4 ms·
Upon rereading, it seems to me that they're mostly looking to do this type of increased accuracy when an Uber is parked somewhere waiting for their rider, and y
by nickysielicki 9y ago
Upon rereading, it seems to me that they're mostly looking to do this type of increased accuracy when an Uber is parked somewhere waiting for their rider, and you want to be able to show the rider exactly where he is parked, rather than something that is meant to be used while in motion or anything like that.
When you limit the scope like that, I think most of my concerns go away. Very cool work here.
- woolvalley 9y agoAlso applies in figuring out the spot to pick up the rider, most people don't move much when requesting a car.
- adrianmonk 9y agoYour concerns about bad data from dodgy hardware seem real, but it seems like their system is meant to be used while in motion. From the article: "We therefore use past measurements and constrain the location evolution over time using a motion model adapted to the application (e.g., pedestrian vs. vehicular motion)." … "Over time, the probability weights and particle locations evolve based on the measurements and the motion model." Presumably this means something along the lines of throwing out hypothesized locations based on impossible movements. If they were very sure that a car was at a particular location 5 seconds ago and now it is at another location 100 meters away, then that's believable because cars can travel at that speed (~40 mph or ~65 km/h). But if dealing with a pedestrian, it indicates low probability of the latest measurement being correct. I'm just guessing on the specifics of what the motion model does. But it seems like they're trying to use history in more cases than just when someone is sitting still.