A Machine Vision Based System for Guiding Lane-change Maneuvers

In this report, the authors propose a new approach for vision based longitudinal and lateral vehicle control which makes use of binocular stereopsis. Novel aspects include (a) exploitation of domain constraints to simplify and make robust the search problem in finding binocular correspondences (b) dealing with crowded traffic scenes where substantial segments of the lane boundaries may be occluded. The vision system is designed to interface in a modular fashion with the use of non-visual sensors such as magnetic sensors for lateral position measurement and active range sensors. These are employed for an integrated approach to vehicle control such as that being investigated in the California PATH project.

[1]  Matthew Turk,et al.  VITS-A Vision System for Autonomous Land Vehicle Navigation , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Bill Ross,et al.  A practical stereo vision system , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Surender K Kenue,et al.  Lanelok: Detection Of Lane Boundaries And Vehicle Tracking Using Image-Processing Techniques - Part I: Hough-Transform, Region-Tracing And Correlation Algorithms , 1989, Other Conferences.

[4]  Jitendra Malik,et al.  Computational framework for determining stereo correspondence from a set of linear spatial filters , 1992, Image Vis. Comput..

[5]  Jitendra Malik,et al.  Robust computation of optical flow in a multi-scale differential framework , 1993, 1993 (4th) International Conference on Computer Vision.

[6]  O. D. Altan,et al.  Computer architecture and implementation of vision-based real-time lane sensing , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[7]  Larry H. Matthies,et al.  Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Charles E. Thorpe,et al.  Representation and recovery of road geometry in YARF , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[10]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[11]  Daniel Raviv,et al.  A new approach to vision and control for road following , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[12]  J. Crisman.  Color vision for the detection of unstructured road and intersections , 1990 .

[13]  B. Ulmer.  VITA-an autonomous road vehicle (ARV) for collision avoidance in traffic , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[14]  Wilfried Enkelmann,et al.  Obstacle detection by evaluation of optical flow fields from image sequences , 1990, Image Vis. Comput..

[15]  Stephen A. Billings,et al.  SWITCHER: a stereo algorithm for ground plane obstacle detection , 1990, Image Vis. Comput..

[16]  Jan-Olof Eklundh,et al.  Object detection using model based prediction and motion parallax , 1990, ECCV.

[17]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[18]  Larry H. Matthies,et al.  Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation , 1991, Optics & Photonics.

[19]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  Rachid Deriche,et al.  Information contained in the motion field of lines and the cooperation between motion and stereo , 1990, Int. J. Imaging Syst. Technol..

[21]  Martial Hebert,et al.  Vision and navigation for the Carnegie-Mellon Navlab , 1988 .

[22]  Rachid Deriche,et al.  Recovering 3D motion and structure from stereo and 2D token tracking cooperation , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[23]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[24]  Olivier D. Faugeras,et al.  Determining the fundamental matrix with planes: instability and new algorithms , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Dean A. Pomerleau,et al.  Progress in neural network-based vision for autonomous robot driving , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[26]  I. Masaki,et al.  Vision-based vehicle guidance , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[27]  Wilfried Enkelmann,et al.  Obstacle Detecion by Evaluation of Optical Flow Fields from Image Sequences , 1990, ECCV.

[28]  S. Chandrashekhar,et al.  Temporal analysis of stereo image sequences of traffic scenes , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[29]  Narendra Ahuja,et al.  Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  W. von Seelen,et al.  Vision-based car-following: detection, tracking, and identification , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[31]  Monique Thonnat,et al.  A Pyramidal Stereovision Algorithm Based On Contour Chain Points , 1990, ECCV.

[32]  Allen M. Waxman,et al.  Closed-form solutions to image flow equations for 3D structure and motion , 1988, International Journal of Computer Vision.

[33]  Olivier D. Faugeras,et al.  Relative 3D positioning and 3D convex hull computation from a weakly calibrated stereo pair , 1993, 1993 (4th) International Conference on Computer Vision.

[34]  Roger Y. Tsai,et al.  Techniques for Calibration of the Scale Factor and Image Center for High Accuracy 3-D Machine Vision Metrology , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  A. Meygret,et al.  Segmentation of optical flow and 3D data for the interpretation of mobile objects , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[36]  Andrew Blake,et al.  Affine-invariant contour tracking with automatic control of spatiotemporal scale , 1993, 1993 (4th) International Conference on Computer Vision.