Mao, Yichong (2021) An investigation by experimentation of road lighting and the performance of typical pedestrian tasks after dark. PhD thesis, University of Sheffield.
Abstract
The lighting recommendations and guidelines for pedestrians propose that road lighting in residential roads mainly aims to enhance the walking safety after dark. However, the lighting standards may not be supported by sufficient empirical evidence. The key visual tasks for pedestrians are obstacle detection and facial emotion recognition (FER). These have been studied in previous work but there are a number of limitations: FER studies have used 2D images and not 3D models; obstacle detection studies have used raised but not lowered trip hazards; these tasks were the sole focus of trials and hence were able to use a greater degree of cognitive resource than when in natural conditions. Further work was therefore conducted to investigate these limitations, and the implications for previous conclusions about how lighting changes affect the ability to detect peripheral objects and identify facial expressions.
Two pilot studies were conducted to test if 3D face models can be used for FER. The results confirmed that 3D face models could replace photographs by comparing the results with previous studies which were using photographs. Three experiments were carried out. Experiment 1 compared obstacle detection performance when raised or lowered obstacles: no significant difference was found. Experiments 2 and 3 followed the methods used in previous obstacle detection and FER experiments but sought performance of these tasks in parallel rather than as separate experiments, thus to explore whether multi-tasking affects the performance of obstacle detection and FER. Experiment 2 used two illuminances; experiment 3 used a similar combination of obstacle locations, obstacle heights, emotion types and task conditions but expanded to five levels of illuminance. The results revealed a plateau-escarpment relationship between both obstacle detection and FER and light level.
To consider the impact of multitasking, these results were compared with the results of previous studies where obstacle detection and FER were performed in isolation. This comparison suggests that the performance of each task was impaired when conducting multi tasks.
It is concluded that the optimal horizontal illuminance for obstacle detection is 1.0 lux, even for multi-task condition. For FER, the optimal luminance was suggested to be 0.53 cd/m2 which was slightly lowered than proposed before. Further work is required to address the limitations of this research, including the impact of disability from glare, and variations in face orientation and lighting geometry.
Metadata
Supervisors: | Fotios, Steve |
---|---|
Related URLs: | |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Identification Number/EthosID: | uk.bl.ethos.842825 |
Depositing User: | Mr Yichong Mao |
Date Deposited: | 07 Dec 2021 16:07 |
Last Modified: | 01 Jan 2022 10:54 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:29792 |
Download
Final eThesis - complete (pdf)
Filename: full thesis_minor_1111_formatted.pdf
Licence:
This work is licensed under a Creative Commons Attribution NonCommercial NoDerivatives 4.0 International License
Export
Statistics
You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.