The Impact of a Biological Driver State Monitoring System on Visual Attention During Partially Automated Driving

Stephenson, Alice; Eimontaite, Iveta; Caleb-Solly, Praminda; Alford, Chris

doi:10.1007/978-3-030-50943-9_25

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Biological visual systems have the ability to automatically recognize and recognize objects and can adapt to particularly complex environments, which are still far from being compared with existing computer systems [1]. The biological visual system has the ability to perceive the changes of illumination, scale, position, and rotation of the target unchanged and the ability to automatically group the ordered visual features [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Image Classification and Key Technologies Based on 3D Feature Extraction Algorithm

Lei

Jia

2022

Journal of Sensors

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Image classification and recognition has a very wide range of applications in computer vision, which involves many fields, such as image retrieval, image analysis, and robot positioning. Especially with the rise of brain science and cognitive science research, as well as the increasing diversification of imaging means, three-dimensional image data mainly based on magnetic resonance image plays an increasingly important role in image classification and recognition, especially in medical image classification and recognition. However, due to the high dimensional characteristics of human magnetic resonance images, human readability is reduced. Therefore, classification and recognition of 3-dimensional images is still a challenge. In order to better extract local features from images and effectively use their spatial information, this paper improved the “feature bag” and “spatial pyramid matching” algorithms on the basis of 3D feature extraction algorithm and proposed an image classification framework based on 3D feature extraction algorithm. Firstly, the multiresolution “3D spatial pyramid” algorithm, the multiscale image segmentation and image representation method, and the SVM classifier and feature fusion method are described. Secondly, the gender information contained in the magnetic resonance images is classified and recognized on the three databases selected in the experiment. Experimental results show that this method can effectively utilize the spatial information of three-dimensional images and achieve satisfactory results in the classification and recognition of human magnetic resonance images.

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Section: Introductionmentioning

confidence: 99%

Research on Image Classification and Key Technologies Based on 3D Feature Extraction Algorithm

Lei

Jia

2022

Journal of Sensors

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In-vehicle displays for driving automation: a scoping review of display design and evaluation using driver gaze measures

Kanaan,

Powell,

et al. 2024

Transport Reviews

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Assessment of Driving Automation Interfaces via Visual Attention Measures: A Literature Review

Kanaan

Powell

Donmez

2021

Proceedings of the Human Factors and Ergonomics Society Annual

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Different in-vehicle interface designs have been proposed to support drivers in operating vehicle automation systems. Measures of visual attention are particularly useful in the evaluation of such interfaces, especially in conditions when driving performance measures cannot be used, i.e., when the driver is not physically controlling the vehicle. In this paper, we present a literature review on visual attention measures in the assessment of in-vehicle interfaces to support the use of driving automation. This review was a preliminary pilot study for a larger, ongoing scoping review investigating visual attention and driving automation more broadly. Based on a systematic search of one engineering literature database, this review focused on driving automation that does not require the driver to physically control the vehicle but does require driver involvement in some capacity. The findings of this review contributed to the development of the larger review and can help identify future research trends.

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The Impact of a Biological Driver State Monitoring System on Visual Attention During Partially Automated Driving

Cited by 3 publications

References 12 publications

Research on Image Classification and Key Technologies Based on 3D Feature Extraction Algorithm

Research on Image Classification and Key Technologies Based on 3D Feature Extraction Algorithm

In-vehicle displays for driving automation: a scoping review of display design and evaluation using driver gaze measures

Assessment of Driving Automation Interfaces via Visual Attention Measures: A Literature Review

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