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Having multiple sources of data is a benefit, not a problem. Entire signal processing and engineering domains exist to take advantage of this. Even the humble K
by _aavaa_ 10mo ago
Having multiple sources of data is a benefit, not a problem. Entire signal processing and engineering domains exist to take advantage of this. Even the humble Kalyan filter lets you combine multiple noisy sources to get a more accurate result than would be possible if using any one source.
- fpoling 10mo agoKalman filters and more advanced aggregators add non-trivial latency. So even if one does not care about cost, there can be a drawback from having an extra sensor.
- _aavaa_ 10mo agoYes, there are tradeoffs to be made, but having to reconcile multiple sensors is not intrinsically a negative. But also, if you didn’t get the right result, I don’t care how quickly you didn’t get it.
- MadnessASAP 10mo agoThe latency from video capture and recognition is going to be so significant that it makes all other latency sources not even worth mentioning.
- Zigurd 10mo agoCars and roads are built for human reaction times. That's why you have low speed limits in urban areas. You can have a pile of latencies attributable to processing a scene and still have superhuman reaction time that contributes to outperforming humans. It's analogous to communications latency. High latencies are very annoying to humans, but below a threshold they stop mattering.
- guywithahat 10mo agoWhat I've heard out of Elon and engineers on the team is that some of these variations of sensors create ambiguity, especially around faults. So if you have a camera and a radar sensor and they're providing conflicting information, it's much harder to tell which is correct compared to just having redundant camera sensors. I will also add in my personal experience, while some filters work best together (like imu/gnss), we usually either used lidar or camera, not both. Part of the reason was combining them started requiring a lot more overhead and cross-sensor experts, and it took away from the actual problems we were trying to solve. While I suppose one could argue this is a cost issue (just hire more engineers!) I do think there's value in simplifying your tech stack whenever possible. The fewer independent parts you have the faster you can move and the more people can become an expert on one thing Again Waymo's lead suggests this logic might be wrong but I think there is a solid engineering defense for moving towards just computer vision. Cameras are by far the best sensor, and there are tangible benefits other than just cost.
- ambicapter 10mo agoI don’t understand how running into difficulties when trying to solve a problem can be interpreted as “[taking] away from the actual problem”.
- guywithahat 10mo agoIn our case if we're spending a lot of time on something that doesn't improve the product, it just takes away from the product. Like if we put 800 engineering hours into sensor fusion and lidar when the end product doesn't become materially better, we could have placed those 800 hours towards something else which makes the end product better. It's not that we ran into problems, it's that the tech didn't deliver what we hoped when we could have used the time to build something better.
- karlgkk 10mo agoCounterpoint: Waymo > solid engineering defense for moving towards just computer vision COUNTERPOINT: WAYMO
- fragmede 10mo agoWe don't know enough about the internals of either one of them to make a judgement. The only one that is, is Comma.ai.
- karlgkk 10mo agoWaymo just reached 20 million public unsupervised rides. When will it be validated enough for Tesla fanboys? (Answer: never)
- fragmede 10mo agoFanboy or not, we don't know how much Waymo's model relies on an army of contractors labeling every stop light, sign, and trash can that, sure, they're using LIDAR to detect them and not cameras. We also don't know much about Tesla's Robotaxi initiative and how much human help they're relying on either.