After watching this group for a while, thought it would be interesting to
bring up a topic I have been working on for several years and see if I can
get any help from the geowanking crowd.
Goal: Create highly accurate and complete digital maps of the
transportation network suitable for safety of life applications with
accuracy commensurate with future GNSS systems (decimeters). It seems to
me that this can only be done through a statistical, probe based, approach
since imagery and 'mobile mapping' approaches are error prone with low
revisit rates.
Problem: Given a very large set of vehicle PVT (position, velocity, time)
information,
1) derive the location of the centerline of every lane, along with lane
attributes such as direction and ability to cross to the adjacent lane,
2) derive the location of all turn restrictions and traffic controls, and
3) relate the PVT accuracy of the data to the accuracy of the resulting
'map' for different quantities of data.
For extra credit, identify movements within lanes that indicate a vehicle
intends to turn, stop, or execute some other maneuver. Of course, all of
these answers must come with a statistical accuracy metric.
Background: There are a lot of GPS units in a lot of cars collecting a lot
of data on where the cars (roads) are and how they move (controls such as
yields and stops). This data is then thrown away. If this data can be
captured (and there are efforts underway to do this), how does one build a
map of the roads and all of the signs and signals that control the motion
of vehicles? I believe that the entire infrastructure that influences the
behavior of vehicles is captured in this data, and that, by the central
limit theorem, the data has ever increasing (and quantifiable!) accuracy.
This is exactly what is needed for map based transportation safety
systems currently under development. This is one very promising way to
address the 40,000+ fatalities/ $200B a year caused by accidents on US
roads.
We spent a couple of years looking at this and devised a k-means approach
bundling data across the direction of travel to pull out the lanes. The
data could then be grouped by lanes to derive centerlines. Stop signs and
traffic lights were easy, we never got to yields or speed limits. Our
approach was successful, but computationally intensive, and required that
one work with the entire data set rather than a Kalman filter approach
where data can be incrementally added to improve the solutions validity
(or indicate that the world has changed). We also did not get far on the
accuracy metrics. The key to this problem seems to be grouping vehicles
into like groups going from 'A' to 'B', where 'A' and 'B' are any two
arbitrary points on the road network with an accuracy of around 30 cm. We
can 'generally' assume that a vehicle is within 30cm of the 'lane center'.
One problem, of course, is that the accuracy of any individual vehicle's
position is generally somewhat larger than the lane width.
Does anyone know anybody working this (or similar) problems?
Any ideas on how to approach this from the geo-statistical crowd out
there? We came at this from an AI perspective, and I think a
geo-statistical approach might have gone a different direction.
Other thoughts?
-=Chris
PS- This approach is really promising for getting public, low cost,
accurate maps of transportation networks, and yes, there are some serious
privacy issues to work through. There will never be unique identifiers in
the data, and we can cut out the first and last mile.
[EMAIL PROTECTED]
650/845-2579
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