Abstract

Connected Cruise Control (CCC) aims to improve throughput in dense commuter traffic on motorways by advising drivers on speed, headway and lane-use via a nomadic human-machine interface. The advice is generated from a prediction of future traffic flow, based on actual traffic-loop data and is transmitted to the in-car platform via 3.5G communication. The systems advantage compared to Cooperative Adaptive Cruise Control (CACC) lies in its potential for rapid implementation and its additional support of lateral driver behaviour. It is important to note that CCC will not have direct control over the car movement. Its beneficial effect is dependent on the driver‘s willingness to comply with the advice. In turn, willingness to comply, in the long term, is also dependent on the perceived benefit of using the system. It is assumed, that to increase both the driver‘s initial willingness to comply and the perceived benefit of using the system, CCC has to reduce the frequency of situations that are seen as most unpleasant by drivers during peak hour traffic. Therefore it is important to understand what these situations are. This study determines the level of frustration caused by driver behaviour in dense motorway traffic, at a lane drop and at an on-ramp/off-ramp. The results help to identify use cases scenarios for driving simulator experiments to ensure that CCC can have a beneficial effect in these situations.

Abstract

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO"inf"2"/inf" emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. © 2015.

Abstract

Motivation/Research approach - An exploratory study was conducted to investigate whether eye movement metrics discriminate between different air traffic control tasks. Findings/Design - The results show the three tasks elicit different eye movement, as Yarbus (1967) also showed in static pictures, and that a number of eye tracking metrics demonstrate the differences. Research limitations/Implications - The effect was demonstrated using only one participant. The results can be used to further study various eye movement metrics. Originality/Value - The research demonstrates that different calculus distinguishes between tasks allowing targeting specific support given the type of task. Take away message - A combination of eye tracker metrics discriminates between tasks helping to provide flexible task support. © 2011 Authors.

Abstract

simulator study has been conducted demonstrating a positive effect on airsickness by utilizing a 3D artificial Earth-fixed visual pattern. Participants were exposed to the same turbulent physical aircraft motion in a simulator three times in a row, each time using a different visual cue. In one condition only the interior of the simulator cabin was visible. In another condition an Earth-fixed star field moving opposite the simulator cabin was projected in front of the participant. In a third condition the same star field was used, however with additional anticipatory information by means of a rollercoaster like track showing the trajectory to go. Participants were asked for their misery and joyfulness ratings at fixed time instants using an 11-points misery scale (no problems-vomiting) and joyfulness scale (unpleasant-pleasant). The results showed that viewing an Earth-fixed visual frame moving instantaneously opposite the cabin motion did reduce motion sickness significantly by a factor of 1.6, thereby improving comfort. This condition could be applied in air transport, where often a monitor is available in the back of the seat ahead. The largest effect, i.e., a reduction by a factor of 4.2 was realized by adding anticipatory information. Although it is not possible to predict the effect of turbulence on the aircraft motion yet, an anticipatory display might already be applicable in other domains, such as at sea by using a wave radar and a ship motion model. © 2011 Elsevier B.V. All rights reserved.

Abstract

Future air traffic is expected to grow increasingly, opening up a gap for task dependent automation and adaptive interfaces, helping the Air Traffic Controller to cope with fluctuating workloads. One of the challenging factors in the application of such intelligent systems concerns the question what the operator is doing in order to optimize support and minimize automation surprises. This study questions whether eye metrics are able to determine what task the operator is engages in. We therefore examined A) if the eye-path would differ for three different ATC tasks and B) whether this effect can be quantified with six eye-metrics. In an experiment, the six eye-metrics were calculated and used as a dependent variable. The results show that some tasks can be inferred by eye movement metrics and other metrics infer workload, although none inferred by both task and workload. Copyright 2014 ACM.