Communication and Localization Techniques in VANET Network for Intelligent Traffic System in Smart Cities: A Review

Chehri, Abdellah; Quadar, Nordine; Saadane, Rachid

doi:10.1007/978-981-15-5270-0_15

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…x [2] refer to the first 2N (N − 1)(T + 1) elements and the last N T m elements of x, respectively. The same notations…”

Section: Define a Matrixmentioning

confidence: 99%

“…The existing issues, together with the rapid development of autonomous driving technologies and information technologies, prompt the emergence of connected autonomous vehicles (CAVs), which provide a cooperative driving solution that can potentially alleviate the problem [1]. CAVs are generally equipped with one or more communication devices that enable the information exchange with other on-road vehicles (vehicleto-vehicle), roadside infrastructure (vehicle-to-infrastructure), and cloud device (vehicle-to-cloud) [2]. Such communication conveys the driving intention between CAVs and forms the physical basis of cooperative driving.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Zhenmin

Shen

2023

IEEE Trans. Intell. Transport. Syst.

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“…x [2] refer to the first 2N (N − 1)(T + 1) elements and the last N T m elements of x, respectively. The same notations…”

Section: Define a Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Zhenmin

Shen

2023

IEEE Trans. Intell. Transport. Syst.

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“…The sensors deployed along the roadside of the highway to collect traffic information have been evolving considerably [27]. Conventional traffic sensors provide aggregate traffic measures, such as traffic flow, speed, and occupancy, for traffic agencies to monitor traffic conditions and manage traffic operations in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Lei

Zhao

et al. 2022

IET Intelligent Trans Sys

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Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars.

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“…It produces very correct results in open and plane areas where communication is online, and at least four satellites are available for GPS. Localization gains information from the vehicle in this method navigating vehicle-automated vehicle Nemours and vehicle tracking; while in localization, GPS use the de facto standard of localization, and there are two error for localization: one is the global, and the other is local factor error localization algorithm that is executed in three stages like measurement updates and communication updates, and the other one is position estimation, and the planed localization scheme is to depend on RSU [7,8].…”

Section: Introductionmentioning

confidence: 99%

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

et al. 2023

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Vehicular ad hoc networks (VANETs) are an eminent area of intelligent transportation systems (ITS) which includes vehicle tracking, positioning, and emergency warnings. For protective applications, vehicle localization in the urban area is a significant problem. Global Positioning Systems (GPS) are one of the many solutions that have been offered, although they do not offer accuracy. To locate a target vehicle accurately, a unique approach called received signal strength- (RSS-) based localization scheme has been proposed. By detecting signals within its range, it establishes communication through roadside units (RSUs) and determines the typical RSS. Following the discovery of the RSS, the RSS-based localization algorithm helped in determining the precise position of the vehicle. The high signal-to-noise ratio of the proposed algorithm, which is derived from neighbouring RSUs, is its key component. The vehicle’s Cramer Rao lower bound (CRLB) is examined after its position is discovered. Various experimentations considering dynamic vehicles ranging from 2 to 100, localization error, ranging error, and throughput have been conducted which demonstrates that the proposed algorithm (RSS) has better results (87%, 80%, and 120) than other well-established least squares (LS) and weighted least squares methods (WLS).

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Communication and Localization Techniques in VANET Network for Intelligent Traffic System in Smart Cities: A Review

Cited by 10 publications

References 14 publications

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

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