MPC-based Haptic Shared Steering System: A Driver Modelling Approach for Symbiotic Driving

A.M. Rios Lazcano, Tenghao Niu, Xabier Carrera Akutain, David Cole, Barys Shyrokau

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this study, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behaviour, neuromuscular dynamics, and the vehicle-steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive costfunction algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy.

Original languageEnglish
JournalIEEE/ASME Transactions on Mechatronics
DOIs
Publication statusAccepted/In press - 4 Mar 2021

Keywords

  • Collaboration
  • Collaborative driving
  • Driver modelling
  • Haptic shared control
  • Human-machine interaction
  • Model predictive control
  • Neuromuscular
  • Task analysis
  • Torque
  • Vehicle dynamics
  • Vehicles
  • Wheels

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