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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

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.

Ekaros 3y ago
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.
- giantg2 3y ago
  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.
  - bhouston 3y ago
    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.
    - giantg2 3y ago
      True, historical data is basically what humans use too. I just wonder if there's a way to prevent the accidents that would generate that data
    - rented_mule 3y ago
      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.

Ekaros 3y ago

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.

giantg2 3y ago
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.
- bhouston 3y ago
  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.
  - giantg2 3y ago
    True, historical data is basically what humans use too. I just wonder if there's a way to prevent the accidents that would generate that data
  - rented_mule 3y ago
    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.