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International Journal of Automotive Technology

03.01.2025 | Connected Automated Vehicles and ITS, Vision and Sensors

Human–Machine Cooperative Vehicle Control Based on Driving Intention and Risk Avoidance

verfasst von: Yong Guan, Ning Li, Pengzhan Chen, Yongchao Zhang

Erschienen in: International Journal of Automotive Technology

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Abstract

This study explores human–machine collaborative planning and tracking control methods for autonomous vehicles. The proposed approach is based on an improved Fastformer GAN (FGAN) algorithm and a risk assessment mechanism for driver behavior. Initially, a novel trajectory prediction method that integrates the Fourier Attention Fastformer (FAF) and GAN models is proposed. This method is used for predicting driver behavior and adjusting trajectories based on driver intent, correcting any improper actions by the driver. Subsequently, a risk assessment system, which couples Artificial Potential Field (APF) and Dynamic Potential Field (DPF) models, is introduced to evaluate the risk levels of driver behavior. Adaptive activation of human–machine collaboration is based on the assessed driving risks. Simulation results indicate that the proposed FGAN algorithm significantly improves trajectory prediction accuracy on public datasets. Furthermore, the proposed human–machine collaboration method ensures both vehicle safety and stability while greatly reducing human–machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

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Metadaten
Titel
Human–Machine Cooperative Vehicle Control Based on Driving Intention and Risk Avoidance
verfasst von
Yong Guan
Ning Li
Pengzhan Chen
Yongchao Zhang
Publikationsdatum
03.01.2025
Verlag
The Korean Society of Automotive Engineers
Erschienen in
International Journal of Automotive Technology
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI
https://doi.org/10.1007/s12239-024-00200-w

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