Data-Driven Shared Steering Control of Semi-Autonomous Vehicles

Mengzhe Huang, Weinan Gao, Yebin Wang, Zhong-Ping Jiang

Research output: Contribution to journalArticle

Abstract

This paper presents a cooperative/shared framework of the driver and his/her semi-autonomous vehicle in order to achieve desired steering performance. In particular, a copilot controller and the driver together operate and control the vehicle. Exploiting the classical small-gain theory, our proposed shared steering controller is developed independent of the unmeasurable internal states of the human driver, and only relies on his/her steering torque. Furthermore, by adopting data-driven adaptive dynamic programming and an iterative learning scheme, the shared steering controller is studied from the measurable data of the driver and the vehicle. Meanwhile, the accurate knowledge of the driver and the vehicle dynamics is unnecessary, which settles the problem of their potential parametric variations/uncertainties in practice. The effectiveness of the proposed method is validated by rigorous analysis and demonstrated by numerical simulations.

Original languageEnglish (US)
JournalIEEE Transactions on Human-Machine Systems
DOIs
StateAccepted/In press - Jan 1 2019

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Keywords

  • Adaptation models
  • Adaptive dynamic programming (ADP)
  • human in the loop
  • Mathematical model
  • Roads
  • shared driving
  • small gain
  • steering control
  • Torque
  • Vehicle dynamics
  • Vehicles
  • Visualization

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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