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采用：面向互联和自动驾驶车辆的基于交通知识的导航系统。

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.

机构信息

Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte 574110, India.

Department of Computer Science, Computing and Informatics Research Centre, and Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK.

出版信息

Sensors (Basel). 2023 Jan 6;23(2):653. doi: 10.3390/s23020653.

DOI:10.3390/s23020653
PMID:36679448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9861979/
Abstract

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

摘要

联网和自动驾驶汽车（CAV）受到了业界和学术界的广泛关注，它们致力于研究和开发这项技术，以实现其在道路上的实际应用。最先进的 CAV 利用现有的导航系统来实现移动性和行驶路径规划。然而，可靠的导航系统连接并不能得到保证，特别是在城市道路交通环境中，这里有高楼大厦、附近的道路和多层立交桥。有鉴于此，本文提出了 TAKEN——基于交通知识的导航，以支持城市道路交通环境中的 CAV。本文提出了一种交通分析模型，用于挖掘面向传感器的交通数据，为车辆生成精确的导航路径。还开发了一种知识共享方法，用于从道路上的车辆收集和生成新的交通知识。CAV 的导航使用交通知识和分析所提供的信息来执行。实验性能评估结果证明了 TAKEN 在城市交通环境中对 CAV 精确导航的好处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/7cafd2000f26/sensors-23-00653-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/703a6cf6f5e9/sensors-23-00653-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/d5a26bab1f0c/sensors-23-00653-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/e30e2d890969/sensors-23-00653-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/54fbfbbb4aed/sensors-23-00653-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/7cfc83a124e6/sensors-23-00653-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/c1b7aad3517d/sensors-23-00653-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/7cafd2000f26/sensors-23-00653-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/2057089be356/sensors-23-00653-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/362c0d8afbdf/sensors-23-00653-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/81bb88991b10/sensors-23-00653-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/e3692c22e14c/sensors-23-00653-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/4c36d33bf0de/sensors-23-00653-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/703a6cf6f5e9/sensors-23-00653-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/d5a26bab1f0c/sensors-23-00653-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/e30e2d890969/sensors-23-00653-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/54fbfbbb4aed/sensors-23-00653-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/7cfc83a124e6/sensors-23-00653-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/c1b7aad3517d/sensors-23-00653-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fa1/9861979/7cafd2000f26/sensors-23-00653-g012.jpg

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