An MEC Architecture-Oriented Improved RRT Algorithm for Regional Trajectory Planning

Qingwen Han, Ziyun Huang, Lingqiu Zeng, Yunqu Wu, Lei Ye, Hui Zu, Yu Lei
<span title="">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/khqygtfojnby3jyh5obuwd7ko4" style="color: black;">Mobile Information Systems</a> </i> &nbsp;
Multi-access Edge Computing (MEC), which could provide real-time computing ability, is considered as an effective approach to improve performance of Vehicular Ad Hoc Network (VANET). MEC could process regional vehicles information and generate real-time road hazard features, which could be used to realize trajectory planning progress of vehicles. In this paper, an MEC-oriented VANET infrastructure is presented, and a road hazard feature-based trajectory planning method is proposed. Back
more &raquo; ... ion (BP) neural network is employed to predict road hazard feature changing, while a hazard-based cost function is defined. Then, an improved Rapidly Exploring Random Tree (RRT) algorithm is proposed for novel regional trajectory planning. A joint simulation is done based on SUMO and NS3 platforms. Simulation results verify the effectiveness and stability of the proposed algorithm.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/6689293">doi:10.1155/2021/6689293</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/26c08580666143a18d6fee5e64c977d8">doaj:26c08580666143a18d6fee5e64c977d8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/finuff3kgffkdmlr7yelomsc5i">fatcat:finuff3kgffkdmlr7yelomsc5i</a> </span>
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