A tree falling on a car while it's driving: relatively unavoidable. Cars hitting that car: far less likely with computers. They don't get "shocked", or hit the wrong pedal, and have faster reaction times. And pile-ups due to an accident are often far more deadly than the accident itself, especially in busy areas where automated driving could make its biggest improvement in safety.
Deer / kids / unexpected obstacles: can be detected prior to them even entering the road by cars ahead, and flagged for caution for cars behind. IR cameras will spot living things for you, motion / blockage detection will spot things near / in the road. Once such a thing is flagged, it's reasonable to assume that other cars would leave more room between each other and around the obstacle, and probably slow down. All things which people should do, but only actually do rarely, and never collaboratively except at extreme cost and increased risk over something organized.
Yes they do. You could perhaps argue they do so less.
Unless they're using a random number generator seeded with outside data, all computer operation can be called deterministic and 100% predictable. Because it's 100% repeatable given identical preconditions. People? Not so / not currently doable.
If the wrong operation for a situation were performed, it'd be due to a higher-level decision going wrong, not the command. Meanwhile, people can "hit the brakes" (high-level command) and instead slam the accelerator (command not carried out). And fully believe they hit the brakes, and that the car accelerated uncontrollably.
I work on autonomous driving robots. Teams I have been on in the past won extremely competitive autonomous vehicle competitions. Our robots crashed all the time. ALL THE TIME!
There is nothing deterministic about how autonomous systems work. In fact our algorithms were by definition random number generators! We randomly generated "hypotheses" and then had heuristic tests that figured out which was the most likely trail and then chose it. It worked well, 99% of the time. But that 1% was a killer.
Mind you, since we were winning we were doing better than the other solutions which tried to be more deterministic and use maps or things like that. We were basically stateless which reduces some of the feedback associated with getting messed up in some of your estimations.
The problem is all at the low level, not the high level.
Give a robot a perfect map and it is easy to find a way around safely. The problem comes determining what the hell the camera is seeing (lasers are a lot easier, but not as flexible and more expensive). That is why the bulk of the effort is focused on computer vision not path finding, path finding algorithms are simple.
Sure actuating the pedals is fairly trivial, but to call autonomous systems "deterministic" is scary to someone who knows how fickle they are. I'm a huge advocate of self-driving cars, but we are not there yet, and when we get there we need to do a pile of peer review on the code (which better be open source). You never know what race conditions will blow it all up.
That's essentially the definition of deterministic. If your machine is powerful enough, you can even do it before the real one completes its operations. Anything deterministic is predictable in this manner.
That you can't predict it, or that you don't have a computer powerful enough to predict it has nothing to do with something not being deterministic, nor predictable. Even a massive, PRNG-influenced neural network is deterministic.
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On an aside for people who are thinking of quoting the halting problem at me:
The halting problem requires that the detector returns an answer, and it must be true or false and accurate, or the proof through paradox fails. Therefore, it cannot simulate the operation of what it's detecting, because there could be an infinite loop which would cause it to run without end. You also cannot merely abort it after a while, as that would be either a 3rd return state ("unknown") or an inaccurate response.
But in the field, even if it is stationary the pictures are never the same from frame to frame and everything relies on a lot a randomness, and random noise.
Simulators are almost a waste of time because the real challenge is handling that noise, NOT dealing with the information extracted after the noise. Once you have a clear view of the world, everything it is easy, it is getting the eyes to actually understand anything that is hard.
If you're suggesting that we know what a computer will do on every single given input, we don't. That's in a class of unsolvable problems, as it requires the halting problem to be solved.
[Added the following section after being down-voted]
A "shock", in the deterministic computer world, could be an incorrect classification resulting in an incorrect action. The results are similar to when a human is shocked and doesn't act correctly.
Is the scenario I've painted limited to neural nets? No, any trained classifier, even any program at all may have "minor" slips like this. (When dealing with an analogue world, there's a ridiculously large number of situations that may exist). But you know what? Humans don't perform perfectly; and a computer that's been shown to perform the correct final* response 100% of the experimental time, and 99.9997% of the real time is competent at the activity, and trustworthy.
Disclaimer: I love machine learning, I love neural networks, and I see great usefulness for them in this world; however, I also believe that the perfect combination of variables could yield unusual results. Is it unlikely? Incredibly. Possible? yes.
* Consider a situation where a child is playing with a ball. The computer may not recognize that anomaly on the side of the road as the child and the ball, or even as something to be avoided. If the computer recognizes the anomaly as something that needs more information; then tells the car to slow down until it can acquire that information, then recognizes it as something that needs to be avoided and does so, successfully, safely, and with sufficient space for the conceivable outcomes that may have resulted with this child; then, while the computer couldn't discern the object at first, it did in the end and adapted to the situation appropriately. The "final response" was correct.
[edit: rather than simply down-vote me, please tell me where I'm wrong so I can learn.]
I could go for "fully computerised traffic would be significantly safer than human traffic", but absolutes about the former being unfailing are just silly.
And of course, there will be the Android app the pedestrian can use as a last resort to avoid beng run over that stops any cars heading for the phone. :-)
I have made no claims that computers can make flawless decisions. I make no claims they can detect / avoid everything avoidable. I don't know where you're getting the idea that I've said this, unless you're dragging in past battles with other people - everywhere I've claimed computers would do better I've included qualifiers like "likely".
The point I was trying to make is that the instructions of a computer, barring external phenomena which cannot be accounted for, are fully deterministic and accurate. If the high-level decisions are made incorrectly, then the end result will be incorrect, but all down the path everything will be accurately performed.
Not so with humans (just look at someone with epilepsy, or at your own memory), or at least not in the minds of some people, which is the only reason I include it. And, if nothing else, we're far further from accurately simulating an entire human body / neural net than from simulating another computer.
Even if you have near perfect data about where the other cars are there's always uncertainty about other objects on the road: tree branches, deer, a child running after her puppy. I'd speculate that it's better to rely on a vision system that can recognize those as well as cars well enough to be safe instead of treating different classes of objects differently.