this is an interesting and prescient comment for a couple reasons...

1) road sign standardization does exist, it is called the MUTCD [0] and SHOULD be highly usable for this type of training of image systems. I would be shocked (really shocked) if there are people working on this who have not loaded the full size EPS graphics into their software in some way [1]. However, when you install billions (I'm WAGing there are probably billions of road signs in the US?) of signs - variance is problematic. The signs themselves can age, wear, or be wrongly printed. They can also be effected by situational variables such as blocked by a tree limb, installed incorrectly (misaligned), or be temporary, or be affected by weather (snow, ice, rain). Standardizing is very much done but has its limits.

2) Your examples of 'legibility' is really interesting because it presumes human sense - e.g., primarily relying on sight. There are lots of ways to make roads signs legible that rely on senses humans do not possess but computers can. Rather than putting code on signs that relies on vision, it would make much more sense to satisfice other sensing methods for communicating/broadcasting information about roadways. You could put long distance RFID in the signs, hell this seems like an actually good use of IoT. It would help solve the visibility, weather, aging, and temporary signage problems. Prior to the current age of self driving cars, this type of non-vision, non-self contained approach, roads designed for self driving cars was considered the most viable [2, see pages 18-19]. Good maps loaded into your onboard systems make a road more legible - even if they can't be seen. There are already ways to hack non-visible ways of making the road system 'legible' [3]

3) The last piece is that there is a huge difference between making the road signs themselves legible and making roads legible. [2, section 5.1, pg. 107] talks about this extensively and our perception of how we think about sensing as people and how we ontologically categorize vehicles as independent of the road...Its why you some people have laughed at the idea of working to better prepare roads for self driving cars [4], even in ways that were used throughout later 1900's attempts at self driving cars. for purposes of the current US road network that is probably a good assumption. Somewhere like Japan, you can probably assume the roads are much more consistently designed, signed, and implemented. Unsurprisingly, the first actual for sale level 3 car is in Japan [5].

[0] https://mutcd.fhwa.dot.gov/ser-shs_millennium.htm

[1] https://mutcd.fhwa.dot.gov/shsm_interim/index.htm#sas

[2] https://cmsw.mit.edu/wp/wp-content/uploads/2016/06/268475427...
footnote 28: In her book Magnetic Appeal, Joyce argues that “seeing does not equal truth or unmediated access to the human body,” but that practices equating these are so common that images are often used to stand for truth despite doctors’ awareness of how social practices shape this evidence [Joyce, 2008,p. 76]. Popular narratives are particularly prone to fall prey to the “myth of photographic truth” [Joyce, 2008, p. 75]. These tendencies are of great relevance when considering other complex, technological projects dependent on imaging and which use images rhetorically, to stand for the “truth” of their ability to perform a task—such as detect a pedestrian in a crosswalk.

[3] https://www.wired.com/story/99-phones-fake-google-maps-traff...

[4] https://www.washingtonpost.com/news/innovations/wp/2014/03/1...

[5] https://www.caranddriver.com/news/a35729591/honda-legend-lev...