They measured out to 18m & point out that the typical measured limits of angular resolution of the human eye mean that we could extract stereo image information out to 200m or more.
This paper claims to demonstrate stereopsis out to 250m, which is roughly the limit you’d expect from human visual acuity: https://jov.arvojournals.org/article.aspx?articleid=2191614
It seems that the claim that stereo vision only occurs in the near field case is probably wrong?
As seen on their website [1], and confirmed numerous times, they have monocular vision around the car and, though having three front-facing cameras, they each have different focal lengths and are located next to each other and thus can not operate as binocular vision.
Human life is worth about $10 million (in US), that's a bit more than the sensor does. If one in 10,000 of camera-only car causes deaths, then it's not economically viable.
A London bus costs about $300,000, it is economically viable. Why is $1,000 sensor a problem. It is definitely viable to be installed on busses and trucks. Maybe you need to get out of the mindset of personal cars. It is not a viable business model, and it is not a viable model of dealing with congestion either.
The $100,000 system would only ever make sense if the fleet was already entirely self driving, and money for other life saving stuff like the environment and health care also hit suitable diminishing returns. Of course, by then, the cheap systems will have improved.
This argument holds for any non-negative dollar value you place on human life.
It is also independent of who owns the vehicles. Money the bus fleet spends in expensive self driving pulls money away from bus stop upgrades, pollution controls, etc, etc.
Don't forget some interesting scenarios like dust particles or mud on one of the cameras (or windshield if cameras are located behind the windshield) or rain beading and distorting the image thus breaking the feature matcher and resulting disparity estimates.
Next, to "see" further, a stereo rig needs to have a decent baseline. For example, in a classic KITTI dataset, the baseline is approximately 0.54m which is much larger than, for example, human eyes (0.065m). Such baseline, 54cm, together with focal length, which, if I remember correctly, is about 720px in case of KITTI vehicle cameras, would give about 388m in the ideal case of being able to detect 1 pixel disparity. But detecting 1px of D is very difficult in practice - don't forget you will be running your algo on a car with limited compute resources. Say, you can have around 5px of D, that means max depth of around 77m - comparable to older Velodyne LiDARs.
Some of the issues I mentioned are not specific to stereovision (e.g. you need to calibrate monocular cameras as well and so on), just wanted to point out that stereovision does not magically enable depth perception. The solution would likely be a combination of monocular and stereo cameras, combined with SfM (Structure from Motion) and depth-from-stereo algorithms.
Binocular cameras provide absolute depth information, and are an order of magnitude cheaper sensors than the other options.
Since this technology is clearly computationally limited, you should subtract the budget for the sensors from the budget for the computation.
According to the article, the non-camera sensors are in the $1000’s per car range, so the question becomes whether a camera system with an extra $2000 of custom asic / gpu / tpu compute is safer than a computationally-lighter system with a higher bandwidth sensor feed.
I’m guessing camera systems will be the safest economically-viable option, at least until the compute price drops to under a few hundred dollars.
So, assuming multi-camera setups really are first to market, the question then is whether the exotic sensors will ever be able to justify their cost (vs the safety win from adding more cameras and making the computer smarter).