Since human drivers can do this, what's missing from autonomous systems to perform here as well? More training data? Different optical or radar systems? Assuming our ability to drive isn't some God given gift, there shouldn't be a hard limit on what autonomous cars can do, yes?
They don't have enough data to brute force the problem with machine learning. The dataset available will never be large enough to allow ML autonomous cars to solve every situation a human driver can. Another AI breakthrough is needed.
LLMs are so good because they have almost the entire digitized text output of humanity available for training. They may not always provide factually correct information, but it is dished out in near perfect English (or whichever language you prefer).
Personally i think if we want to recreate a human driver full AGI is needed. But, in the right conditions current autonomous cars will work well. They just need some external helpers, like being geo-fenced to a set of roads which have all the correct sign posts and markings.
> The dataset available will never be large enough to allow ML autonomous cars to solve every situation a human driver can.
Not every human driver can solve every situation that the best human driver can solve. Also, the most dangerous situations happen at high speed, which humans aren't great at handling unless we've had a lot of training. The main reason I'm optimistic about autonomous cars is that a system like Waymo's will only get better and better over the years. And newly produced cars will be just as good as "experienced" cars.
The other problem is that even if you have car that can do 95% of the driving and only need 5%...
...that makes the human driver, who normally would get say 10k miles of practice every year will drive... 500 miles on average.
It really does need to be "car can do all of the driving", or else it will be putting people that have zero practice in those hard situations it can't handle.
Yes, I agree. And regardless of experience, people at some point get too old to drive, but don't always want to accept that fact. Having cars that can do 100 % of the driving would be great.
> Also, the most dangerous situations happen at high speed
This is a mantra from speed limit fans. There are a lot of dangerous situations happening at low speed (bicicle coming from left in an intersection, children playing )
I am sure they will do this eventually, but it is next level.
They would likely have to setup in a Northern location and run their cars in that location for a while. Also need to update their simulations to account for snow and icy roads. So more complex driving models and more challenging road detection.
Sometimes there is light snow over the whole road and there are no markings. You just have to position yourself relative to street signs or ditches where you think your lane is.
Also we then get to some more interesting challenges. Certain snow conditions can have rather bad traction like very muddy roads do. And self-driving should be able to navigate these better than regular drivers.
How can the car determine the road condition, especially if it varies (snow here vs ice under the snow in this spot vs that's a drift)? It seems like a lot of this is based on driver experience whereas traction controls today are based on things like differential wheel speed comparison. It won't determine a bad situation until you're in it.
Autonomous vehicles are good at making routine repeatable decisions based on clear data. I'm not sure how we can sense and input this sort of data clearly enough.
Eventually these autonomous vehicles should share driving condition data in real-time-ish. If one car finds a stretch of the road slippery, it should basically mark it on the map as a bad location, and then other cars and be careful.
I think weather conditions can also predict road conditions pretty well. And the combine that with historical data on where things were slippery, I bet you can create a pretty good model of what conditions to expect across a driving environment.
Although in Canada, sometimes all roads in a city are just horrid - dozens of cars in ditches or hitting each other because no one can stop. And maybe in those cases, autonomous vehicles should refuse to drive until it is improved.
Where I live, the sharing is done at a meta level. I live in the Sierra Nevada mountains of California and we often get experienced snow drivers who get stuck immediately because they aren't used to driving on 25% grades that experience frequent freeze / thaw cycles (and therefore generate ice). The issues here are predictable and don't even require real-time sharing.
When someone new moves into town, someone will typically explain to them to watch for where the sun hits the ground in winter. Where that happens on a steep slope, avoid driving there when there is snow. Before they know to do this, a human will attempt to climb that hill, not make progress because of the ice, back down, and try another route. This is a rural area without much traffic, so conditions might change significantly between "real-time" updates.
So, we have human drivers "zooming out" in a couple of ways... in time/context (observing conditions that lead to certain routes becoming high risk) and in problem resolution (try another route). This is quite different from what is currently being tried algorithmically.
You need more training data and more sensors and completely different rules that are often opposite the rules for good conditions. The things you really need for snow/ice that are hard for computers are anticipation and flexible rules.
