Using Particle Image Velocimetry for Road Vehicle Tracking and Performance Monitoring. Samuel C. Kucera Jeremy S. Daily

Transcription

1 Using Particle Image Velocimetry for Road Vehicle Tracking and Performance Monitoring Samuel C. Kucera Jeremy S. Daily SAE World Congress Detroit, MI April 13, 2011 Outline Purpose Mathematics Mathematical Basis for Digital Particle Image Velocimetry Description of Planar Vehicle Kinematics Experimental Procedures Experimental Results Conclusions 1

2 Create a low-cost and reliable measurement system to determine planar kinematic motion of road vehicles using a consumer grade high speed camera. Introduction Purpose QuickTime and a YUV420 codec decompressor are needed to see this picture. 3 Why we need something Why better/different? we need something What s the best way to measure the performance of a U-turn? GPS works (e.g. VBOX) Expensive at high resolution IMU works (e.g. VC4000) Drift from integration of accelerometer signal when determining velocity and position External stationary tracking systems 4 2

3 5 Digital Image Analysis Techniques Two fields: Digital image correlation (DIC) (Sutton, et al., 1986) PIV/DPIV (Willert, 1991) DIC has been around longer (Bailey, 1976) Many techniques for avoiding error are developed in DIC DPIV more open (Fincham, 1997) Less commercialized than DIC 6 3

4 Introduction Background of DPIV Digital particle image velocimetry Analyzes digital images to ascertain displacement Knowing the time duration between images allows the determination of velocity 7 Introduction Background (Application of DPIV) Asphalt Ground provides a random pattern High speed videography provides images Snow 8 4

5 Analytical Development Mathematical Basis for DPIV Description of Vehicle Kinematics 9 Mathematical Basis for DPIV The Cross-Correlation Peak shifts the number of displaced pixels (m, M N M N Determine these values 10 5

6 Mathematical Basis for DPIV (cont.) The displacement of the camera has been determined. How does that relate to the motion of the vehicle itself? Pixels Pixels 11 Vehicle Velocity from DPIV DPIV Known Geometry/ Triangulation Calibration 12 6

7 Vehicle Kinematics Assume planar motions only Origin of reference frame set at lower-left corner of initial image 13 Vehicle Kinematics (cont.) Setting the reference point (RP) to the lower left corner simplifies the algorithm for keeping track of the velocity vector components. Least Squares Analysis Triangulation referencing tires Triangulate reference point 14 7

8 Vehicle Kinematics (cont.) Observed from DPIV Geometrically Driven 15 Unknown Kinematics Residuals Vehicle Kinematics The Pseudo-Inverse This equation implements the pseudo-inverse to solve the previous system of equations in a least-squares sense. Since the DPIV cross-correlation algorithm contains no means of directly detecting angular velocity, this method provides a measure of angular velocity, assuming a good fit. 16 8

9 Experimental Procedures Apparatus 17 Experimental Procedures Data acquisition Data is acquired as a video of the ground Video is converted into an image sequence 18 9

10 Experimental Procedures Data Processing 19 Signal-to-noise filtering 20 10

11 Performance Study S/N # Failures % Displacement 21 Maximum Velocity for DPIV 22 11

12 Data Analysis Initial filtering in DPIV software Only looks at signal-to-noise ratio Post-processing residual analysis 23 Data Analysis How well do the data fit the kinematic model? Beta values are known from the least squares technique Calculate residuals Filtering is based upon the coefficient of determination

13 Example of Data Fit Quality In-Plane Motion High R 2 Out-of-Plane Motion Low R 2 Experimental Results 26 13

14 Analysis of Preceding Experimental Results DPIV VC4000 (Accel.) VC400 0 (GPS) Radar Acceleration (g) Deceleration (g) % difference 27 Experimental Results Low speed with VBOX Shadowed region 28 14

15 Conclusions DPIV with a consumer camera works for collecting information about vehicle kinematics Low speed tests with VBOX look promising Large uncertainty at high speeds Better camera should help Technique is sensitive to jostling and shadows 29 Acknowledgments Thesis Committee: Dr. Jeremy Daily, Dr. Scott Holmstrom, and Dr. Michael Keller Funding sources: Wilfred Woobank Graduate Assistantship NSF Disclaimer: References to brands are not intended to be endorsements or commercialization

16 Questions? 31 16