Sage Journals: Discover world-class research

Abstract

An image sensing technique is developed using image plane analysis to measure the vehicular motion variables through an in-vehicle camera. A new measurement model is derived to directly connect the image features of vanishing point and base point with the desired motion variables comprising heading angle, lateral deviation, yaw rate and sideslip angle of the vehicle. These are useful in vehicular steering control applications such as automatic and four-wheel steering systems for providing feedback motion information in a convenient way. In order to test the proposed vision-based measuring scheme, a computerized road scene is simulated as the test sample using a newly proposed model for curvature-based road generation. Finally, experimental works are performed using the real road scene to verify the image sensing method. Consistent results are obtained by comparing with other measurements from a yaw rate gyro and from vehicular traces left on the road.

Keywords

vehicular motion measurement image sensing image plane analysis road scene generation

Get full access to this article

View all access options for this article.

References

Peng

Tomizuka

Preview control for vehicle lateral guidance in highway automation. Trans. ASME, J. Dynamic Systems, Measurement and Control, December 1993, 115, 679–686.

Shladover

S. E.

Wormley

D. N.

Richardson

H. H.

Fish

Steering controller design for automated guideway transit vehicles. Trans. ASME, J. Dynamic Systems, Measurement and Control, March 1978, 100, 1–8.

Fenton

R. E.

Melocik

G. C.

Olson

K. W.

On the steering automated vehicle: Theory and experiment. IEEE Trans, on Automatic Control, 1976, 21(3), 306–315.

Furukawa

Yuhara

Sano

Takeda

Matsushita

A review of four-wheel steering studies from the viewpoint of vehicle dynamics and control. Vehicle System Dynamics, 1989, 18, 151–186.

Yeh

E. C.

R. H.

Closed-loop design for decoupling control of a four-wheel-steering vehicle. Int. J. Vehicle Design, 1989, 10(6), 703–727.

Lappe

Rauschecker

J. P.

A neural network for the processing of optic flow from ego-motion and higher mammals. Neural Computation, 1993, 5(3), 374–391.

Heel

Direct dynamic motion vision. IEEE International Conference on Robotics and Automation, California, 1990, pp. 1142–1147.

Shim

J. S.

Heo

S. J.

Yoon

Y. S.

Neural network representation of visual-motor coordination at vehicle driving situation. Proceedings of the International Symposium on Advanced Vehicle Control, Yokohama, Japan, 1992, 493–498.

Thorpe

Hebert

Kanade

Shafer

The new generation system for the CMU Navlab. In Vision-based Vehicle Guidance (Ed. Masaki

), 1991, pp. 30–82 (Springer-Verlag, New York).

10.

Dickmanns

E. D.

Graefe

Application of dynamic monocular machine vision. Machine Vision Applic., 1988, 1, 241–261.

11.

Dickmanns

E. D.

Mysliwetz

B. D.

Recursive 3-D road and relative Ego-State recognition. IEEE Trans, on Pattern Analysis and Machine Intelligence 1992, 14(2), 199–213.

12.

Green

Olson

The development and use of the UMTRI driving simulator. Technical report in UMTRI-89-25, University of Michigan Transportation Research Institute, Ann Arbor, Michigan, USA, 1989.

13.

Yeh

E. C.

Hou

D. G.

Hsu

J. C.

Computer simulation analysis of automobiles in acrobatic sharp turning. Proceedings of the 17th National Conference on Theoretical and Applied Mechanics, Taipei, Taiwan, 1993, pp. 879–886.

Vision-based motion measurement by directly extracting image features for vehicular steering control

Abstract

Abstract

Keywords

Get full access to this article

References