Fuzzy logic-based speed guidance strategy for mixed platoons at intersections with communication delay compensation and driver reaction time modeling

This paper proposes a comprehensive speed guidance strategy for mixed vehicle platoons at signalized intersections. The strategy integrates communication delay compensation and models driver reaction time. This research addresses critical challenges in Cooperative Vehicle-Infrastructure Systems (CVIS), where heterogeneous vehicle behaviors and communication uncertainties significantly impact traffic efficiency. The key innovations include: (1) A four-dimensional speed guidance framework (acceleration, deceleration, constant speed, stopping) for mixed CAV-HV platoons; (2) A fuzzy logic-enhanced Smith predictor for dynamic V2X delay compensation; (3) A multi-factor driver reaction time model incorporating age, fatigue, and cognitive load effects. Quantitative results demonstrate that the system achieves maximum acceleration error reduction of 80.9% and speed error reduction of 62% under communication delays of 0.5-2.0 s. The driver reaction time model improves trajectory prediction accuracy, increases intersection throughput by approximately 36% compared to fixed-parameter approaches. Prescan - MATLAB/Simulink co - simulation with 5 - vehicle platoons and field experiments with 3 - vehicle platoons confirm the practical effectiveness and safety of the proposed strategy. The integrated approach significantly enhances platoon coordination, reduces travel time, and improves overall intersection efficiency in mixed autonomous-human traffic environments.

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Publication Details

Journal
Scientific Reports
Published
2025-12-23
DOI
https://doi.org/10.1038/s41598-025-32008-8
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00

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article

Fuzzy logic-based speed guidance strategy for mixed platoons at intersections with communication delay compensation and driver reaction time modeling

X. Y. Liu, Menghua Yan, Runjia Dai, Mingxing Gao
Scientific Reports
Traffic control and management
article

Fuzzy logic-based speed guidance strategy for mixed platoons at intersections with communication delay compensation and driver reaction time modeling

X. Y. Liu, Menghua Yan, Runjia Dai, Mingxing Gao
article en

Abstract

This paper proposes a comprehensive speed guidance strategy for mixed vehicle platoons at signalized intersections. The strategy integrates communication delay compensation and models driver reaction time. This research addresses critical challenges in Cooperative Vehicle-Infrastructure Systems (CVIS), where heterogeneous vehicle behaviors and communication uncertainties significantly impact traffic efficiency. The key innovations include: (1) A four-dimensional speed guidance framework (acceleration, deceleration, constant speed, stopping) for mixed CAV-HV platoons; (2) A fuzzy logic-enhanced Smith predictor for dynamic V2X delay compensation; (3) A multi-factor driver reaction time model incorporating age, fatigue, and cognitive load effects. Quantitative results demonstrate that the system achieves maximum acceleration error reduction of 80.9% and speed error reduction of 62% under communication delays of 0.5-2.0 s. The driver reaction time model improves trajectory prediction accuracy, increases intersection throughput by approximately 36% compared to fixed-parameter approaches. Prescan - MATLAB/Simulink co - simulation with 5 - vehicle platoons and field experiments with 3 - vehicle platoons confirm the practical effectiveness and safety of the proposed strategy. The integrated approach significantly enhances platoon coordination, reduces travel time, and improves overall intersection efficiency in mixed autonomous-human traffic environments.

Scientific ReportsVol. 16(1)
Inner Mongolia Agricultural University (CN), Tianjin University of Technology and Education (CN), State Key Laboratory of Vehicle NVH and Safety Technology (CN)
Tianjin University of Technology, Tianjin University
Industry, innovation and infrastructure
Openalex Percentile: Top 38%
Traffic control and management
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