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DCFS-based deep learning supervisory control for modeling lane keeping of expert drivers

Author

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  • Chen, Jin
  • Sun, Dihua
  • Zhao, Min
  • Li, Yang
  • Liu, Zhongcheng

Abstract

In this paper, a novel driver model for lane keeping is proposed to replicate the steering behavior of expert drivers. Specifically, a feedforward-feedback control scheme mocking expert drivers is adopted: the feedforward controller plays a leading role, which is a data-driven model based on deep convolutional fuzzy systems (DCFS), and for the sake of human-simulation and guaranteed stability, a supervisory feedback controller is designed, which works only if the state hits the set boundary. Comparing with the previous driver models, the key novelty of the paper is to introduce the “motor intermittency” of human behavior into driver modeling, which is an important issue for biological modeling of the drivers. Simulations on the joint platform of PreScan and CarSim show that the newly presented driver model has better matching performance to the expert drivers comparing with the two different types of advanced model predictive control (MPC) controllers. The proposed driver model has the potential application for semi-automated vehicles to provide human-like qualities for automated driving, which may be one of the essential points to promote the comfort when the driver hands over the steering authority, and improve the transition smoothness in the scenario of human vehicle co-piloting.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120310189
    DOI: 10.1016/j.physa.2020.125720
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    References listed on IDEAS

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    1. Zhang, Jing & Xu, Keyu & Li, Shubin & Wang, Tao, 2020. "A new two-lane lattice hydrodynamic model with the introduction of driver’s predictive effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. 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).
    3. Zhu, H.B. & Zhou, Y.J. & Wu, W.J., 2020. "Modeling traffic flow mixed with automated vehicles considering drivers ’ character difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s desire for smooth driving on the curved road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    5. Li, Lixiang & Cheng, Rongjun & Ge, Hongxia, 2021. "New feedback control for a novel two-dimensional lattice hydrodynamic model considering driver’s memory effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
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