Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

Asif Mehmood, Afaq Muhammad, Faisal Mehmood () and Wang-Cheol Song ()
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Asif Mehmood: Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam-si 13120, Republic of Korea
Afaq Muhammad: Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems, College of Computing and Mathematics, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
Faisal Mehmood: Department of AI and Software, College of IT Convergence, Gachon University, Seongnam-si 13120, Republic of Korea
Wang-Cheol Song: Department of Computer Engineering, Jeju National University, Jeju-si 63243, Republic of Korea

Mathematics, 2024, vol. 12, issue 24, 1-20

Abstract: In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

Keywords: multi-access edge computing; location prediction; road-aware rectification; real-time geo-location data; internet of vehicles (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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