© 2005 ritsumeikan univ. all rights reserved. context aware operation reproduction for safety...
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© 2005 Ritsumeikan Univ. All Rights Reserved.
Context Aware Operation Reproduction for Safety Driving
Satoshi Kaede
Ritsumeikan University
Graduate School of Computer Science
Date Engineering Laboratory JapanE-mail [email protected]
© 2005 Ritsumeikan Univ. All Rights Reserved.
Contents
1. Goal of Our Research
2. Describing Model of a Context
3. Verification of the Model
4. Conclusion and Future works
© 2005 Ritsumeikan Univ. All Rights Reserved.
Goal of Our Research
Set a Steering Lock !
Get Your Valuables !
We propose a method to reproduce operations from contexts of the driver and someone on a vehicle.
© 2005 Ritsumeikan Univ. All Rights Reserved.
The method of representing human behavior
The human behavior is consisted by individual act.
Starting to drive a vehicleOpen the door
Get a key case
Turn the key in the ignition
Unlock the side brake
Push the accelerator down
© 2005 Ritsumeikan Univ. All Rights Reserved.
Bayesian Network
The Bayesian Network It models dependency relation using probability
networks. The structure of the Network is Directed Acyclic Graph.
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Behavioral Scene Characteristic
S1: A set of objects which are accessed when a user is taking a particular behavior.
S2: A set of signals from ubiquitous environment when a user is taking the behavior.
S3: A set of accessed objects and a set of signals from ubiquitous environment when a user is taking behaviors other than the one.
S3
S1
S2
Behavioral Scene Characteristic
© 2005 Ritsumeikan Univ. All Rights Reserved.
Bayesian Network
The Bayesian Network It models dependency relation using probability
networks. The structure of the Network is Directed Acyclic Graph.
K2 Algorithm It automatically configures Bayesian Network by
statistical data. It creates a dual directional arrow which represents
dependency relationship between nodes. The allow interferes with configuration of DAG.
© 2005 Ritsumeikan Univ. All Rights Reserved.
Result
Switch to open a Gas TankLock
Cap of Gas Tank
Steering Lock
Side Brake
Switch of Automatic window
KeyKnob
The Bayesian Network of Human Behavior
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HeuristicsHeuristic 1
An aim node is excluded from the set of candidate nodes which have a possibility becoming the parent node of all another nodes.
Heuristic 2
2.1 To cut the arrow that does not influence the aim node.
2.2 To cut the arrow using semantics which the nodes have.
© 2005 Ritsumeikan Univ. All Rights Reserved.
Result
Switch to open a Gas TankLock
Cap of Gas Tank
Steering Lock
Side Brake
*1
*2
Switch of Automatic window
KeyKnob
The Bayesian Network of Human Behavior
© 2005 Ritsumeikan Univ. All Rights Reserved.
Describing Model of Human BehaviorHeuristic 1
An aim node is excluded from the set of candidate nodes which have a possibility becoming the parent node of all another nodes.
Heuristic 22.1 To cut the arrow that does not influence the aim node.2.2 To cut the arrow using semantics which the nodes have.
Heuristic 3To sort the configured BNs which Heuristic 1, 2.1 and
2.2 are applied in ascending order using true cases and false cases.
© 2005 Ritsumeikan Univ. All Rights Reserved.
RFID tag
Access Log
Accesses to RFID Tag
Experiment for Verification of Model
Database ServerPDA with RFID reader
Scenario : If a user leaves valuables in a vehicle when he leaves from vehicle for a long time, a system notifies him that valuables may be stolen by ruining on the vehicle.
© 2005 Ritsumeikan Univ. All Rights Reserved.
True CasesFalse Cases
dp1
dp2
Pro
babi
lity
of
leav
ing
from
veh
icle
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35Case
Analysis of experimental result
© 2005 Ritsumeikan Univ. All Rights Reserved.
Conclusion and Future works
We proposed the heuristics to configure behavioral scene characteristic from the context using Bayesian Network and the K2 Algorithm.
Dual directional arrows are cut to configure candidate set of Bayesian networks by using proposed heuristics.
We will get more experimental logs for verification model.
© 2005 Ritsumeikan Univ. All Rights Reserved.
The Layer of Inferring Method
The first stage: The BSC created from user contexts is checked with an access log and signals from ubiquitous environment. The check picks up behavior which may be occurring.
The second stage: The behaviors which are picked up at the first stage are scrutinized as for the sequence of accesses and durations of accesses in access log, to determine whether the behaviors are really taken.