximum number of the largest impact vehicles under consideration.…”

Section: State and Action Spacementioning

confidence: 99%

“…In recent years, the development of connected vehicles [1] has prompted the innovation of transportation technology. By introducing the technologies of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, multivehicle coordination has become possible to improve traffic safety and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Jiang

Zhang

2020

Mathematical Problems in Engineering

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Unsignalized intersection control is one of the most critical issues in intelligent transportation systems, which requires connected and automated vehicles to support more frequent information interaction and on-board computing. It is very promising to introduce reinforcement learning in the unsignalized intersection control. However, the existing multiagent reinforcement learning algorithms, such as multiagent deep deterministic policy gradient (MADDPG), hardly handle a dynamic number of vehicles, which cannot meet the need of the real road condition. Thus, this paper proposes a Cooperative MADDPG (CoMADDPG) for connected vehicles at unsignalized intersection to solve this problem. Firstly, the scenario of multiple vehicles passing through an unsignalized intersection is formulated as a multiagent reinforcement learning (RL) problem. Secondly, MADDPG is redefined to adapt to the dynamic quantity agents, where each vehicle selects reference vehicles to construct a partial stationary environment, which is necessary for RL. Thirdly, this paper incorporates a novel vehicle selection method, which projects the reference vehicles on a virtual lane and selects the largest impact vehicles to construct the environment. At last, an intersection simulation platform is developed to evaluate the proposed method. According to the simulation result, CoMADDPG can reduce average travel time by 39.28% compared with the other optimization-based methods, which indicates that CoMADDPG has an excellent prospect in dealing with the scenario of unsignalized intersection control.

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“…Thus, different players have developed and adopted vehicular communications into academia and manufacturing of devices and vehicles. In fact, this market has been moving millions of dollars and is expected to move billions of dollars in the coming years [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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A tutorial survey on vehicle-to-vehicle communications

Cited by 108 publications

References 109 publications

Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios

Analysis of Non-Stationarity for 5.9 GHz Channel in Multiple Vehicle-to-Vehicle Scenarios

Cooperative Multiagent Deep Deterministic Policy Gradient (CoMADDPG) for Intelligent Connected Transportation with Unsignalized Intersection

Computation Offloading for Vehicular Environments: A Survey

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