[pja]

My understanding is that RADAR systems can detect stationary objects just fine: What's going on is that in-car radar systems can't differentiate between, say, a metal sign on a gantry or at the side of the road and a parked car on the road, because their spatial resolution is poor to non-existent. Hence in-car RADAR systems (often) ignore radar returns that are non-moving relative to the road surface. Otherwise the car would panic brake every time you went past a road sign.

The effect of this is that current in car RADAR systems are great at avoiding collisions with vehicles that suddenly brake to a halt in front of you whilst at the same time will happily let you drive a full speed straight into the back of a parked car.

This is why I believe current self driving vehicles (apart from Tesla) are all using LIDAR for object detection.

(The above is my interpretation of my reading on the current state of the art. If anyone knows more detail, please correct me.)

[URSpider94]

The problem for all self-driving car systems is modeling the road. Let’s say you are climbing a gentle hill, and there’s an overpass ahead. The nose of the car is aiming up, so it will see the overpass dead ahead and not moving. This looks the same to the car as driving down a flat road with a semi perpendicular to the travel lanes and dead stopped. This is true for all imaging systems - RADAR, LIDAR and camera.

Same if you are about to enter a right-hand sweeper, pedestrians standing on the sidewalk in front of you would look as if they are directly in your path.

Musk mentioned this in one of his blog posts. Tesla is attempting to build a database of obstacles like this and geotag them, so that the car can filter them out.