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Not sure about the current state of the actual ML, but compared to other self driving companies Tesla has a treasure trove of data because they have so many veh

Not sure about the current state of the actual ML, but compared to other self driving companies Tesla has a treasure trove of data because they have so many vehicles on the road at all hours of every day. The edge cases are the parts that are hard to identify and solve so having all that drive time data to identify edge cases would seem to give them a big advantage.

jboy55 4y ago
If the data doesn't have the details required to build accurate models, then the data is just costing Tesla money. Since Tesla's are just cameras only, with telemetry, they can replay scenarios with existing roads, but what happens when someone cones off half of the road?
- Initial_BP 4y ago
  I imagine they have a way to replay incidents from their cars in a simulation and even if it's not super accurate, they could likely look at camera data and rebuild a similar situation (Sim or real life to identify and test the edge case) I work at another autonomous car company (as a security engineer not ML related work) and I know we have a Lot of simulated situations that we run the ML against and add more from situations collected from actual driving.
teyc 4y ago
Most self-driving is about avoiding collissions, and signalling intent, especially when streets are narrow and there's merging or shared use. The physics of cars, people, bikes and kids around roads are well understood (acceleration, velocity). This can be simulated, and a game engine can generate data for virtual sensors to be trained. There's no reason to require time on the road.
- cptskippy 4y ago
  But you'll never be able to come up with all of the possible scenarios to simulate. What Tesla has demonstrated is creating virtual scenarios where they can dynamic adjust all factors (light, weather, traffic, etc) and base them off real world situations they've encountered where their Model failed.
  - bastawhiz 4y ago
    Maybe not manually, but surely you could develop an adversarial ML model that quickly and concurrently tests scenarios.
    - cptskippy 4y ago
      What data is that model based off? Tesla has the data based on real world failures to build that model. Does anyone else?
      - bastawhiz 4y ago
        You can't discount all the data they Waymo has collected over nearly a decade or the scenarios they've manually created. They also have the world's most complete map and spatial dataset, which could easily be extended to create a model that creates tricky roadways. Stimulating obstructions or hardware failures doesn't require very much data at all. If you are modeling scenarios like a game engine, a "discriminator" model isn't necessary: you just check whether a simulation doesn't result in a crash.
        cptskippy 4y ago
        I'm not discounting their data, I just think Tesla has so much more. If you were looking at just those opted into the FSD Beta you have a larger fleet actively running the model with feedback loops capturing every failure. But cars without FSD are still running the model and capturing data as well.

jboy55 4y ago

If the data doesn't have the details required to build accurate models, then the data is just costing Tesla money. Since Tesla's are just cameras only, with telemetry, they can replay scenarios with existing roads, but what happens when someone cones off half of the road?

Initial_BP 4y ago
I imagine they have a way to replay incidents from their cars in a simulation and even if it's not super accurate, they could likely look at camera data and rebuild a similar situation (Sim or real life to identify and test the edge case) I work at another autonomous car company (as a security engineer not ML related work) and I know we have a Lot of simulated situations that we run the ML against and add more from situations collected from actual driving.

teyc 4y ago

Most self-driving is about avoiding collissions, and signalling intent, especially when streets are narrow and there's merging or shared use. The physics of cars, people, bikes and kids around roads are well understood (acceleration, velocity). This can be simulated, and a game engine can generate data for virtual sensors to be trained. There's no reason to require time on the road.

cptskippy 4y ago
But you'll never be able to come up with all of the possible scenarios to simulate. What Tesla has demonstrated is creating virtual scenarios where they can dynamic adjust all factors (light, weather, traffic, etc) and base them off real world situations they've encountered where their Model failed.
- bastawhiz 4y ago
  Maybe not manually, but surely you could develop an adversarial ML model that quickly and concurrently tests scenarios.
  - cptskippy 4y ago
    What data is that model based off? Tesla has the data based on real world failures to build that model. Does anyone else?
    - bastawhiz 4y ago
      You can't discount all the data they Waymo has collected over nearly a decade or the scenarios they've manually created. They also have the world's most complete map and spatial dataset, which could easily be extended to create a model that creates tricky roadways. Stimulating obstructions or hardware failures doesn't require very much data at all. If you are modeling scenarios like a game engine, a "discriminator" model isn't necessary: you just check whether a simulation doesn't result in a crash.
      - cptskippy 4y ago
        I'm not discounting their data, I just think Tesla has so much more. If you were looking at just those opted into the FSD Beta you have a larger fleet actively running the model with feedback loops capturing every failure. But cars without FSD are still running the model and capturing data as well.