POSITIONING IN REAL-TIME PUBLIC TRANSPORT NAVIGATION

Transcription

1 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)

2 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

3 Motivation EU-Project SMART-WAY: Problem Airport Dresden?? Fraunhofer IVI, Zeunerstrasse 38

4 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

5 Motivation Urban Canyons, Tunnels and Shielded Vehicles Tunnel, shielded vehicle source: Urban Canyons source: Metro source:

6 Motivation Smartphone Sensors Source:

7 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

8 From Rawdata to Signal Features of Motion States General Data collection Data preproccessing Feature generation Feature extraction Signal classification

9 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

10 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

11 From Rawdata to Signal Features of Motion States Signal classification statistical approaches probabilistic approaches geometric approaches decision trees hidden Markov models artificial neural networks

12 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

13 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 Hz: vibration of lateral acceleration Hz Higher modes of vehicle sub elements In-Vehicle Frequency Ranges for Road vehicles, adapted from [Harrison 2004]

14 Recognizing Vehicle Signals in Smartphone Data Measurement Tram Dresden (TU Dresden) x 4 x 3 x 2 x 1 photo: Klaus Habermann

15 Recognizing Vehicle Signals in Smartphone Data Using Smartphones as Vehicle Acceleration Sensors Motorola DEFY HTC Wildfire

16 Recognizing Vehicle Signals in Smartphone Data Time Patterns in Tram and Smartphone Data (I): Stops uncalibrated MT accelerations in m/s x MT y MT z MT uncalibrated SP accelerations in m/s 2 SP orientation does not equal MT orientation x SP y SP z SP measurement index (200 Hz) time measurement index (interpolated, 200 Hz) time

17 Recognizing Vehicle Signals in Smartphone Data Time Patterns in Tram and Smartphone Data (II): Bends Time: Smartphone 1 Time: Measurement tram Time: Smartphone 2

18 Recognizing Vehicle Signals in Smartphone Data Time Patterns in Tram and Smartphone Data (III): Switches Time: Smartphone 1 Time: Measurement tram Time: Smartphone 2

19 Recognizing Vehicle Signals in Smartphone Data Frequency Analysis in Tram and Smartphone Data (FFT) stops

20 Recognizing Vehicle Signals in Smartphone Data Frequency Analysis in Tram and Smartphone Data (FFT) stops

21 Recognizing Vehicle Signals in Smartphone Data Frequency Analysis in Tram and Smartphone Data (STFT) drive stop drive stop drive Time (s) stop drive stop Time (s) drive stop Frequency (Hz) for x, y, z-axis Measurement tram data Frequency (Hz) for x-axis Smartphone 1, x-axis

22 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

23 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

24 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)