That's gotta be a bigger project in terms of man power and money than anyone has invested in self driving cars already. We need more testing to even establish the problem areas and figure out how to actively solve them.
I'm wondering how this is going to be different than self-driving, where we can get 90% of the way there, but the last 10% is notoriously difficult with not nearly enough edge cases represented in the data
But this hasn't applied for self-driving cars, there has been huge amounts of money poured into it and very smart people working on the problem and yet the results have not really been up to the level.
Self-driving cars is one of the biggest boondoggles of all time. How much money, time, effort, amd resources have been spent on this? And yet, there's barely anything to show and the end goal is still not even clear.
I asked this question before and received no answers, so I'll ask again: Is there a source that details the specific issues that self-driving cars could face, and potential methods of solving them?
It’s worth noting that many of us consider self driving to he essentially analogous to full AI in its complexity and difficulty, and thus believe that we are literally nowhere near anything that fits this description.
No amount of “data” is going to solve the problem. Driving a car requires making a full mental model of the immediate world you’re in and creating accurate predictions of what everything else in that world is about to do, many of those things being sentient beings with whom you’re communicating through your own actions.
Nothing that Tesla is doing is getting much closer to that. The next major landmark on this timeline is a team successfully passing the Turing test, not a car moving across a parking lot.
If you share that opinion, that makes the Tesla a lethal toy.
The issue is that self driving needs to be better than human drivers by a considerable margin. The public will not tolerate an AI driver that makes the same mistakes as humans, they expect better. To do this it will need access to sensors that humans don't have
The issue with that is NN fail in some really interesting ways so you still need a lot of effort to get a robust solution. Remember, after some serious investments by many organizations self driving cars are still in development. At the same time a few people have demonstrated a basic system that seems close without nearly that much investment. Unfortunately, the difference between a demo and working solution can be several orders of magnitude.
this is quite an academic exercise since a decade of intensive research hasn't brought us close to working self-driving cars, much less 1x safe self-driving cars, much less 5x safe, nor is there any clear path to resolving this open research problem.
That's not a bad sounding idea, but it makes a lot of assumptions about the infrastructure available in the region the self-driving car is being deployed.
If the goal is really to make cars that can drive as well as humans, they need to be able to do that based only on on-board sensors.
Thanks for data point. How is it interacting with the self-driving cars? I'm concerned that a lot of thought has gone into experience for the passenger of self driving car, but much less for the rest of general public. Feels like they missed an opportunity to improve driving experience for rest of us as well with some more signals or communication between vehicles.
Self-driving is not going to be solved by throwing money at it. If it was, Google would have "solved" it already and Apple wouldn't have abandoned their effort.
The obvious shortage is engineering talent. This is more debatable (many people, like Elon Musk, don't believe this to be the case), but the field also needs one or more research breakthroughs, especially in the realm of planning and reasoning. Most of the machine learning in self-driving cars goes towards perception / detection / mapping. The actual decision-making is still heuristics and hard-coded rules.
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