文章基本信息

标题：Lane prediction optimization in VANET
本地全文：下载
作者：Ghassan Samara
期刊名称：Egyptian Informatics Journal
印刷版ISSN：1110-8665
出版年度：2021
卷号：22
期号：4
页码：411-416
DOI：10.1016/j.eij.2020.12.005
语种：English
出版社：Elsevier
摘要：AbstractAmong the current advanced driver assistance systems, Vehicle-to-Vehicle (V2V) technology has great potential to increase Vehicular Ad Hoc Network (VANET) performance in terms of security, energy efficiency, and comfortable driving. In reality, vehicle drivers regularly change lanes depending on their assumptions regarding visual distances. However, many systems are not quite well-designed, because the visible range is limited, making it difficult to achieve such a task. V2V technology offers high potential for VANET to increase safety, energy efficiency, and driver convenience. Drivers can make more intelligent options in terms of lane selection using predicted information of downstream lane traffic, which is essential for obtaining mobility benefits. An assistant lane selection system is proposed in this research, which helps the driver locate an optimal lane-level travel path in order to minimize travel time. The decision-making criteria are based on the predicted lane traffic conditions via V2V technology. This paper aims to create a specific V2V system to support lane selection based on the predicted traffic states to find the best travel lane. In this paper, a Spatial–Temporal (ST) prototype is developed and then applied to predict future traffic conditions for road cells using spatial and temporal information. The suggested lane selection assistance system uses this information to select the optimized lane sequence. Then, an intensive simulation-based assessment is conducted in different scenarios. Results indicate that the proposed system outperforms other published systems.
关键词：VANET;Lane prediction;Optimization;V2V