.png){kind=icon}
返回
Machine learning-based human-robot interaction in ITS
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2021.102750.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the last few years, intelligent transport systems (ITS) have drawn growing attention, and these applications would have a clear and more comfortable experience for transportation. ITS provides applications with a chance to address the future condition on the route beforehand. The major issues in ITS to accomplish a precise and effective traffic flow prediction system are essential. Therefore, in this paper, a machine learning-assisted intelligent traffic monitoring system (MLITMS) has proposed improving transportation protection and reliability to tackle several challenges. The suggested ML-ITMS uses mathematical models to improve the accuracy estimation of traffic flow and nonparametric processes. The Machine Learning-based (ML) method is one of the best-known methods of nonparametric. It requires less prior information about connections between various traffic patterns, minor estimation limitations, and better suitability of nonlinear traffic data features. Human-Robot Interaction (HRI) helps resolve crucial issues concurrently on both the customers and service supplier levels at both ends of the transport system. Thus the experimental results show the proposed ML-ITMS to enhance traffic monitoring to 98.6% and better traffic flow prediction systems than other existing methods.
Keyword:
Human-computer interaction
Machine learning
Intelligent transportation system
Intelligent traffic monitoring system
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
Multi-sensor fusion for body sensor network in medical human-robot interaction scenario医疗人机交互场景下人体传感器网络的多传感器融合.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Intrasession Reliability of the Tests to Determine Lateral Asymmetry and Performance in Volleyball PlayersSymmetry
OAAI
SymmetryIF0
OAAI分享收藏
查看更多内容