Characterization of Driver Neuromuscular Dynamics for Human-Automation Collaboration Design of Automated Vehicles

• Published in: IEEE/ASME transactions on mechatronics Vol. 23; no. 6; pp. 2558 - 2567
• Main Authors: Lv, Chen, Wang, Huaji, Cao, Dongpu, Zhao, Yifan, Auger, Daniel J., Sullman, Mark, Matthias, Rebecca, Skrypchuk, Lee, Mouzakitis, Alexandros
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automated systems; Automated vehicle; Automatic control; Automation; Autonomous vehicles; Collaboration; driver neuromuscular dynamics; Drivers; driver–vehicle interaction; Dynamic models; experimental characterization; Mathematical model; Neuromuscular; Parameter estimation; Parameter identification; Properties (attributes); Resonant frequencies; Steering; system identification; Task analysis; Transfer functions; Vehicle dynamics; Vehicles
• ISSN: 1083-4435; 1941-014X; 1941-014X
• DOI: 10.1109/TMECH.2018.2812643

In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper, a dynamic model of drivers' neuromuscular interaction with a steering wheel is first established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions, and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.