This paper assessed four types of human–machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286–297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19–36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition—low), sphere (information acquisition—high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers’ initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.@en

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.

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This paper presents formative research to inform the design of intelligent automotive user interfaces. It describes an evaluation of dialogue-based interfaces, mediating the driver to take back control from the autonomous mode of a car. Four concepts designed to increase driver Situation Awareness were evaluated in a driving simulator. They used dialoguebased interaction, where driving-related information was either asked from or repeated by the driver, with the alternative of a countdown-based interface with no additional information. An inclusive set of participants, with a wide age spectrum, tested the interfaces. The shorter and simpler interaction of the countdown timer was most accepted. The interface seeking answers to driving-related questions came next, and the interface requiring repetition of driving-related information, even when augmented by visual and tactile cues, was least accepted. Design guidelines on utilizing dialogue as a means of keeping the driver in the loop during a takeover were thus derived.

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Across the automotive industry, manufacturers have recently released various Partial Automation systems (SAE Level 2) which allow simultaneous/combined execution of both lateral and longitudinal vehicle control at the same time, yet still require active human supervision/engagement. Current reactive trends will be reviewed across major automotive players regarding differences in terminology, HMI input/outputs, and escalation intervals. Scholarly research is also reviewed pertaining to proactive strategies for driver engagement. Additionally, human factors research and findings will be presented regarding recommendations for situation awareness, human machine interfaces, TOR, as well as shared control concepts. The tutorial will conclude with discussion and brainstorming around outlook toward tele-operated remote driving services (Tele-Driving); what they have to offer beyond assisted/automated driving, autonomous vehicles, and ride-hailing/car-sharing paradigms; as well as the design/conduct of human factors research regarding Tele-Driving.

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