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针对单一可变时距模型(VTH)难以在长距离范围内同时满足定速巡航、变速跟车等问题，提出一种基于改进粒子群优化算法的连续综合可变时距模型(CSVTH)。首先，设计一种融合3种单一VTH模型优点的CSVTH来计算跟车的安全时距，通过过渡函数在不同VTH模型之间的连续转换实现多场景的VTH计算，并获取合理的跟车距离。其次，通过自适应惯性权重和学习权重对粒子群算法进行改进，并对CSVTH模型的斜率参数进行了优化。最后，构建了Simulink-Carsim跟车仿真环境。仿真结果表明，和非优化的CSVTH模型相比，经过优化的CSVTH模型在3种跟车场景中将油耗降低了4.4%,舒适度提升了6.4%,跟车误差降低了5.3%。
Abstract：To address the challenges of the single variable time headway(VTH) model in simultaneously meeting the requirements of constant-speed cruising and variable-speed car-following over long-distance, this study proposes a continuous and synthesized variable time headway(CSVTH) model based on an improved particle swarm optimization(PSO) algorithm. Firstlg, a CSVTH model is designed to integrate the advantages of three single VTH models for calculating safe time headway for car-following. By employing transition functions to enable smooth switching between different VTH models, the proposed approach achieves multiscenario VTH computation and derives reasonable following distances. Second, the particle swarm optimization algorithm is enhanced through adaptive inertia weight and learning weight adjustments, which are then applied to optimize the slope parameters of the CSVTH model. Finally, a Simulink-Carsim co-simulation environment is constructed for car-following scenarios. Simulation results demonstrate that, compared to the non-optimized CSVTH model, the optimized CSVTH model reduces fuel consumption by 4.4%, improves ride comfort by 6.4%, and decreases following error by 5.3% across three car-following scenarios.
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基本信息:
DOI：10.15926/j.cnki.issn1672-6871.2025.04.006
中图分类号:U463.6
引用信息:
[1]付主木,陈军,陶发展,等.基于连续综合可变时距模型的跟车优化控制[J].河南科技大学学报(自然科学版),2025,46(04):43-52+104+120.DOI:10.15926/j.cnki.issn1672-6871.2025.04.006.
基金信息:
国家自然科学基金项目(62371182,62301212)
2025-08-05
2025-08-05