Citation

Ye S, Liu S, Wang F. Comput. Intell. Neurosci. 2022; 2022: e3924013.

Copyright

(Copyright © 2022, Hindawi Publishing)

DOI

10.1155/2022/3924013

PMID

35795752

PMCID

PMC9252665

Abstract

Vehicular ad hoc network (VANET) is a key part of intelligent transportation system. VANET technology is very important for realizing vehicle-to-vehicle communication, remote control of unmanned vehicles, and early warning reception of road condition information ahead of time when external networks such as the Internet are limited. Aiming at the problems of uncertainty in vehicle mobility, uneven distribution of traffic density in road sections, and uncertainty in the road scene where the vehicle is located in VANET, a multiscenario intelligent QoS routing algorithm (MISR) for vehicle network is proposed. The algorithm analyzes a variety of vehicle network scenarios and discusses the routing methods used in scenarios with/without roadside auxiliary units and vehicle uniform acceleration limited/unrestricted, so that the vehicle network can ensure that the communication link is not interrupted as much as possible. At the same time, QoS performance criteria such as data transmission rate, bit error rate, and delay time are considered. For complex scenes with variable vehicle speeds, this paper introduces a deep reinforcement learning method to intelligently select routing nodes for vehicle networks.

Language: en