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Stability analysis of an improved car-following model accounting for the driver’s characteristics and automation

Author

Listed:
  • Zhao, Hongzhuan
  • Chen, Qiguang
  • Shi, Wei
  • Gu, Tianlong
  • Li, Wenyong

Abstract

This study presents an improved car-following model accounting for the driver’s characteristics and automation for longitudinal driving. We attempt to reveal some features of driving behavior, which drivers make decision in consideration with delayed decision and aggressiveness as well as the automated controller. The delayed decision and aggressiveness of drivers are represented via the integral term with kernel function and headway perturbation, and the automated controller consists of proportional–derivative element. Stability analysis is performed both driver’s characteristics and controller gains adopting frequency domain sweeping method. Stability regions of proposed model show the relationships between driver’s characteristics and automated controller. Some numerical examples are given to illustrate the effectiveness of the proposed methods and interpret the relation between driver’s characteristics and automated controller.

Suggested Citation

  • Zhao, Hongzhuan & Chen, Qiguang & Shi, Wei & Gu, Tianlong & Li, Wenyong, 2019. "Stability analysis of an improved car-following model accounting for the driver’s characteristics and automation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119305989
    DOI: 10.1016/j.physa.2019.04.226
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    Citations

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    Cited by:

    1. Madaan, Nikita & Sharma, Sapna, 2022. "Delayed-feedback control in multi-lane traffic system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    2. Zhai, Cong & Li, Kening & Zhang, Ronghui & Peng, Tao & Zong, Changfu, 2024. "Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Chen, Jin & Sun, Dihua & Zhao, Min & Li, Yang & Liu, Zhongcheng, 2021. "DCFS-based deep learning supervisory control for modeling lane keeping of expert drivers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).

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