Citation

Bakhuraisa, Yaser A. and Abd. Aziz, Azlan and Tan, Kim Geok and Mahmud, Azwan and Alias, Mohamad Yusoff and Nor, Mohd Khanapiah (2024) Deep Learning Based Vehicle Localization Using Angular Characteristics of Millimeter Wave. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

Text Deep Learning Based Vehicle Localization Using Angular Characteristics of Millimeter Wave.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Accurate vehicle localization is essential for connected and automated Vehicles (CAVs). However, there are significant deficiencies in the performance of Machine Learning-based localization techniques that utilize received signal strength (RSS) from millimeter-wave (mm-Wave) communications, primarily due to instability in dynamic scenarios such as those involving vehicles. In this work, we propose a vehicle localization method based on deep neural networks (DNNs) with mm-Wave propagation characteristics, including RSS and angular characteristics. Specifically, three types of artificial neural networks (ANNs), namely feedforward, function fitting, and cascade-forward, are developed. A commercial ray tracing simulation is utilized to construct a real vehicle-to-infrastructure (V2I) communication scenario and model mm-Wave propagation characteristics. The performance of the proposed method is evaluated by testing the accuracy of the proposed ANN using different datasets. Moreover, the accuracy of the proposed method is tested by using a corrupted dataset to study its robustness against characteristics uncertainties. The numerical results show that the feed-forward model outperforms the other NN models. The constructed empirical cumulative distribution function (CDF) demonstrates that 90% of vehicles have a localization error of below 2.5m. However, the results of the corrupted dataset indicate that the accuracy degraded significantly. This aspect will be further investigated and addressed in future work.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	vehicle localization, V2I, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Feb 2025 00:24
Last Modified:	07 Feb 2025 00:24
URII:	http://shdl.mmu.edu.my/id/eprint/13380

Downloads

Edit (login required)