Literature DB >> 32966229

Communication and Interaction With Semiautonomous Ground Vehicles by Force Control Steering.

Miguel Martinez-Garcia, Roy S Kalawsky, Timothy Gordon, Tim Smith, Qinggang Meng, Frank Flemisch.   

Abstract

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving-involving transitions of control between the human and the machine-are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the H -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles.

Entities:  

Year:  2021        PMID: 32966229     DOI: 10.1109/TCYB.2020.3020217

Source DB:  PubMed          Journal:  IEEE Trans Cybern        ISSN: 2168-2267            Impact factor:   11.448


  3 in total

1.  An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.

Authors:  Olivia Vargas-Lopez; Carlos A Perez-Ramirez; Martin Valtierra-Rodriguez; Jesus J Yanez-Borjas; Juan P Amezquita-Sanchez
Journal:  Sensors (Basel)       Date:  2021-05-01       Impact factor: 3.576

2.  Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution.

Authors:  Mooseop Kim; YunKyung Park; KyeongDeok Moon; Chi Yoon Jeong
Journal:  Int J Environ Res Public Health       Date:  2021-06-08       Impact factor: 3.390

3.  Predicting the Coping Skills of Older Drivers in the Face of Unexpected Situation.

Authors:  Yusuke Kajiwara; Haruhiko Kimura
Journal:  Sensors (Basel)       Date:  2021-03-17       Impact factor: 3.576

  3 in total

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