One reason might be a lack of datasets. There are a ton of datasets out there for things like traffic sign identification, and vehicle/pedestrian/etc identific

One reason might be a lack of datasets. There are a ton of datasets out there for things like traffic sign identification, and vehicle/pedestrian/etc identification - but most of those vehicles in those datasets are: 1. From the rear (or oblique rear angles) 2. Of mostly automobiles and small trucks There likely aren't many real-world datasets of vehicles facing the vehicle and stopped in the road, and/or of larger vehicles (buses, semi-trucks/lorries, emergency vehicles, bulldozers/tractors, etc). Without having such datasets, the AI you have trained won't have any way of knowing about those objects, and they may be odd enough that they can't be easily extrapolated from the datasets you do have to train on. And by "dataset" - I mean you need massive amounts of data; 50,000 images would be a "small" dataset. Ideally you want many times that, plus you want to "generate" synthetic views for more training data (by skewing/warping the existing images, it helps the system generalize on the data). That's a possible explanation, but I don't know if that's the reason, part of the reason, or no reason at all. Plus, it's not to say they are "at fault" - or to absolve the driver of any responsibility. I'm just throwing it out there, based on my knowledge and experience - which isn't much, I admit (I completed the Udacity Self-Driving Car Engineer Nanodegree last year, plus Udacity's CS373 course, and what is now the Coursera Machine Learning course - but I took it as ML Class in 2011 before Coursera existed).