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Driver readiness model for regulating the transfer from automation to human control

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Author: Mioch, T. · Kroon, L. · Neerincx, M.A.
Type:article
Date:2017
Publisher: Association for Computing Machinery
Source:22nd International Conference on Intelligent User Interfaces, IUI 2017. 13 March 2017 through 16 March 2017 Part F126745, Part F126745, 205-213
Identifier: 756679
doi: doi:10.1145/3025171.3025199
ISBN: 9781450343480
Keywords: Collaborative driving · Driver readiness · Knowledge representation · Ontology · Reusability · Transfer of control · Automobile drivers · Digital storage · Knowledge based systems · Knowledge representation · Ontology · Reusability · Trucks · Collaborative driving · Driver readiness · Literature studies · Ontological modeling · Readiness models · Seating position · Situation awareness · Transfer of controls · User interfaces · Human & Operational Modelling · PCS - Perceptual and Cognitive Systems · ELSS - Earth, Life and Social Sciences

Abstract

In the collaborative driving scenario of truck platooning, the first car is driven by its chauffeur and the next cars follow automatically via a so-called 'virtual tow-bar'. The chauffeurs of the following cars do not drive 'in the towbar mode', but need to be able to take back control in foreseen and unforeseen conditions. It is crucial that this transfer of control only takes place when the chauffeur is ready for it. This paper presents a Driver Readiness (DR) ontological model that specifies the core factors, with their relationships, of a chauffeur's current and near-future readiness for taking back the control of driving. A first model was derived from a literature study and an analysis of truck driving data, which was refined subsequently based on an expert review. This DR model distinguishes (a) current and required states for the physical (hand, feet, head, and seating position) and mental readiness (attention and situation awareness), (b) agents (human and machine actor), (c) policies for agent behaviors, and (d) states of the vehicle and its environment. It provides the knowledge base of a Control Transfer Support (CTS) agent that assesses the current and predicted chauffeur state and guides the transition of control in an adaptive and personalized manner. The DR model will be fed by information from the network and in-car sensors. The behaviors of the CTS agent will be generated and constrained by the instantiated policies, providing an important step towards a safe transfer of control from automation to human driver. © 2017 ACM. ACM SIGAI; ACM SIGCHI