>a sizable number of them are due to the limits of human visual spectrum input But how do you know that? You don't really. The lion's share could well be from

>a sizable number of them are due to the limits of human visual spectrum input But how do you know that? You don't really. The lion's share could well be from just not being consistently careful in the right ways, or reckless high speed driving, or the tyranny of human reaction times. I do agree that at this stage, it's worth trying to pile on all of the sensors possible to see whether that gets you more reliable odometry / mapping. But you are getting super fixated on the specific problem of knowing what the shape of things around you is, and what people are trying to get across to you is that the problem is way, way harder than that. Just knowing where stuff is is like 10% of the problem. You also have to know what that stuff is and how it semantically relates to your driving problem. You have to be able to make predictions about how the world is going to play out, and what constitutes a safe action. And that's not even the hardest part - you also have to figure out how to glide through the world and not drive like a grandma, even though there is some inherent unsafety in building up a head of steam in a 2 ton piece of metal when a child could dive in front of you at any time. In other words, you have to be able to push the limits of UNsafe actions so that you're not completely obnoxious to be around on the road. All of this is going to require a many-levels-up understanding of the world around the car, and that understanding is going to do a ton of heavy lifting in terms of interpreting things that haven't been directly sensed. As soon as you get an occlusion, you have already left the realm of sensing and entered into the higher level realm of reasoning about the world's state based on priors. It might be that the other sensing modalities will help you be better than a human, but by far the more important unsolved problem is getting human-ish to begin with, and we are still very far from that. The other thing you're brushing past is that high-level reasoning does not have an infinite bandwidth of attention. As you ingest more and more sensors, your sensor fusion problem becomes much more expensive. For a reasonable amount of compute hardware, and with the constraints of latency, you don't have infinite latitude to reason over ever-wider sensor streams. So at a certain point you're going to encounter a squaring effect with having to add both sensors and the compute to handle it, with additional considerations once that data gets so wide that "normal" ways of handling it stop working and you have to get exotic. For a given compute substrate, it is not a given that the best way to use it is to slurp a massively wide sensor suite into it. Your cycles could be better spent doing deeper reasoning over the data you've got. If you need an evolutionary analogy, you can ask yourself why two eyes seems to be plenty for the majority of animals? The brainpower is spent on building a complicated internal state of the world, and it can only focus on so much at a time anyway. Bio-inspired robotics is a thing for a reason - we learn a lot of subtle lessons from nature that are not intuitively obvious. This could easily be one of them.