Collecting Vehicle Trajectory Information by Smartphones when GPS Signal is Lost

Transcription

1 Collecting Vehicle Trajectory Information by Smartphones when GPS Signal is Lost Mecit Cetin, PhD Department of Civil and Environmental Engineering Tamer Nadeem, PhD Computer Science Ilyas Ustun, Abdulla Alasaadi, Olcay Sahin, Matthew Orensky

2 Introduction Smartphones market share As of Dec 2013, 65% have it Smartphones equipped with various sensors GPS, accelerometer, gyroscope, compass, proximity sensor, and ambient light sensor, etc. Sensor data used in: Activity classification (sitting, walking, running, biking) Travel mode identification (car, bus, light rail, riding a bike, walking) 2

3 Objective Without using GPS detect whether the vehicle is in motion or stopped estimate speed Why avoid GPS? Low accuracy of GPS in urban areas with tall buildings, tunnels, covered areas, Low precision of GPS localization, High-power consumption when the GPS is in use 3

4 Project Description Funded by TranLIVE UTC (University Transportation Center) Develop an app to estimate fuel consumption and CO 2 emissions for a multi-modal trip Provide feedback to the driver 4

5 Potential Applications Traffic monitoring Signal timing optimization Fuel consumption and emission predictions Estimating vehicle trajectories while in a tunnel 5

6 Data Collection within Tunnels 6

7 Data Collection System 7

8 Data Collection Android App Acceleration, Gyroscope, Magnetometer sensor data GPS latitude, longitude, and speed OBD speed (if available) GPS and OBD speeds are used for model development and testing 8

9 GoGreen App (in Development) 9

10 Database View 10

11 Collected Data 11

12 Three-axis Accelerometer Data 12

13 Raw Acceleration Data in Y and Z 13

14 Std Deviation of Y and Z 14

15 Range Range Calculated over 1-sec Data Orientation invariant data 2.5 Driver 16_ _Trip 1 dodge caliber, LGE Nexus 5 Motion percentage, Overlap: 0.5, Window: 1 secs, Motion Standstill Total Acceleration 15

16 Hidden Markov Model The X s are hidden states (vehicle being stationary or in motion). The Y s are visible observations (acceleration data). 16

17 Speed (mph) Sample Data Toyota Prius Driver 2_ _Trip 1, GPS speed used, Overlap: 0.5, Window: 1 secs, toyota prius, samsung GT-N7000 Motion percentage 0.74, HMM trained at and the emission vector created at: Range: 0.5 stop point move point Threshold X squared std dev mean range max gps speed threshold pred HMM pred :00 02:04 04:08 06:12 08:16 10:20 12:24 14:28 16:32 18:36 20:41 22:45 index 17

18 Speed (mph) Sample Data - Dodge Caliber Driver 16_ _Trip 1, GPS speed used, Overlap: 0.5, Window: 1 secs, dodge caliber, LGE Nexus 5 Motion percentage 0.59, HMM trained at and the emission vector created at: Range: 0.6 stop point move point Threshold X squared std dev mean range max gps speed threshold pred HMM pred :00 01:05 02:11 03:17 04:23 05:29 06:34 07:40 08:46 09:52 10:58 12:03 index 18

19 Likely states Speed (mph) 60 Detecting Motion and Stops Acura RL Speed Likely states Observation Actual stop point Actual move point :00 02:15 04:31 06:47 09:02 11:18 13:34 15:49 18:05 20:21 22:37 24:52 Time (mm:ss) Device: Samsung Note I Vehicle: Acura RL

20 Likely states Speed (mph) Detecting Motion and Stops Toyota Prius Speed Likely states Observation Actual stop point Actual move point :00 02:04 04:08 06:12 08:17 10:21 12:25 14:30 16:34 18:38 20:43 22:47 Time (mm:ss) Device: Samsung Note I Vehicle: Toyota Prius

21 Likely states Speed (mph) Detecting Motion and Stops Dodge Caliber 80 Speed Likely states Observation Actual stop point Actual move point :00 01:06 02:12 03:18 04:24 05:30 06:36 07:42 08:48 09:54 11:00 12:06 Time (mm:ss) Device: LGE Nexus 5 Vehicle: Dodge Caliber

22 Mean Acceleration (m/s 2 ) Speed Estimation Accelerometer data Noisy and biased Bias varies from phone to phone Mean Acceleration Readings ( over 11s) z -z +y -y +x -x Orientation (axis facing up / opposing gravity) 22

23 Speed (mph) Speed Estimation Before Calibration File: ALLdata Resample freq: 10 Hz Est speed OBD speed GPS speed :00 02:26 04:53 07:19 09:46 12:13 14:39 17:06 19:32 21:59 24:26 26:52 Time (Min) 23

24 Speed (mph) Speed Estimation After Calibration File: ALLdata Resample freq: 10 Hz Est speed OBD speed GPS speed :00 02:26 04:53 07:19 09:46 12:13 14:39 17:06 19:32 21:59 24:26 26:52 Time (Min) 24

25 00: : : : : : : : : : : : : : : : : : : :29.2 Speed (mph) Speed Estimation Using PCA GPS Speed PCA Speed Time 25

26 Conclusions Developed an App and DB to collect high resolution data from smartphones The number of stops and their times can be estimated with relatively good accuracy Speed estimation with calibration is feasible for obtaining acceptable results Continue the research to develop more robust and accurate models 26