[10x_contrarian]

He clearly states that extra sensors "contribute noise and entropy into everything. And they bloat stuff." https://www.youtube.com/watch?v=_W1JBAfV4Io

Essentially that trying to utilize multiple sensors cripples any progress (given that resources will never be infinite).

[Ono-Sendai]

While technically true that extra sensors contribute noise, surely with the proper programming they should be helpful. You just need to weight the information from you extra sensors appropriately based on your confidence the signal is accurate.

For example, if your lidar sensor is 99.999% percent sure there is an obstacle in front of you, surely it's helpful to take that information into account, even if it is a tiny bit uncertain/noisy.

[pclmulqdq]

It's too bad that none of their competitors, who all have better results, disagree.

[c4ptnjack]

Wouldn't that be more related to parallel processing power and throughout capabilities than feasibility?

More data in any situation where bandwidth is already maximized will lead to entropy and noise. However, if the capabilities were there to process all of that data in low latency scenarios with headroom to spare, surely adding additional sensors and data points would lead to a more complete model of spatial awareness for the car.

That's all hypothetical and reliant on ignoring the realities of running a business and tech development lol

[jeffreygoesto]

A bit like our discussion here. More standpoints can ideally be merged into a coherent, more complete picture. The key is to sort out disagreements as coming from different backgrounds and biases and everybody admitting she is not 100% right. Otherwise it is a quarrel of stubborn knowitalls that can't agree.

Fusion is hard. As hard as getting humans to agree. Been there. In both situations.

And of course you can concentrate on improving one echo chamber, err single sensor. But you can never come past its fundamental limitations.