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cpgxiii | 1 month ago
> Lidar just fundamentally can’t read signs, traffic lights or road markings in a reliable way.
Actually, given that basically every meaningful LIDAR on the market gives an "intensity" value for each return, in surprisingly many cases you could get this kind of imaging behavior from LIDAR so long as the point density is sufficient for the features you wish to capture (and point density, particularly in terms of points/sec/$, continues to improve at a pretty good rate). A lot of the features that go into making road signage visible to drivers (e.g. reflective lettering on signs, cats eye reflectors, etc) also result in good contrast in LIDAR intensity values.
energy123|1 month ago
It's like having 2 pilots instead of 1 pilot. If one pilot is unexpectedly defective (has a heart attack mid-flight), you still have the other pilot. Some errors between the 2 pilots aren't uncorrelated of course, but many of them are. So the chance of an at-fault crash goes from p and approaches p^2 in the best case. That's an unintuitively large improvement. Many laypeople's gut instinct would be more like p -> p/2 improvement from having 2 pilots (or 2 data streams in the case of camera+LIDAR).
In the camera+LIDAR case, you conceptually require AND(x.ok for all x) before you accelerate. If only one of those systems says there's a white truck in front of you, then you hit the brakes, instead of requiring both of them to flag it. False negatives are what you're trying to avoid because the confusion matrix shouldn't be equally weighted given the additional downside of a catastrophic crash. That's where two somewhat independent data streams becomes so powerful at reducing crashes, you really benefit from those ~uncorrelated errors.
powerapple|1 month ago
setr|1 month ago
cameronh90|1 month ago
I know there are theoretical and semi-practical ways of reading those indicators with features that are correlated with the visual data, for example thermoplastic line markings create a small bump that sufficiently advanced lidar can detect. However, while I'm not a lidar expert, I don't believe using a completely different physical mechanism to read that data will be reliable. It will surely inevitably lead to situations where a human detects something that a lidar doesn't, and vice versa, just due to fundamental differences in how the two mechanisms work.
For example, you could imagine a situation where the white lane divider thermoplastic markings on a road has been masked over with black paint and new lane markings have been painted on - but lidar will still detect the bump as a stronger signal than the new paint markings.
Ideally while humans and self driving coexist on the same roads, we need to do our best to keep the behaviour of the sensors to be as close to how a human would interpret the conditions. Where human driving is no longer a concern, lidar could potentially be a better option for the primary sensor.
cpgxiii|1 month ago
Conflicting lane marking due to road work/changes is already a major problem for visual sensors and human drivers, and something that fairly regularly confuses ADAS implementations. Any useful self-driving system will already have to consider the totality of the situation (apparent lane markings, road geometry, other cars, etc) to decide what "lane" to follow. Arguably a "geometry-first" approach with LIDAR-only would be more robust to this sort of visual confusion.
ActorNightly|1 month ago
The focus shouldn't be on which sensor to use. If you are going to use humans as examples, just take the time to think how a human drives. We can drive with one eye. We can drive with a screen instead of a windshield. We can drive with a wiremesh representation of the world. We also use audio signals quite a bit when when driving as well.
The way to build a self driving suite is start with the software that builds your representation of the world first. Then any sensor you add in is a fairly trivial problem of sensor fusion + Kalman filtering. That way, as certain tech gets cheaper or better or more expensive and worse, you can just easily swap in what you need to achieve x degree of accuracy.
cpgxiii|1 month ago
We truly have no understanding of how the human brain really models the world around us and reasons over motion, and frankly anyone claiming to is lying and trying to sell something. "But humans can do X with just Y and Z..." is a very seductive idea, but the reality is "humans can do X with just Y, Z, and an extremely complex and almost entirely unknown brain" and thus trying to do X with just Y and Z is basically a fool's errand.
> ...builds your representation of the world first...
So far, I would say that one of the very few representations that can be meaningfully decoupled from the sensors in use is world geometry, and even that is a very weak decoupling because the ways you performantly represent geometry are deeply coupled with the capabilities of your sensors (e.g. LIDAR gives you relatively sparse points with limited spatial consistency, cameras give you dense points with higher spatial consistency, RADAR gives you very sparse targets with velocity). Beyond that, the capabilities of your sensors really define how you represent the world.
The alternative is that you do not "represent" the world but instead have that representation emerge implicitly inside some huge neural net model. But those models and their training end up even more tightly coupled to the type of data and capabilities of your sensors and are basically impossible to move to new sensor types without significant retraining.
> Then any sensor you add in is a fairly trivial problem of sensor fusion + Kalman filtering
"Sensor fusion" means everything and nothing; there are subjects where "sensor fusion" is practically solved (e.g. IMU/AHRS/INS accelerometer+gyro+magnetometer fusion is basically accepted as solved with EKF) and there are other areas where every "fusion" of multiple sensors is entirely bespoke.