Bakhuraisa, Yaser and Aziz, Azlan bin Abd and Geok, Tan Kim 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 2024.
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Deep Learning Based Vehicle Localization Using Angular Characteristics of Millimeter Wave.pdf 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 feed-forward, 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) |
|---|---|
| Divisions: | Faculty Of Electronics And Computer Technology And Engineering |
| Depositing User: | NURHASHIRAH BORHAN |
| Date Deposited: | 31 Jul 2026 07:42 |
| Last Modified: | 31 Jul 2026 07:42 |
| URI: | http://eprints.utem.edu.my/id/eprint/30126 |
| Statistic Details: | View Download Statistic |
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