Someone else mentioned airbags. Airbags are great overall but increase danger for some people, and that's without obvious signs of anything being wrong.
With AEB we know it makes dangerous mistakes in significant amounts. So we very much should analyze different implementations under different kinds of weather, road, and driving style.
Is it a new behavior and jarring when engaged? Certainly. Should we keep watching the data for problems? Certainly. Should folks report all problems so that they are tracked? Certainly.
Do most people actually have trouble driving safely? Despite their insistent view of how great they are at driving? ...
I have been in a car where the alert went off that it was engaging aeb. The driver was far too close to the car in front of us...
https://www.youtube.com/watch?v=BLRRBHNjCn4 There's a car that it's reasonable to worry about, but it was clearly braking and almost stopped by the time AEB kicked in, and AEB kept going as that car went to zero.
https://www.youtube.com/watch?v=TO6tJnM_HSs Adaptive cruise was already in control and working fine.
https://www.youtube.com/watch?v=hCk6yrN453c Absolutely nothing in front.
And whatever happened here probably counts: https://theintercept.com/2023/01/10/tesla-crash-footage-auto...
Yes. This was my movivation for questioning the "data says" logic. The NHTSA has made decisions based on data that were poor decisions because they misunderstood the problem domain and became overly reliant on specific metrics. Here is a copy-paste of an earlier comment in this thread:
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> the NHTSA is a pretty cautious organization, and they like numbers.
They may be cautious but they're not necessarily smart. Their vehicle safety ratings only measure the safety of occupants in that vehicle. Result? An arms race where cars keep getting bigger, heavier, and taller to do better in crash tests against bigger, heavier, and taller cars. These "safer" vehicles are now more deadly to everyone else, especially pedestrians and bicyclists.
McNamara fallacy in action.
That said, I could cede this point if you can show evidence of it in these studies. The org is a bunch of people, so I grant they could make mistakes. Without specific concerns, this is the definition of fud. You aren't giving criticism of these studies and actions, but seeding doubt on their data.
This sounds like the beginnings of the McNamara fallacy: that there is some abstract "data" where all the relevant variables can be explored to decide the best course of action. The reason it's a fallacy is that data is itself only a reflection of conscientious decisions about what to measure and how to measure, and these are invariably limited to what we already appreciate and understand, not accounting for the things we don't know how to measure and don't know we should be measuring.
To be more concrete, let's say we follow your suggestion and focus on crashes. Maybe it's found that automatic breaking reduces the number of crashes so it's mandated. But maybe automatic breaking also increases the likelihood that a crash is deadly in some sub-population like pedestrians, construction workers, and motorcyclists. So overall we see crashes decline because a large number of minor crashes are averted but we also see deadly crashes increase because pedestrians, construction workers, and motorcyclists are killed more frequently.
So data isn't a free lunch. Every decision about what to measure and what to optimize involves some prior contextual understanding. A misunderstanding of the context means the data will misrepresent some aspect of the problem and you'll overoptimize yourself into a local maximum.