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 driving-simulator study aimed to motivate cooperative lane-change maneuvers in automated freeway driving under human supervision. Two interaction concepts were designed based on game theory. These concepts supported drivers’ cooperation by applying both rewards and sanctions as the proverbial carrot and stick. The social-status interaction rewards gap creation by revealing a driver's prior cooperative behavior to other road users. The trade-off interaction introduces a system in which points compensate time loss and gain. Both concepts were evaluated from the left- and right-lane perspective, framing 39 participants to “be fast.” Drivers in the right lane asked those in the left lane to open a gap to overtake, mediated through a vehicle-to-vehicle connection and an augmented-reality user interface. Only 67% of the merging requests were accepted by left-lane drivers due to time pressure in the baseline condition. The social-status interaction enhanced acceptance to 86% on average and even to 97% for requests made by drivers marked as cooperative. The trade-off interaction enhanced acceptance to 87% as drivers gained a virtual benefit for losing one second. The subjective evaluation was positive for all conditions, and the social concepts were rated significantly higher on items associated with social relationships. Both social interaction concepts motivate cooperation and shape drivers’ behavior even under time pressure. Social mechanisms power maneuver-based local cooperation between traffic participants. It is expected that involving drivers in cooperative maneuvers has a beneficial effect on traffic performance, which microscopic traffic flow modeling should validate next. Gamified interaction and interface elements involve drivers of automated vehicles into strategic decisions and could help to mitigate automation effects. Since they don't “drive” any more, cooperative interaction concepts now make them “play driving” and formulate pleasing strategies.

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