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I'm kind of coming around to the idea that Comma AI's approach of full end-to-end is the right one. Tesla, Waymo, Cruise, etc all take real world data and load

I'm kind of coming around to the idea that Comma AI's approach of full end-to-end is the right one. Tesla, Waymo, Cruise, etc all take real world data and load it into a virtual representation, then do planning inside virtual space, then execute that plan in the real world. It seems like this is just really clunky and doesn't handle weird edges very well. Comma AI's thesis is that creating this big virtual world to do planning in is both a waste of time and compute, and also a worse driving experience. They just take a ton of human data, and essentially say "given this situation, what would a human do?". I.e. it's a neural network that takes in the last few frames of video, and outputs steering + accelerator + brake commands, with no intermediate virtual space representation. A huge advantage of this approach is it scales really well. Human data in, model out. They are maybe a year behind Tesla FSD in terms of capability with a tiny team of ~15 or so, and they are gaining ground. Tesla could probably improve their system by adopting a similar end-to-end approach, but I think this may not be easy to do for organizational/corporate political reasons. They have several hundred engineers working on all of the various bits of their FSD stack who are all incentivized to defend their turf.

birken 4y ago
As a Comma owner (2 Commas in fact), I feel like Comma's main innovation is only releasing features (on the main branch at least) that they feel are quite battle tested, and at the same time not being afraid to lean into the fact that Comma is somewhat limited and isn't full FSD. My Commas are bulletproof at doing what I expect them to do, and they don't do what I don't expect them to do. And from my perspective as a driver, it's incredibly, incredibly useful. When driving is easy and mundane I can trust that Comma can handle it (and Comma will tell me if it can't), and when driving is hard then I don't expect Comma to do handle every complex situation (though it does do a good job most of the time). They don't overpromise, and they deliver what they promise. I'm not convinced this method will get to FSB, but I from my perspective I don't think that is necessary. I genuinely believe if every car on the road only had Comma's capabilities and no more, that would on its own reduce highway accidents by like 50%. I really wish some car manufacturers would lean into this and work with Comma on making a first-hand integration. I would 100% buy any type of car from any manufacturer if it had first-hand Comma support.
sho_hn 4y ago
This is not good enough to handle edge cases. It's a little bit like ChatGPT: ChatGPT is a next-word prediction engine. It has ingested and analyzed "human data" which has given it a take on what might come after a prompt. With good training data, a large enough token window and a helping of RLHF this can do very cool things. But there's no actual reasoning going on. An "edge case" is a prompt that throws this approach for a spin, because statistics over the training data corpus don't yield a good prediction on it. For example because it's novel or its structure is uncommon. In those cases ChatGPT will do its best and confidently return something very dumb. With the driving problem, Tesla and others are trying to mitigate this with two approaches: - superstructures of rule-based planning to keep the predictions within safe lanes (no pun intended), i.e. bake in some reasoning after all - trying to write simulations that auto-generate "edge cases" or just stuff that is underrepresented in the training data set and add those simulated 3d renders to editorialize the predictions It's currently still unclear if this is enough to solve the problem or if we're still some innovations (e.g. ones that get us closer to AGI) away from being able to make it work.
- yowzadave 4y ago
  I wonder if self-driving doesn't actually require reasoning, though. Most of us are able to drive instinctively, without fully engaging all of our attention; we certainly aren't solving problems with deep analytical thinking. Perhaps it's a problem that's well-suited to the approach an AI like ChatGPT takes?
  - gamegoblin 4y ago
    I agree. I go weeks at a time without having to do any critical thinking whatsoever. There probably are some situations that do actually require a looping reasoning process that a simple feed-forward neural network can't do, but they are fairly rare. The most common ones probably actually involve complex parking lot navigation tasks, where you have to negotiate the space with other drivers, understand their intentions, yield space, take space, etc.
    - archagon 4y ago
Avshalom 4y ago
>>takes in the last few frames of video isn't that how uber killed a woman walking a bike in phoenix? During the critical last second(s) the model didn't know what she was and kind of ignored her even though previous seconds had tagged her as a moving cyclist.
- gamegoblin 4y ago
  From the wiki article about the Uber incident: > The recorded telemetry showed the system had detected Herzberg six seconds before the crash, and classified her first as an unknown object, then as a vehicle, and finally as a bicycle, each of which had a different predicted path according to the autonomy logic. 1.3 seconds prior to the impact, the system determined that emergency braking was required, which is normally performed by the vehicle operator. However, the system was not designed to alert the operator, and did not make an emergency stop on its own accord, as "emergency braking maneuvers are not enabled while the vehicle is under computer control, to reduce the potential for erratic vehicle behavior", according to NTSB So Uber had a lot of systemtic issues leading to the crash, but even if most of those were fixed, I think Comma's approach works better here because it is explicitly not trying to do any of this fancy classification and prediction. If a human sees an unknown moving object near the road at night, they start slowing down even before they have identified the object. Comma is trained to just do what a human would do, so it will also slow down. [1] https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg
  - Avshalom 4y ago
    Jesus that's right, I forgot they had turned off braking, fair enough to the Comma folks.
Vecii 4y ago
I'm not sure where you heard this, but you heard wrong. Tesla absolutely does use real world data to train their neural nets.
- gamegoblin 4y ago
  I think you have misinterpreted my comment. Of course Tesla uses real world data. My claim is that their self driving system it not end-to-end. That is, it's not "just" a neural network that takes sensor data as input and gives steering/accelerator/brake commands as output. They take in data from all their sensors, fuse it together, and create a real-time virtual copy of the world. They do planning inside this virtual copy of the real world. This has been demonstrated in many Tesla presentations. The FSD visualization on the screen of the Tesla is itself a depiction of the virtual world being constructed from the sensor data.
panarky 4y ago
> takes in the last few frames of video, and outputs steering + accelerator + brake commands Isn't this pretty close to what Tesla does? Cars phone home with timestamped video from multiple cameras plus data on what hundreds of thousands of human drivers do, also timestamped. Use all the human events to train a DNN to make similar decisions as humans. Then augment that with GPS, maps and routing to a destination.
- gamegoblin 4y ago
  No, Tesla does something much, much more complex. They build an entire 3D representation of the world and do path planning inside that virtual model. Check out the videos from the last Tesla AI day. They are creating a voxel representation of the world, doing object classification and loading objects into the world, doing monte carlo tree search path planning in the virtual world, etc.

birken 4y ago

As a Comma owner (2 Commas in fact), I feel like Comma's main innovation is only releasing features (on the main branch at least) that they feel are quite battle tested, and at the same time not being afraid to lean into the fact that Comma is somewhat limited and isn't full FSD. My Commas are bulletproof at doing what I expect them to do, and they don't do what I don't expect them to do. And from my perspective as a driver, it's incredibly, incredibly useful. When driving is easy and mundane I can trust that Comma can handle it (and Comma will tell me if it can't), and when driving is hard then I don't expect Comma to do handle every complex situation (though it does do a good job most of the time). They don't overpromise, and they deliver what they promise. I'm not convinced this method will get to FSB, but I from my perspective I don't think that is necessary. I genuinely believe if every car on the road only had Comma's capabilities and no more, that would on its own reduce highway accidents by like 50%. I really wish some car manufacturers would lean into this and work with Comma on making a first-hand integration. I would 100% buy any type of car from any manufacturer if it had first-hand Comma support.

sho_hn 4y ago

This is not good enough to handle edge cases. It's a little bit like ChatGPT: ChatGPT is a next-word prediction engine. It has ingested and analyzed "human data" which has given it a take on what might come after a prompt. With good training data, a large enough token window and a helping of RLHF this can do very cool things. But there's no actual reasoning going on. An "edge case" is a prompt that throws this approach for a spin, because statistics over the training data corpus don't yield a good prediction on it. For example because it's novel or its structure is uncommon. In those cases ChatGPT will do its best and confidently return something very dumb. With the driving problem, Tesla and others are trying to mitigate this with two approaches: - superstructures of rule-based planning to keep the predictions within safe lanes (no pun intended), i.e. bake in some reasoning after all - trying to write simulations that auto-generate "edge cases" or just stuff that is underrepresented in the training data set and add those simulated 3d renders to editorialize the predictions It's currently still unclear if this is enough to solve the problem or if we're still some innovations (e.g. ones that get us closer to AGI) away from being able to make it work.

yowzadave 4y ago
I wonder if self-driving doesn't actually require reasoning, though. Most of us are able to drive instinctively, without fully engaging all of our attention; we certainly aren't solving problems with deep analytical thinking. Perhaps it's a problem that's well-suited to the approach an AI like ChatGPT takes?
- gamegoblin 4y ago
  I agree. I go weeks at a time without having to do any critical thinking whatsoever. There probably are some situations that do actually require a looping reasoning process that a simple feed-forward neural network can't do, but they are fairly rare. The most common ones probably actually involve complex parking lot navigation tasks, where you have to negotiate the space with other drivers, understand their intentions, yield space, take space, etc.
  - archagon 4y ago

Avshalom 4y ago

>>takes in the last few frames of video isn't that how uber killed a woman walking a bike in phoenix? During the critical last second(s) the model didn't know what she was and kind of ignored her even though previous seconds had tagged her as a moving cyclist.

gamegoblin 4y ago
From the wiki article about the Uber incident: > The recorded telemetry showed the system had detected Herzberg six seconds before the crash, and classified her first as an unknown object, then as a vehicle, and finally as a bicycle, each of which had a different predicted path according to the autonomy logic. 1.3 seconds prior to the impact, the system determined that emergency braking was required, which is normally performed by the vehicle operator. However, the system was not designed to alert the operator, and did not make an emergency stop on its own accord, as "emergency braking maneuvers are not enabled while the vehicle is under computer control, to reduce the potential for erratic vehicle behavior", according to NTSB So Uber had a lot of systemtic issues leading to the crash, but even if most of those were fixed, I think Comma's approach works better here because it is explicitly not trying to do any of this fancy classification and prediction. If a human sees an unknown moving object near the road at night, they start slowing down even before they have identified the object. Comma is trained to just do what a human would do, so it will also slow down. [1] https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg
- Avshalom 4y ago
  Jesus that's right, I forgot they had turned off braking, fair enough to the Comma folks.

Vecii 4y ago

I'm not sure where you heard this, but you heard wrong. Tesla absolutely does use real world data to train their neural nets.

gamegoblin 4y ago
I think you have misinterpreted my comment. Of course Tesla uses real world data. My claim is that their self driving system it not end-to-end. That is, it's not "just" a neural network that takes sensor data as input and gives steering/accelerator/brake commands as output. They take in data from all their sensors, fuse it together, and create a real-time virtual copy of the world. They do planning inside this virtual copy of the real world. This has been demonstrated in many Tesla presentations. The FSD visualization on the screen of the Tesla is itself a depiction of the virtual world being constructed from the sensor data.

panarky 4y ago

> takes in the last few frames of video, and outputs steering + accelerator + brake commands Isn't this pretty close to what Tesla does? Cars phone home with timestamped video from multiple cameras plus data on what hundreds of thousands of human drivers do, also timestamped. Use all the human events to train a DNN to make similar decisions as humans. Then augment that with GPS, maps and routing to a destination.

gamegoblin 4y ago
No, Tesla does something much, much more complex. They build an entire 3D representation of the world and do path planning inside that virtual model. Check out the videos from the last Tesla AI day. They are creating a voxel representation of the world, doing object classification and loading objects into the world, doing monte carlo tree search path planning in the virtual world, etc.