Using gamification to motivate human cooperation in a lane-change scenario

Lütteken, Niklas; Zimmermann, M. B.; Bengler, Klaus

doi:10.1109/itsc.2016.7795662

Cited by 20 publications

(17 citation statements)

References 23 publications

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“…This level has parallels with Gamification. It includes factors to incentivise behavioural change by allowing performance comparison with other users, facilitating cooperation and/or competition, creating transparency in behaviour-result relationships of other users and even applying forms of normative social pressure (see for example Lütteken et al, 2016). Social factors vary in importance and effects on different age groups (McEachan, Conner, Taylor, & Lawton, 2011), which is important for instance when targeting specific demographic groups.…”

Section: Integrating Persuasive Methodsmentioning

confidence: 99%

“…These provide persuasive elements that can be used depending on the type of system and the context in which it is intended to be applied. For example, in a cooperative system, which is social by nature, the 'social support level' provides ways to add persuasive elements to the social aspects present in the system (see Lütteken et al, 2016). More generally: system credibility can assist persuasion in most systems by increasing trust in the validity of the messages over time, which has been shown to be a large factor in whether a driver responds to the advice or not (Abe & Richardson, 2006;Risto & Martens, 2013), or even a factor in determining system usage over time (Martens & Jenssen, 2012).…”

Section: Integrating Persuasive Methodsmentioning

confidence: 99%

“…We apply the model to the design of our lane-specific advice system, as described in Section 6 of this paper. The developed model can be applied in a broader sense, for example to cooperative driving systems that require drivers to behave in a certain way (Lütteken, Zimmermann, & Bengler, 2016;Risto, 2014), eco-driving systems (Ecker et al, 2011;Magana & Organero, 2011), or systems stimulating safer driving styles (Rodríguez et al, 2014;Steinberger et al, 2016).…”

Section: Context Of the Developed Frameworkmentioning

confidence: 99%

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A conceptual model for persuasive in-vehicle technology to influence tactical level driver behaviour

Gent

Farah

Nes

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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Persuasive in-vehicle systems aim to intuitively influence the attitudes and/or behaviour of a driver (i.e. without forcing them). However, the challenge in using these systems in a driving setting, is to maximise the persuasive effect without infringing upon the driver's safety. This paper proposes a conceptual model for driver persuasion at the tactical level (i.e., driver manoeuvring level, such as lane-changing and car-following). The main focus of the conceptual model is to describe how to safely persuade a driver to change his or her behaviour, and how persuasive systems may affect driver behaviour. First, existing conceptual and theoretical models that describe behaviour are discussed, along with their applicability to the driving task. Next, we investigate the persuasive methods used with a focus on the traffic domain. Based on this we develop a conceptual model that incorporates behavioural theories and persuasive methods, and which describes how effective and safe driver persuasion functions. Finally, we apply the model to a case study of a lane-specific advice system that aims to reduce travel time delay and traffic congestion by advising some drivers to change lanes in order to achieve a better distribution of traffic over the motorway lanes.

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Section: Integrating Persuasive Methodsmentioning

confidence: 99%

Section: Integrating Persuasive Methodsmentioning

confidence: 99%

Section: Context Of the Developed Frameworkmentioning

confidence: 99%

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A conceptual model for persuasive in-vehicle technology to influence tactical level driver behaviour

Gent

Farah

Nes

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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“…3). Simultaneously, an augmented reality display (based on [39], [62], and [63]) showed warnings, information, or decision suggestions for courses of action (see Fig. 4).…”

Section: Hmis For Torsmentioning

confidence: 99%

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

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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 acquisitionlow), 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.

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“…An example application illustrates the utility of such interventions. As such, and alongside researchers like Lütteken et al (2016) who proposed using gamification to motivate human cooperation in a highly automated environment, I look into the future of gamified driving.…”

Section: Preamblementioning

confidence: 99%

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

Steinberger¹

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Using gamification to motivate human cooperation in a lane-change scenario

Cited by 20 publications

References 23 publications

A conceptual model for persuasive in-vehicle technology to influence tactical level driver behaviour

A conceptual model for persuasive in-vehicle technology to influence tactical level driver behaviour

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

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