Positioning in Real-Time Public Transport Navigation

POSITIONING IN REAL-TIME PUBLIC
TRANSPORT NAVIGATION
DRESDEN
Comparison Of
Vehicle-based And Smart-phone Generated Acceleration Data
To Determine Motion States Of Passengers
Dipl.-Ing. Ina Partzsch (Fraunhofer IVI)
Dipl.-Ing. Gunther Dürrschmidt (TU Dresden)
Prof. Dr.-Ing. Oliver Michler (TU Dresden)
Dr.-Ing. Georg Förster (Fraunhofer IVI)
© Fraunhofer
Overview
 Motivation: Enhance positioning in difficult urban mobility scenarios
 Theory: From raw data to signal features of motion states
 Application: Recognizing vehicle data in smartphone data
 Outlook: Recognizing all motion states of passengers
© Fraunhofer
Motivation
EU-Project SMART-WAY: Problem
Airport Dresden
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?
Fraunhofer IVI, Zeunerstrasse 38
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Motivation
EU-Project SMART-WAY: Solution
Project Goals
 Proper navigation in public transport like a car navigation
considering current events and delays
 Continous guidance to a final destination
even with breaks by the user
 Application of Satellite Navigation Systems as a basis technology
plus ITCS and current smartphone-localisation standards
© Fraunhofer
Motivation
Urban Canyons, Tunnels and Shielded Vehicles
Tunnel, shielded vehicle
source: http://www.badische-seiten.de/freiburg/loretto-tunnel.php
Urban Canyons
source: http://autoelectronics.com/telematics/navigation_systems/GPS_2.jpg
Metro
source: http://kyynel.biz/photography/
© Fraunhofer
Motivation
Smartphone Sensors
Source: http://www.prlog.org/10792126/1
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Overview
 Motivation: Enhance positioning in difficult urban mobility scenarios
 Theory: From raw data to signal features of motion states
 Application: Recognizing vehicle data in smartphone data
 Outlook: Recognizing all motion states of passengers
© Fraunhofer
From Rawdata to Signal Features of Motion States
General
 Data collection
 Data preproccessing
 Feature generation
 Feature extraction
 Signal classification
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From Rawdata to Signal Features of Motion States
Data Preprocessing
 Determine sample rates of signals to be compared for a
defined period
 Interpolate smartphone signal to retrieve a time-equidistant
data set
 Re-sample the smartphone signal at the sample rate of the
vehicle measurement system
 Use a low-pass filter in order to avoid frequencies introduced
by the re-sampling process
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From Rawdata to Signal Features of Motion States
Feature Generation
 Time-Domain
 statistical metrics
 correlation metrics
 signal characteristics
 Frequency-Domain
 key coefficents
 energy
 entropy
 Cepstral-Domain
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From Rawdata to Signal Features of Motion States
Signal classification
 statistical approaches
 probabilistic approaches
 geometric approaches
 decision trees
 hidden Markov models
 artificial neural networks
© Fraunhofer
Overview
 Motivation: Enhance positioning in difficult urban mobility scenarios
 Theory: From raw data to signal features of motion states
 Application: Recognizing vehicle data in smartphone data
 Outlook: Recognizing all motion states of passengers
© Fraunhofer
Recognizing Vehicle Signals in Smartphone Data
Ground truth: Vehicle Signals
0-6 Hz Primary ride: wheel-surface contact, motion relative to the
surface
6-30 Hz Secondary ride: motion of sub elements of the vehicle,
thereof:
•
0-15 Hz: vibration of longitudinal acceleration
• 15-20 Hz: vibration of lateral acceleration
30-200 Hz Higher modes of vehicle sub elements
In-Vehicle Frequency Ranges for Road vehicles, adapted from [Harrison 2004]
© Fraunhofer
Recognizing Vehicle Signals in Smartphone Data
Measurement Tram Dresden (TU Dresden)
x
4
photo: Klaus Habermann
© Fraunhofer
x
3
x
2
x
1
Recognizing Vehicle Signals in Smartphone Data
Using Smartphones as Vehicle Acceleration Sensors
Motorola DEFY
HTC Wildfire
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Recognizing Vehicle Signals in Smartphone Data
Time Patterns in Tram and Smartphone Data (I): Stops
12
10
x MT
8
y MT
6
z MT
4
2
0
-2
-4
0
5000
4000
3000
2000
1000
measurement index (200 Hz)
time
© Fraunhofer
uncalibrated SP accelerations in m/s 2
SP orientation does not equal MT orientation
uncalibrated MT accelerations in m/s2
12
10
8
6
4
2
x SP
0
y SP
-2
z SP
-4
0
5000
4000
3000
2000
1000
measurement index (interpolated, 200 Hz)
time
Recognizing Vehicle Signals in Smartphone Data
Time Patterns in Tram and Smartphone Data (II): Bends
Time:
Smartphone 1
Time:
Measurement tram
Time:
Smartphone 2
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Recognizing Vehicle Signals in Smartphone Data
Time Patterns in Tram and Smartphone Data (III): Switches
Time:
Smartphone 1
Time:
Measurement tram
Time:
Smartphone 2
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Recognizing Vehicle Signals in Smartphone Data
Frequency Analysis in Tram and Smartphone Data (FFT)
stops
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Recognizing Vehicle Signals in Smartphone Data
Frequency Analysis in Tram and Smartphone Data (FFT)
stops
© Fraunhofer
Recognizing Vehicle Signals in Smartphone Data
Frequency Analysis in Tram and Smartphone Data (STFT)
drive
stop
drive
stop
Time (s)
stop
drive
stop
Time (s)
drive
drive
stop
Frequency (Hz) for x, y, z-axis
Measurement tram data
© Fraunhofer
Frequency (Hz) for x-axis
Smartphone 1, x-axis
Overview
 Motivation: Enhance positioning in difficult urban mobility scenarios
 Theory: From raw data to signal features of motion states
 Application: Recognizing vehicle data in smartphone data
 Outlook: Recognizing all motion states of passengers
© Fraunhofer
Summary and Outlook
 Smartphone sensors may deliver valuable data on vehicle movements
 Sensor data may be analyzed and classified in various ways in
time/frequency domain
 Next steps:
 Define signal features
 Reduce signal features to important ones
 Test classifiers
 Human movements on top of vehicle movements
© Fraunhofer
POSITIONING IN REAL-TIME PUBLIC
TRANSPORT NAVIGATION
DRESDEN
Comparison Of
Vehicle-based And Smart-phone Generated Acceleration Data
To Determine Motion States Of Passengers
Dipl.-Ing. Ina Partzsch (Fraunhofer IVI)
Dipl.-Ing. Gunther Dürrschmidt (TU Dresden)
Prof. Dr.-Ing. Oliver Michler (TU Dresden)
Dr.-Ing. Georg Förster (Fraunhofer IVI)
© Fraunhofer