Because Tesla have demonstrated that it's unnecessary. The depth information they are getting from the forward-facing camera is exceptional. Their vision stack now produces depth information that is dramatically superior to that from a forward-facing radar.
(It's also worth noting that depth information can be validated when the vehicle is in motion, because a camera in motion has the ability to see the scene from multiple angles, just like a binocular configuration. This is how Tesla trains the neural networks to determine depth from the camera data.)
It makes intuitive sense since you can say, play video games with one eye closed. Yes you lose field of view. Yes you lose some depth perception. But you don’t need to touch your finger tips and all your ability to make predictive choices and scan for things in your one-eyed field of view remains intact.
In fact, we already have things with remote human pilots.
So increasing the field of view with a single camera should intuitively work as long as the brains of the operation was up to the task.
What I was talking about was largely doesn’t apply to the Autopilot legacy stack currently deployed to most Tesla cars.
Personally I wish Tesla would spend a couple of months cleaning up their current beta stack and deploying it specifically for AEB. But I don’t know if that’s even feasible without affecting the legacy stack.
> Their vision stack now produces depth information that is dramatically superior to that from a forward-facing radar.
RADAR is more low fidelity though, blocky, slow and doesn't do changes in direction or dimension very well. RADAR isn't as good as humans at depth. Only benefit of RADAR is it works well in weather/night and near range as it is slower to bounce back than lasers. I assume the manholes and bridges that confuse RADAR are due to the low fidelty / blocky feedback.
LiDAR is very high fidelity and probably more precise than the pixels. LiDAR is better than humans at depth and at distance. LiDAR isn't as good at weather, neither is computer vision. Great for 30m-200m. Precise depth, dimension, direction and size of object in motion or stationary.
See the image at the top of this page and overview on it. [1]
> High-end LiDAR sensors can identify the details of a few centimeters at more than 100 meters. For example, Waymo's LiDAR system not only detects pedestrians but it can also tell which direction they’re facing. Thus, the autonomous vehicle can accurately predict where the pedestrian will walk. The high-level of accuracy also allows it to see details such as a cyclist waving to let you pass, two football fields away while driving at full speed with incredible accuracy.
> Because Tesla have demonstrated that it's unnecessary. The depth information they are getting from the forward-facing camera is exceptional.
Sure! Here's a Tesla using its exceptional cameras to decide to drive into a couple of trucks. For some strange reason the wretched human at the wheel disagreed with the faultless Tesla:
That was an issue with the path planner, not depth perception, as demonstrated by the visualisation on screen. The challenge of path planning is underrated, and it's not a challenge that gets materially easier with the addition of LIDAR or HD maps. At best it allows you to replace one set of boneheaded errors with another set of boneheaded errors.
No! It was an issue with the trucks! They shouldn't have been in the way in the first place! Don't they know a Tesla is driving through? They mustn't have been able to see it since they lack exceptional cameras.
Because the software running in release mode is a much, much older legacy stack. (Do we know if the vehicle being tested was equipped with radar or vision only?)
Because Tesla have demonstrated that it's unnecessary. The depth information they are getting from the forward-facing camera is exceptional. Their vision stack now produces depth information that is dramatically superior to that from a forward-facing radar.
https://www.youtube.com/watch?v=g6bOwQdCJrc&t=556s
(It's also worth noting that depth information can be validated when the vehicle is in motion, because a camera in motion has the ability to see the scene from multiple angles, just like a binocular configuration. This is how Tesla trains the neural networks to determine depth from the camera data.)