Team focus on vision which is by far the highest accuracy and bandwidth sensor allows for a faster rate of safety innovation given a constant team size.
The bottom line seems to be that the part shortages would have slowed production and cost cutting. The rest of the story seems like a fable to me. It was pretty clear Tesla removed the radar because it couldn't get enough radars.
The interview didn't really impress me. I'm sure Andrej is bound by NDA and not wanting to sour his relationship with Tesla/Elon but a lot of the answers were weak. (On Tesla and some of the other topics, like AGI).
I also expect an automated system to be better than the poor human in the drivers seat.
If you're against having multiple sensors though, the rational conclusion would be to just have one sensor, but Tesla would be the first to tell you that one of the advantages their cars have over human drivers is they have multiple cameras looking at the scene already.
You already have a sensor fusion problem. Certainly more sensors add some complexity to the problem. However, if you have one sensor that is uncertain about what it is seeing, having multiple other sensors, particularly ones with different modalities that might not have problems in the same circumstance, it sure makes it a lot easier to reliably get to a good answer in real-time. Sure, in unique circumstances, you could have increased confusion, but you're far more likely to have increased clarity.
Yes, in machine learning, pruning down to higher signal data is important, but good models are absolutely amazing at extracting meaningful information from noisy and diffuse data; it's highly unusual to find that you want to dismiss a whole domain of sensor data. In the cases where one might do that, it tends to be only AFTER achieving a successful model that you can be confident that is the right choice.
Tesla's goal is self-driving that consumers can afford, and I think in that sense they may well be making the right trade-offs, because a full sensor package would substantially add to the costs of a car. Even if you get it working, most people wouldn't be able to afford it, which means they're no closer to their goal.
However, I think for the rest of the world, the priority is something that is deemed "safe enough", and in that sense, it seems very unlikely (more specifically, we're lacking the tell tale evidence you'd want) that we're at all close to the point where you wouldn't be safer if you had a better sensor package. That means, in effect, they're effective sacrificing lives (both in terms of risk and time) in order to cut costs. Generally when companies do that, it ends in law suits.
More or less. You can take that decision on other grounds - e.g. "what would be safest to do if one of them is wrong and i don't know which one?"
The system is not making a choice between two sensors, but determining a way to act given unreliable/contradictory information. If both sensors allow for going to the emergency lane and stopping, maybe that's the best thing to do.
Predictability especially around failure cases is a very important feature. Most human drivers have no idea about the failure modes of lidar/radar.
It has everything to do with cost cutting?
Okay? Tesla is a car company and they are absolutely trying to sell a cheaper car. That's obvious to anyone that's been in one.
"Both methods have trade-offs."
Right, isn't that why most other systems use both?
He's not conspiring to trick people per se but he's also not being super clear. His position obviously makes it difficult to answer this question. It's possible he really believes this is better but if he didn't he wouldn't exactly tell us something that makes him and his previous employer look bad. Also his belief here may or may not be correct.
Is it a coincidence that the technical stance changed at the same time when part shortages meant that cars could not be built and shipped because of shortages of radars?
More likely there was some brainstorming as a result of the shortages and the decision was made at that point to pursue an idea of removing the additional sensors and shipping vehicles without those. This external constraint makes believing the claims that this is actually all around better, while hearing some reports of increases in ghost braking (anecdotes) a little difficult. Not clear if there was enough data at that time to prove this and even Andrej himself sort of acknowledges that it's worse by some small delta (but has other advantages, well shipping cars comes to mind).
So yes, sensors have to be fused, it's complicated, it's not clear what the best combination of sensors is, the software might be larger with more moving parts, the ML model might not fit, a larger team is hard to manager, entropy - whatever. Still seems suspicious. Not sure what Tesla can do at this point to erase that, they can say whatever they want, we have no way of validating that.
Here is one possible perspective from an engineering standpoint:
Same amount of $$, same amount of software complexity, same size of engineering teams, same amount of engineering hours, same amount of moving parts. One company focuses on multiple different sensors and complex fusion with some reliance on AI. Another company focuses on limited sensors and more reliance on AI. Which is better? I don't think the answer is clear.
The other point is that I am arguing that many people are over-stating the importance of the sensors. They are important, but far more important is the post-processing. Any raw sensor data is a poor actual representation of the real environment. It is not about the sensors, but about everything else. The brain or the post-sensor processing is responsible for reconstructing an approximation of the environment. We have to infer from previous learned experiences of the 3D world to successfully navigate. There is no 3D information coming in from sensors, no objects, no motion, no corners, no shadows, no faces, etc. That is all constructed later. So whoever does a better job at the post-processing will probably out perform regardless of the choice of sensors.
Lights?
Ultrasonics struggle in the rain, btw.
EDIT: Also I'm not quite positive why the image is so dark when I reverse at night. But it still is. The slope and surface of the driveway might have something to do with that... Still I wouldn't trust that camera. The ultrasonic sensors otoh seem to do a pretty good job. That's just my experience.
EDIT2: I love the Tesla btw. The ultrasonic sensors seem to work pretty reliably, they're pretty much their own system, the argument about complexity doesn't really seem to hold water and on the face of it the cameras won't easily replace them...
Now, simple color matching models are used in some fancy toasters on white bread to determine brownness. That's the most I've ever seen in appliances...
https://www.bloomberg.com/news/articles/2022-10-27/tesla-eng...
And the corresponding HN thread: https://news.ycombinator.com/item?id=33365065
Tesla has ca. 1000 software engineers working in various capacities. The ca. 300 that work on car firmware and autonomous driving are probably not participating in the Twitter drama.
By hiding the ball that you are starting from a much more unsafe position
They are literally the least accurate of all sensors.
Radar tells you distance and velocity of each object. Lidar tells you size and distance of each object. Ultrasonic tells you distance. Cameras? They tell you nothing!
Everything has to be inferred. Have you tried image recognition algorythms? I can recognise a dog from 6 pixels, the image recognition needs hundreds, and has colossal failures.
We have no grip on the results AI will produce and no grasp on it's spectacular failures.
Driving will have to be solved without AI