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Estimating Oncoming Vehicle State via Stereo Vision

This research focuses on estimating driving states of oncoming vehicles using stereo vision from a moving platform. The proposed method integrates object model, tracking techniques, and Kalman filtering for dynamic object detection and safety systems. The study presents experimental results and concludes with the feasibility and challenges of the approach.

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Estimating Oncoming Vehicle State via Stereo Vision

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  1. Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo VisionIEEE Intelligent Transportation Systems 2009 Alexander Barth and Uwe Franke M.S. Student, Heejong Hong 07. 14. 2014

  2. Outline • Introduction • RelatedWorks • Proposed Method • ExperimentalResults • Conclusion

  3. Introduction • Driver-assistance and safety systems Dynamic Object Detection for DAS Safety System with Dynamic Path Estimation http://www.6d-vision.com/home/bedeutung

  4. Related Works • A model-free object representation based on groups • Fusion active sensors • Track-before-detection • Rediscovering an image region labeled as vehicle D. Beymer , P. McLauchlan , B. Coifman and J. Malik  "A real-time computer vision system for measuring traffic parameters",  Proc. Comput. Vis. Pattern Recog,  pp.495 -501 1997  M. Maehlisch , W. Ritter and K. Dietmayer  "De-cluttering with integrated probabilistic data association for multisensormultitarget ACC vehicle tracking",  Proc. IEEE Intell. Veh. Symp.,  pp.178 -183 2007  U. Franke , C. Rabe , H. Badino and S. Gehrig  "6D-vision: Fusion of stereo and motion for robust environment perception",  Proc. 27th DAGM Symp.,  pp.216 -223 2005  X. Li , X. Yao , Y. Murphey , R. Karlsen and G. Gerhart  "A real-time vehicle detection and tracking system in outdoor traffic scenes",  Proc. 17th Int. Conf. Pattern Recog.,  pp.II:761 -II:764 2004  1. 2. 3.

  5. Proposed Method

  6. Object Model • Pose (relative orientation and translation to ego-vehicle) • Motion State (velocity, acceleration, yaw rate) • Shape (rigid 3-D point cloud) Pose Motion State Shape

  7. Object Tracking • Extended Kalman Filter (EKF): Kalman filter for nonlinear model Example) State transition(f) and observation model(h) Discrete-time predict and update equations Jacobian of system & measurement model Wikipedia : http://en.wikipedia.org/wiki/Extended_Kalman_filter

  8. Object Tracking 1. State Vector of an object instance Reference point in ego-coordinates Rotation point in object-coordinates The object origin is ideally defined on the center rear axis

  9. Object Tracking 2. Dynamic(System) Model Predicted state vector Time-discrete system Equation Transformation of an object point Translation matrix Ris 3x3 rotation matrix around the height axis N. Kaempchen , K. Weiss , M. Schaefer and K. Dietmayer  "IMM object tracking for high dynamic driving maneuvers",  Proc. IEEE Intell. Veh. Symp.,  pp.825 -830 2004 

  10. Object Tracking 3. Measurement Model Objects feature points on image coordinates using feature tracker (KLT) Feature point tracking using KLT The measurement nonlinear eq. : perspective camera model Jacobian of measurement model

  11. Kalman Filter Initialization • Image Based Initialization • Radar-Based Initialization(detect oncoming vehicle up to 200m) The centroid of the 3-D positions : The mean velocity vector : Initial Yaw : The lateral and longitudinal positions of the radar target : , Absolute radar velocity of the object : Initial Yaw :

  12. Point Model Update t • Maximum-likelihood estimation • Simple average filter Object’s Shape Expectation = 3x3 covariance matrix of Expected object’s shape

  13. Experimental Result

  14. Simulation Results • Synthetic Sequence

  15. Real World Results • Country Road Curve I

  16. Real World Results • Country Road Curve II

  17. Real World Results • Oncoming Traffic at Intersections

  18. Real World Results • Leading Vehicles & Partial Occlusions

  19. Real World Results • Challenges and Limits

  20. Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013 Conclusion

  21. Joint Histogram Based Cost Aggregation for Stereo Matching - TPAMI 2013 Conclusion • Contribution • New method for the image-based real-time tracking (25Hz, 640x480) • Results of experiments with synthetic data & real-world • Two different object detection method (image & radar) • Feature-based object point model does not require a priori knowledge about the object’s shape • Weakness • No specific system block diagram • User defined rotation point • Shape depends on outlier removing algorithm (ex : max distance parameter) • Shape is very sensitive about outlier of point cloud(because of yaw)

  22. Thank you!

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