Welcome to Journal of Automotive Safety and Energy,

Journal of Automotive Safety and Energy ›› 2025, Vol. 16 ›› Issue (2): 315-325.DOI: 10.3969/j.issn.1674-8484.2025.02.015

• Intelligent Driving and Intelligent Transportation • Previous Articles     Next Articles

Research on vehicle integrated navigation system based on improved sample convolutional interaction network

KUANG Xinghong(), YAN Biyun()   

  1. School of Engineering, Shanghai Ocean University, Shanghai 201306, China
  • Received:2024-07-20 Revised:2024-11-09 Online:2025-04-30 Published:2025-04-22

Abstract:

Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated navigation system in vehicles is prone to signal loss in obstructed environments, leading to divergent positioning results and compromising the efficiency and safety of unmanned vehicles. To address this issue, this study proposed an artificial intelligence solution based on an improved Sample Convolution and Interaction Network (SCINet), which incorporated strategies such as principal component analysis, trend decomposition, and linear convolutional interactive learning on a low-layer SCINet architecture, enhancing the stability and accuracy of the model under such operating conditions. The results show that the proposed model reduces positioning errors by 80.9% and 67.6% compared to Long Short-Term Memory (LSTM) and SCINet, respectively, effectively improving the outdoor positioning accuracy of unmanned vehicles during GNSS signal loss and ensuring the reliability and safety of unmanned vehicle positioning.

Key words: unmanned vehicles, integrated navigation, inertial navigation system (INS) outage, sample convolutional interaction network (SCINet), trend seasonal separating

CLC Number: