Keep Your Distance, Automated Driving Vehicle!—Toward an Accurate Localization Solutions for Automotive Applications

Chehri, Abdellah; Zimmermann, Alfred; Saadane, Rachid

doi:10.1007/978-981-19-3455-1_10

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…First, the optimal element model is obtained and applied to form a hedge investment. Second, based on the root cause for the K layer, the optimal position of the kth coverage plan is determined by reducing the average GDOP method using the APSO optimization algorithm, asynchronous group subdivision [31].…”

Section: Evaluation Of Centroid Localization CL and Weightedmentioning

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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Section: Evaluation Of Centroid Localization CL and Weightedmentioning

confidence: 99%