The article didn’t really clarify that part, so it’s impossible to tell. My guess is, they tested the system by intentionally driving under it a 100 times with a phone in your hand. If the camera caught 95 of those, that’s how you would get the 95% catch rate. That setup has the a priori information on about the true state of the driver, but testing takes a while.

However, that’s not the only way to test a system like this. They could have tested it with normal drivers instead. To borrow a medical term, you could say that this is an “in vivo” test. If they did that, there was no a priori information about the true state of each driver. They could still report a different 95% value though. What if 95% of the positives were human verified to be true positives and the remaining 5% were false positives. In a setup like that we have no information about true or false negatives, so this kind of test setup has some limitations. I guess you could count the number of cars labeled negative, but we just can’t know how many of them were true negatives unless you get a bunch of humans to review an inordinate amount of footage. Even then you still wouldn’t know for sure, because humans make mistakes too.

In practical terms, it would still be a really good test, because you can easily have thousands of people drive under the camera within a very short period of time. You don’t know anything about the negatives, but do you really need to. This isn’t a diagnostic test where you need to calculate sensitivity, specificity, positive predictive value and negative predictive value. I mean, it would be really nice if you did, but do you really have to?