opportunity knocks: a community navigation aid henry kautz don patterson dieter fox lin liao...
TRANSCRIPT
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Opportunity Knocks:A Community Navigation
Aid
Opportunity Knocks:A Community Navigation
Aid
Henry KautzDon Patterson
Dieter FoxLin Liao
University of WashingtonComputer Science & Engineering
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OutlineOutline
1. Need for navigation aids2. Limitations of current devices3. Advancing the State of the Art4. Prototype: Opportunity Knocks5. Future plans
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The Need: Community Access for the Cognitively
Disabled
The Need: Community Access for the Cognitively
Disabled
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Problems in Using Public Transportation
Problems in Using Public Transportation
•Learning bus routes and numbers
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Problems in Using Public Transportation
Problems in Using Public Transportation
•Learning bus routes and numbers
•Transfers, complex plans
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Problems in Using Public Transportation
Problems in Using Public Transportation
•Learning bus routes and numbers
•Transfers, complex plans
•Recovering from mistakes
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ResultResult
•Need for extensive life-coaching
•Need for point-to-bus service
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ResultResult
•Need for extensive life-coaching
•Need point-to-bus service
•Isolation
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Current GPS Navigation Devices
Current GPS Navigation Devices
Designed for drivers, not bus riders!Should I get on this bus?Is my stop next?What do I do if I miss my stop?
Requires extensive user inputKeying in street addresses no fun!
Device decides which route is “best”
Familiar route better than shorter one
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VisionVisionCan we build a system that...
Automatically learns the daily pattern of the user’s transportation plans – no typing!Provides proactive help in carrying out the plansHelps user recover from mistakesIs compatible with today’s transportation infrastructure
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ApproachApproach
User carries GPS cell phoneSystem infers bus use from
Position (near bus stop?)Velocity (on foot? in a vehicle?)Bus route information
Over time system learns about user
Important places Common transportation plans
Mismatches = possible mistakes
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Inferring GoalsInferring Goals
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Transportation Plans Transportation Plans
BA
Goalswork, home, friends, restaurant, doctor’s, ...
Trip segmentsHome to Bus stop A on FootBus stop A to Bus stop B on BusBus stop B to workplace on Foot
Work
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User ModelUser Model
System learns a probabilistic model of the user’s pattern of transportation useRobust even if...
Data is missingBehavior varies
User is predictable, but not rigid!
xk-1
zk-1 zk
xk
mk-1 mk
tk-1 tk
gk-1 gk
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Goal Prediction
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Error Detectio
n: Missed
Bus Stop
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Prototype: Opportunity KnocksGPS camera-phone“Knocks” when opportunity to help
Can I guide you to a likely destination?I think you made a mistake!This place seems important – would you photograph it?
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ExampleExample
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ExampleExample
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ExampleExample
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Example(continued
)
Example(continued
)
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Example(continued
)
Example(continued
)
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Example(continued
)
Example(continued
)
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StatusStatus
Proof of concept prototypeCan use machine learning to create a smarter, more useful personal navigation system for disabled persons
Basic scientific contributions on predicting human behavior
Best paper award at national computer science conference, AAAI
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Next StepsNext Steps
User interface design, testingAudioGraphicalAdaptive
Develop & deployHave begun discussions with METROSeek commercial partnerships
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ENDEND
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Probabilistic Model:Dynamic Bayesian
Network
Probabilistic Model:Dynamic Bayesian
Network
xk-1
zk-1 zk
xk
mk-1 mk Transportation mode
x=<Location, Velocity>
GPS reading
tk-1 tk
gk-1 gk Goal
Trip segment
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Error DetectionError Detection
Approach: model-selectionRun two trackers in parallel
Tracker 1: learned hierarchical modelTracker 2: untrained flat modelEstimate the likelihood of each tracker given the observations
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Novelty DetectionNovelty Detection