Driving state evaluation of intelligent connected vehicles based on centralized multi-source vehicle road collaborative information fusion

Qiuhong Tong (), Zhuolin Yang, Guoqong Chai, Yijie Wang, Zihao Qi, Feng Wang and Kangchao Yin
Additional contact information
Qiuhong Tong: Chang’an University
Zhuolin Yang: Chang’an University
Guoqong Chai: Chang’an University
Yijie Wang: Chang’an University
Zihao Qi: Chang’an University
Feng Wang: Chang’an University
Kangchao Yin: Chang’an University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 10, No 34, 24107-24126

Abstract: Abstract When the intelligent vehicle is driving on the road, the driving state of the vehicle needs to be monitored and evaluated in real time, such as whether the distance from the vehicle or obstacle in front of it is often less than or close to the safe driving distance, whether it often deviates from the lane at high speed, whether the temperature of the power battery is often higher or lower, etc. The intelligent vehicle has rich environment perception systems and communication systems. A large amount of effective driving state information is obtained through these systems, then the information is fused by intelligent algorithm, which is very useful to evaluate the driving state. By using the developed Intelligent Connected Vehicle (ICV) information collection system platform, including vision, radar, environmental perception, positioning, and V2X communication system for obtaining roadside information, etc. various driving state parameters of the vehicle during the current driving process of the intelligent vehicle are obtained in real time when the vehicle is driving, then transmitted to the monitoring platform, and the intelligent immune algorithm is applied to establish model on monitoring platform. In the model, the state space representation of related problems is designed. The user establishes the autologous library according to the typical vehicle driving state in the monitoring road driving characteristics, which is used as the known antigen. The parameters of the real-time driving state of the vehicle are used as the unknown antigen, and the immune algorithm model is applied to get the evaluation of the current vehicle driving state and feedback the evaluation results to the vehicle and give early warning. In this paper, the model is verified by experiments, and the data are analyzed. In the experiment, the evaluation results of the monitoring platform are consistent with the state of the driving vehicle, which shows the feasibility of this model for the evaluation of the driving state of the ICV.

Keywords: Driving state evaluation; Intelligent connected vehicles; Collaborative information fusion (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-021-01957-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:10:d:10.1007_s10668-021-01957-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-021-01957-1

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().