Fuzzy logic-based speed guidance strategy for mixed platoons at intersections with communication delay compensation and driver reaction time modeling.
DOI: 10.1038/s41598-025-32008-8
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Summary
This paper addresses the challenge of optimizing traffic efficiency and safety at signalized intersections within Cooperative Vehicle-Infrastructure Systems (CVIS), specifically focusing on mixed platoons comprising both Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs). The research is motivated by the inefficiencies caused by heterogeneous vehicle behaviors, communication uncertainties, and the variability of human driver reaction times, which traditional fixed-timing signals and rigid speed guidance strategies fail to adequately manage. The authors propose a comprehensive speed guidance strategy designed to minimize stops, reduce travel time, and enhance platoon coordination in mixed traffic environments. The methodology integrates three key innovations: a four-dimensional speed guidance framework (acceleration, deceleration, constant speed, and stopping), a dynamic communication delay compensation mechanism, and a multi-factor driver reaction time model. The speed guidance strategy dynamically optimizes platoon passage timing by considering signal phasing, leading vehicle dynamics, and target vehicle states. To address V2X communication delays, the authors developed a fuzzy logic-enhanced Smith predictor that dynamically compensates for delays ranging from 0.5 to 2.0 seconds. Furthermore, the study introduces a novel driver reaction time model that accounts for nonlinear influences of driver age, fatigue status, and cognitive load (voice broadcast), moving beyond traditional assumptions of fixed reaction times. The system utilizes standard car-following models, including CACC for CAV-CAV interactions, ACC for CAV-HDV interactions, and an improved Intelligent Driver Model (IDM) for HDV behaviors, all constrained by safety metrics such as minimum safe following distance and Time-to-Collision thresholds. The study validates the proposed strategy through Prescan-MATLAB/Simulink co-simulations with five-vehicle platoons and field experiments with three-vehicle platoons. Quantitative results demonstrate significant performance improvements: the system achieved a maximum acceleration error reduction of 80.9% and a speed error reduction of 62% under communication delays of 0.5–2.0 seconds. Additionally, the incorporation of the multi-factor driver reaction time model improved trajectory prediction accuracy and increased intersection throughput by approximately 36% compared to fixed-parameter approaches. The experiments confirmed the practical effectiveness and safety of the strategy, showing that it enables mixed platoons to traverse intersections more smoothly and efficiently. The significance of this work lies in its robust approach to handling the complexities of mixed autonomous-human traffic. By explicitly modeling driver heterogeneity and compensating for communication delays using fuzzy logic, the proposed strategy enhances the adaptability and reliability of speed advisory systems. The findings suggest that integrating these factors significantly improves overall intersection efficiency, reduces energy consumption and emissions associated with stop-and-go traffic, and supports the development of more intelligent and sustainable urban transportation systems. This research provides a scalable framework for future CVIS implementations where communication imperfections and human variability are inevitable.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | PubMed Central | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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