Traffic Sign Characteristics for Machine Vision Safety Benefits
| dc.contributor.author | Kassing, Andrew | en |
| dc.contributor.author | Gibbons, Ronald B. | en |
| dc.contributor.author | Li, Eric | en |
| dc.contributor.author | Palmer, Matthew | en |
| dc.contributor.author | Hamen, Johann | en |
| dc.contributor.author | Medina, Alejandra | en |
| dc.date.accessioned | 2024-07-03T15:39:54Z | en |
| dc.date.available | 2024-07-03T15:39:54Z | en |
| dc.date.issued | 2024-07-03 | en |
| dc.description.abstract | Machine vision has become a central technology for the development of automated driving systems and advanced driver assistance systems. To support safe navigation, machine vision must be able to read and interpret roadway signs, which provide regulatory, warning, and guidance information for all road users. Complicating this task, transportation agencies use a large variety of signs, which can have significantly different shapes, sizes, contents, installation methods, and retroreflectivity levels. Additionally, many environmental factors, such as precipitation, fog, dew, and lighting, also affect the visibility and legibility of roadway signs. Understanding how environmental factors and sign conditions affect machine vision performance will be important for transportation agencies to maximize the technology’s safety benefits. Research began by conducting a literature review cataloguing current research concerning roadway sign and visual performance, vehicle vision systems, and sign significance for automated driving. Information and insight gained during the literature review process informed the design and system development of data collection systems. Field data collection was then performed over the course of 3 months in late spring to early summer in 2021. Simultaneously, sign data were harvested using Google Street View and mapped using ArcGIS. Data collected during the experimental trips were then reduced and carefully prepared for analysis. Researchers conducted a thorough data analysis, particularly looking at sign location, viewing distance, sign color, font size, sun position, and illumination, to assess the impact of many environmental and infrastructure factors on the legibility of sign characters. Results showed that blue and brown signage with white legend text provided the best chance of sign character legibility during the daytime; sign characters were easy to read during the day at all three experimental distances (200, 400, and 500 ft), with small characters becoming less legible as view distance increased; daytime legibility decreased as light levels decreased; sign images captured at nighttime illumination levels had poor legibility results; sign characters on overhead signage were found to be more legible and are expected to be identified at a higher rate by vehicle vision systems; and vehicle vision systems should use a high-quality camera capable of taking pictures at night without motion blur. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120585 | en |
| dc.language.iso | en | en |
| dc.publisher | National Surface Transportation Safety Center for Excellence | en |
| dc.relation.ispartofseries | NSTSCE ; 24-UR-146 | en |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | transportation safety | en |
| dc.subject | roadway design | en |
| dc.subject | traffic signs | en |
| dc.subject | automated driving systems (ADS) | en |
| dc.subject | advanced driver assistance systems (ADAS) | en |
| dc.subject | machine vision | en |
| dc.title | Traffic Sign Characteristics for Machine Vision Safety Benefits | en |
| dc.type | Technical report | en |
| dc.type.dcmitype | Text | en |