Comparing drivers’ visual attention at Junctions in Real and Simulated Environments

Robbins, Chloe J.; Allen, Harriet A.; Chapman, Peter

doi:10.1016/j.apergo.2019.05.005

Cited by 26 publications

(15 citation statements)

References 42 publications

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“…For example, a study in [44] reports differences in road fixations, and [45] showed greater gaze dispersion in the simulator than on-road. In a recent experiment by Robbins et al [46], mean fixation durations recorded in a high-fidelity driving simulation were similar to an on-road experiment but only for mediumto high-demand situations (such as turning at intersections).…”

Section: ) Effect Of Recording Conditionsmentioning

confidence: 69%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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Section: ) Effect Of Recording Conditionsmentioning

confidence: 69%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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“…Furthermore, while head movements when driving in real and simulated situations are similar, there are differences in gaze behaviour, with longer fixations in the simulator than when driving on-road. 62 Overall, this shows that data from a range of experiments are needed.…”

Section: Discussionmentioning

confidence: 99%

The effect of distraction, response mode and age on peripheral target detection to inform studies of lighting for driving

Fotios

Robbins

Fox

et al. 2020

Lighting Research & Technology

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It is expected that the detection of peripheral objects, a key visual task for safe driving, is affected by cognitive distraction, by observer age and by the manner in which action is undertaken following detection. An experiment was conducted to measure these effects, using a fixation cross and peripheral target discs displayed on a screen. The experiment was repeated with young (18–25 years) and old (60+years) age groups, with six distraction tasks, and with simple and choice response modes. The older group was found to respond more slowly than the younger group and detected fewer targets. The results suggest that distraction impairs detection, with the degree of impairment depending on the difficulty of the distraction task. Participants were generally slower at responding with choice response but this did not lead to a greater number of missed targets. Where lighting standards are informed by the ability to detect peripheral hazards, the research should represent older people, choice responses and impaired detection due to distraction.

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“…These simulations range from high-fidelity real environment substitutes to simple laboratory environments, allowing exploration of task performance with greater experimental control. Robbins et al (2019) also consider how simulated environments can be repeated in a trial by trial format, as an efficient way of measuring behaviour. Furthermore, Patton (2014) refers to presence within immersive simulated environments, in terms of how an individual responds to the environment and the extent to which they feel part of that environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Using the Perceptual Experience Laboratory (PEL) to simulate tourism environments for hedonic wellbeing

Baldwin

Haven‐Tang

Gill

et al. 2020

Inf Technol Tourism

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Measuring the relationship between stress, mood and tourism in natural settings is problematic in terms of the ability to undertake detailed, systematic and accurate monitoring. This paper presents the results of a preliminary investigation into the use of an immersive simulated tourism environment to measure tourisms' potential to alleviate physiological and psychological stress and enhance mood. The objectives of the study were to record and analyse participants' heart-rate data before, during and after three experiences (workplace setting, TV-watching setting and simulated tourism setting) and to undertake completion of mood questionnaires before and after each of these three experiences, allowing comparative pre-and post-mood analysis. Qualitative data was also gathered from the participants about these three experiences, in particular the simulated tourism environment. The preliminary results demonstrate that PEL effectively creates a simulated tourism environment which can be used for measuring stress and mood as signifiers of hedonic wellbeing.

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Comparing drivers’ visual attention at Junctions in Real and Simulated Environments

Cited by 26 publications

References 42 publications

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

The effect of distraction, response mode and age on peripheral target detection to inform studies of lighting for driving

Using the Perceptual Experience Laboratory (PEL) to simulate tourism environments for hedonic wellbeing

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