© 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 Japan E-mail [email protected]

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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.

© 2005 Ritsumeikan Univ. All Rights Reserved.

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

© 2005 Ritsumeikan Univ. All Rights Reserved.

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.

Thank you for listening.

Thank you very much.

© 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.

© 2005 Ritsumeikan Univ. All Rights Reserved.

Tagged World Project

Kitchen

Bed Bathroom

TVPC

Table

closet

RFID Reader

Access Log

Access of RFID Tag

VestibuleRFID Tags