Robust Autonomous Driving with Human in the Loop

Mengzhe Huang, Zhong Ping Jiang, Michael Malisoff, Leilei Cui

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter presents a reinforcement-learning-based shared control design for semi-autonomous vehicles with a human in the loop. The co-pilot controller and the driver operate together and control the vehicle simultaneously. To address the effects of human driver reaction time, the interconnected human–vehicle system is described by differential-difference equations. Exploiting the real-time measured data, the adaptive optimal shared controller is learned via adaptive dynamic programming, without accurate knowledge of the driver and vehicle models. Adaptivity, near optimality and stability are ensured simultaneously when the data-driven shared steering controller is applied to the human-in-the-loop vehicle system, which can handle the potential parametric variations and uncertainties in the human–vehicle system. The efficacy of the proposed control strategy is validated by proofs and demonstrated by numerical simulations.

Original languageEnglish (US)
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages20
StatePublished - 2021

Publication series

NameStudies in Systems, Decision and Control
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)


Dive into the research topics of 'Robust Autonomous Driving with Human in the Loop'. Together they form a unique fingerprint.

Cite this