Editorial: Model-based methods for human–machine cooperative and shared control systems

Abstract

Human–machine cooperation and shared control have become major areas of interest in various scientific communities. Humans and machines working together as a team yield a high potential which can be leveraged in numerous application domains. The articles in this Research Topic review state-of-the-art methods and present recent approaches for the modeling and design of human–machine cooperative systems, showcasing the latest research for a variety of applications. In addition, the articles show different ways in which the interaction between humans and machines can take place. The huge variety of interaction scenarios as well as the “type” of interaction lead to a similarly large spectrum of models and methods for designing machines that are capable of completing tasks together with a human. One of the most prominent application domains of cooperative and shared control systems is highly automated driving. This is reflected in two articles reporting on research in this application field. The article titled “A review of shared control in automated vehicles: System evaluation” by Sarabia et al. acknowledges the pivotal role of human–machine cooperative driving in future transportation and provides a survey of recent literature focusing on studies with real drivers. The authors emphasize the importance of turning the attention to the evaluation methodology of shared control approaches. The article reviews the recent literature, focusing both on the study design and on the various metrics used in the studies. The authors provide valuable conclusions. For example, research on shared longitudinal control should catch up with research on shared lateral control. Control authority shifting based on driver state should keep receiving increasing attention. Lastly, the evaluation of the interaction itself and of safety have not been performed as intensively as the evaluation of driving performance. The work of Oudainia et al. aims at minimizing conflicts between drivers and lane keeping systems of intelligent vehicles. In their article titled “Online driver model parameter identification using the Lyapunov approach based shared control,” the authors present a driving assistance system for lane keeping, which is based on a dynamic driver model. To robustly identify the driver model parameters for different drivers and situations, a OPEN ACCESS