Machine Learning Methods for Vehicle Positioning in Vehicular Ad-Hoc Networks

Nayak, Suryakanta; Das, P.; Panda, Satyasen

doi:10.1007/978-981-15-6353-9_7

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Many proposals are present in the literature on different techniques proposed by researchers for resolving different issues related to positioning in vehicular networks. In [17] the authors try to address the problem of traditional technologies in dense urban areas where the Line-of-Sight (LoS) can be obstructed proposing a machine learning method based on Stochastic Gaussian Process (SGP) regression to position vehicles in a distributed vehicular system that uses the received signal vector information together with a continuous approximation of the V2V distance, Angle of Arrival (AoA), and path delay. The use of new technologies such as 5G/6G can represent an effective way to provide accurate positioning of vehicles in a specific area.…”

Section: A Positioning Techniques In Vehicular Networkmentioning

confidence: 99%

CM-SLASM: A Cooperative Multitechnology Simultaneous Localization and Signal Mapping for Vehicles Indoor Positioning

D’Alfonso,

De Rango,

Fedele

et al. 2024

IEEE Sensors J.

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In this paper, a Cooperative Multi-technology Simultaneous Localization and Signal Mapping (CM-SLASM) technique is proposed to improve the signal map accuracy and to build on-thefly a signal map to be used also in indoor environments where conditions can change over time. Moreover, the CM-SLASM combines WiFi, Ultra WideBand (UWB) and LIght Detection And Ranging (LIDAR) signals to improve positioning estimation by sharing information and cooperation among vehicles through Vehicle-To-Vehicle (V2V) communication links. In particular, LIDAR-based distance between vehicles is shared among neighbor vehicles to improve the vehicle positioning estimated by an Extended Kalman Filter (EKF) where WiFi fingerprinting is combined with UWB multilateration. The overall solution where EKF estimation allows to build more precise signal MAP is validated by simulation in a defined indoor scenario where vehicles equipped with different percentages of LIDAR, and different quantities of UWB and WiFi emitters have been considered. The proposed strategy has been validated through an extensive simulation campaign in various scenarios of interest and through a real-world experiment conducted in a laboratory test environment.

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Section: A Positioning Techniques In Vehicular Networkmentioning

confidence: 99%