Let's suppose you are given a 2d field in which there are a number of rectangles, each of which have a velocity vector. So you know the ___location of each rectangle now, and, to a good approximation, in the near future.
How difficult is it to program a path-finding algorithm that will not crash into any of these rectangles, given that lowering speed is always a good option within a city?
I'd say this is really not all that difficult. Yes, there are a lot of special cases to consider, but still, in essence, this seems to me a quite simple problem.
So breaking down the problem is key. If you look at an autonomous vehicle as a black box, then, of course, it will seem dauntingly complicated, and difficult to trust.
IIRC, the autonomous cars all use a LIDAR system (kind of radar) to detect objects, and do not fully rely on computer vision.
Also, here's ([1]) an article about a student who (single-handedly!) wrote the software for a self-driving car, which doesn't use a LIDAR system and is much cheaper.
Let's suppose you are given a 2d field in which there are a number of rectangles, each of which have a velocity vector. So you know the ___location of each rectangle now, and, to a good approximation, in the near future.
How difficult is it to program a path-finding algorithm that will not crash into any of these rectangles, given that lowering speed is always a good option within a city?
I'd say this is really not all that difficult. Yes, there are a lot of special cases to consider, but still, in essence, this seems to me a quite simple problem.
So breaking down the problem is key. If you look at an autonomous vehicle as a black box, then, of course, it will seem dauntingly complicated, and difficult to trust.