Research Article
Multi-objective Tracking Algorithm for Intelligent Networked Vehicles in Hybrid Traffic
@INPROCEEDINGS{10.4108/eai.17-11-2023.2342810, author={Ning Zhou and Jianman Jiang}, title={Multi-objective Tracking Algorithm for Intelligent Networked Vehicles in Hybrid Traffic}, proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India}, publisher={EAI}, proceedings_a={ICSETPSD}, year={2024}, month={1}, keywords={hybrid traffic intelligent networked vehicles multi-target tracking tracking algorithm}, doi={10.4108/eai.17-11-2023.2342810} }- Ning Zhou
Jianman Jiang
Year: 2024
Multi-objective Tracking Algorithm for Intelligent Networked Vehicles in Hybrid Traffic
ICSETPSD
EAI
DOI: 10.4108/eai.17-11-2023.2342810
Abstract
The increase in the number of automobiles has expanded the scope of public travel, bringing great convenience to people's lives and bringing many social problems such as traffic accidents, urban road congestion and the gradual increase of foggy weather. As an important part of the intelligent transportation system, intelligent networked vehicles are of great value and significance in solving the negative problems that exist in current hybrid transportation. The objective of this paper is to study the multi-objective tracking algorithm for intelligent networked vehicles based on hybrid traffic. A robust VB-RMTCT multi-target tracking algorithm is proposed. Considering the unknown localization noise statistics and random wild values of the relative positions of Hv and Cv, the mean-field theory is used to model the student t-distribution and non-Gaussian properties for numerical simulation of the localization noise, and the results show that the VB-RMTCT tracking algorithm can effectively and consistently improve the target state estimation performance compared with other traditional research methods.
