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I think the keyword you’re looking for is ‘sensor fusion’, which apparently is only a problem for Tesla because the others have figured it out years ago. Perhap
by ra7 3y ago
I think the keyword you’re looking for is ‘sensor fusion’, which apparently is only a problem for Tesla because the others have figured it out years ago. Perhaps there is a talent gap at Tesla or an unwillingness to invest in it. They generally avoid having to do anything that’s hard and try to look for shortcuts.
Case in point: the whole radar removal and reintroduction flip flop. It seems like they don’t believe themselves that cameras are sufficient.
- fastball 3y agoI don't think anyone has "figured it out" if they still don't have unconstrained driverless cars.
- ra7 3y agoThat’s tangential to your original point of discussion which was sensor integration. Sensor fusion is absolutely a solved problem. Autonomous driving requires more than just sensor fusion though.
- fastball 3y agoIt's not tangential at all. If the purpose of these sensors is for autonomous driving, and "solving" sensor fusion hasn't gotten you there, how can you possibly say it is a solved problem? Sensor fusion (in this context) is not solved until you are using fused sensors for autonomous driving.
- ra7 3y agoWhy are you equating sensor fusion with solving the entirety of autonomous driving? That’s disingenuous. You understand sub problems can be solved, right? Sensor fusion is just combining different sensor inputs so you have high confidence in your object detection and you’re not “confused with disagreements” as Tesla likes to say. Self driving involves solving prediction and planning problems too, not just sensor fusion.
- fastball 3y agoI'm not equating it with solving the entire problem, I am pushing back against the idea that autonomous driving can be sub-divided into completely discrete sub-problems. So I guess you could say no, I do not understand that sub-problems can be solved. Object detection is inextricably linked with prediction which are both inextricably linked with planning.
- ra7 3y agoOf course, they are linked. It doesn't mean you need to operate worldwide to prove sensor fusion is working. There is no scaling issue with it. Sensor fusion is already proven effective as companies using it are showing excellent safety records where they are operate. The Tesla complaints about "what do you do in case of disagreements?" don't hold good anymore. They just don't want to invest in get it working (but are now forced to as they re-introduce radar in their vehicles).
- fastball 3y agoWhat evidence do you have that there is no scaling problem with sensor fusion? As you point out, the companies that have "solved" it don't have scale.
- ra7 3y agoSensor fusion has nothing to do with scale. They are merely algorithms that combine multiple real time inputs into a single low level representation. This process remains the same whether you operate in San Francisco or worldwide. How they are interpreted is what differs from a Waymo to a Cruise to a Tesla. Waymo/Cruise get richer input they can feed into their downstream perception algorithms. Look at Waymo’s object detection benchmarks against Argoverse, KITTI and Waymo Open Dataset for their performance. They top most of them. They don’t have scale yet for different reasons (adverse weather driving, long testing cycles, costly operations setup). “If they don’t have scale, that must mean sensor fusion doesn’t work” is quite a ridiculous argument. If you think sensor fusion is not possible, then you must also believe Tesla’s attempts at re-integrating radar will not work and that they won’t achieve their stated goal of FSD.