Comment by colordrops
3 days ago
Self driving will never handle all corner cases until they essentially have a frontal cortex. They probably need something like an LLM to help with very high level abstract situations, e.g. avoiding a hurricane like someone else mentioned in this thread.
A frontal cortex isn't enough; there are plenty of corner cases that humans fail at too. The real test is if self-driving performs on par, or better than, humans in the vast majority of cases. If it saves 50,000 lives a year to go with self-driving, it's a net-win even if there are a few people who die in situations where they would have survived with a human driver behind the wheel.
Self driving cars are not going to be accepted if they have only marginally better success rates than humans. Just look at the news. Every minor self driving incident is endlessly magnified by the media while millions of human-caused accidents are just a part of life. That's just how our brains work. All major decisions are made primarily based on emotion, not analytics.
In the case of driving and flying a significant part is the passenger's agency. There are many common sense things you can do to reduce your own chance of crashing your car. Drive defensively, don't speed, don't drive drunk. There is very little a passenger in an AV or on an airplane can do to prevent things from going wrong. And it turns out we really don't like having no agency over our own travels and that's why we have such high safety requirements for airlines — but not general aviation — and now AVs.
Human accidents don't get treated as "just a part of life", serious human driving errors are often considered so egregious that the person making the error picks up a driving ban or even a custodial sentence.
So it's actually entirely rational that the bar for companies to be able to ship software that makes those fatal errors without consequence other than an insurance payout should be higher (especially since when fatal error rates can only be estimated accurately over the order of millions of miles, driverless systems are more prone to systematic error or regression bugs than the equivalent sized set of human drivers, and the cost and appeal of autonomy probably means more experienced drivers get replaced first and more journeys get taken)
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Maybe. But insurance rates, and the government's enforcement of laws, are based on analytics, and overcome a lot of human emotional bias.
Humans don't handle all corner cases. People can be slow to react to completely novel or surprising situations. There will be corner cases where humans generally do better than a machine, but the simple rule to slow down and come to a halt if things look too weird or confusing will almost always be the right answer.
Ideally, driverless cars will one day be better drivers than humans and this will save tens of thousands of traffic deaths per year. Holding up progress because cars will be confused in extremely rare or improbable situations will cost more lives than it saves.
Not only are people slow to react to unusual situations, but this is taken advantage of by city designers to force people to slow down.
Random planters in the middle of the road? Streets that narrow and then widen? Drivers start slowly creeping along, which means they are less likely to injury pedestrians.
I think self-driving cars will only become better once they can do all the learning in real time and on-board. Otherwise, they will only be as good as the data they trained on - which is ultimately real meat driver data and a derivations of said data.
They will add flooded streets to the training simulation and this problem will go away. Eventually, the corner cases not in the training simulation will be so corner they basically never happen. Waymo can be incredibly successful without dealing with "surprise clown parade" or whatever.
this is absolutely already a thing under development, you can see Waymo is hiring for reasoning roles
Tesla's already doing it too
how would a llm help
maybe a little biological brain engineered to think it is a car with api access to the car hardware via the llm?
imagine you get into the car and in the center console you just see a floating brain in vat like fallout
The driving ML model will take care of the next 10 seconds of driving, in a fast loop deciding what steering and throttle commands to give.
The LLM will apply the high level reasoning needed to deal with longer time horizons and complex decisions, like deciding that the best way to reach the car wash 100 yards away is by walking.
Lmao what…
You sound like an econ prof: full of it and hand waving away with hypotheticals.