Vision-based Vehicle Guidance

354 Pages·1992·19.437 MB·English

by  Volker Graefe, Klaus-Dieter Kuhnert (auth.), Ichiro Masaki (eds.)

SPRINGER SERIES
IN PERCEPTION ENGINEERING
Series Editor: Ramesh C. Jain
Springer Series
in Perception Engineering
PJ. Besl: Surfaces in Range Image Understanding
J.L.c. Sanz (ed.): Advances in Machine Vision
E.P. Krotkov: Active Computer Vision by Cooperative Focus and Stereo
R.C. Vogt: Automatic Generation of Morphological Set Recognition
Algorithms
T.J. Fan: Describing and Recognizing 3-D Objects Using Surface Properties
R.C. Jain and A.K. Jain (ed.): Analysis and Interpretation of Range Images
A.R. Rao: A Taxonomy for Texture Description and Identification
K.D. Skifstad: High-Speed Range Estimation Based on Intensity
Gradient Analysis
I. Masaki (ed.): Vision-based Vehicle Guidance
J.M. Gauch: Multiresolution Image Shape Description
Ichiro Masaki
Editor
Vision-based
Vehicle Guidance
With 236 Illustrations
Springer-Verlag
New York Berlin Heidelberg London Paris
Tokyo Hong Kong Barcelona Budapest
Ichiro Masaki
General Motors Research Laboratories
Computer Science Department
Warren, MI 48090-9055
USA
Series Editor
Ramesh C. Jain
Electrical Engineering and
Computer Science Department
University of Michigan
Ann Arbor, MI 48109
USA
Library of Congress Cataloging-in-Publication Data
Vision-based vehicle guidance / [edited by] Ichiro Masaki.
p. cm.-(Springer series in perception engineering)
Includes bibliographical references and index.
ISBN-13: 978-1-4612-7665-4 e-ISBN-13: 978-1-4612-2778-6
DOl: 10.1007/978-1-4612-2778-6
1. Automobile driving-Automation. 2. Mobile robots. 3. Computer
vision. I. Masaki, Ichiro. II. Series.
TL152.5.V55 1991
629.28'3-dc20 91-487
Printed on acid-free paper.
© 1992 Springer-Verlag New York, Inc.
Softcover reprint of the hardcover 1st edition 1992
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Production managed by Bill Imbornoni. Manufacturing supervised by Genieve Shaw.
Typeset by Asco Trade Typesetting Ltd., Hong Kong.
9 8 7 6 5 4 3 2 1
Preface
There is a growing social interest in developing vision-based vehicle guidance
systems for improving traffic safety and efficiency and the environment. Ex
amples of vision-based vehicle guidance systems include collision warning
systems, steering control systems for tracking painted lane marks, and speed
control systems for preventing rear-end collisions.
Like other guidance systems for aircraft and trains, these systems are ex
pected to increase traffic safety significantly. For example, safety improve
ments of aircraft landing processes after the introduction of automatic
guidance systems have been reported to be 100 times better than prior to
installment. Although the safety of human lives is beyond price, the cost for
automatic guidance could be compensated by decreased insurance costs. It is
becoming more important to increase traffic safety by decreasing the human
driver's load in our society, especially with an increasing population of senior
people who continue to drive.
The second potential social benefit is the improvement of traffic efficiency
by decreasing the spacing between vehicles without sacrificing safety. It is
reported, for example, that four times the efficiency is expected if the spacing
between cars is controlled automatically at 90 cm with a speed of 100 kmjh
compared to today's typical manual driving. Although there are a lot of tech
nical, psychological, and social issues to be solved before realizing the high
density jhigh-speed traffic systems described here, highly efficient highways
are becoming more important because of increasing traffic congestion.
Vision-based vehicle guidance systems are expected, as a third potential
benefit, to improve our environment. The time required to run the vehicle
engine to move a given distance would be decreased if the vehicle could get
to the destination in a shorter time because of higher traffic efficiency. The
shorter engine running time would contribute to cleaner air.
In addition to the social interests described, vision-based vehicle guidance
is an exciting field for technical research. Vision systems for vehicle guidance
must be able to deal with less structured environments, shorter processing
time, lower processing cost, and higher reliability, compared to conventional
machine vision systems for factory automation. Operating under various
v
vi Preface
weather changes, for example, is a great challenge for conventional vision
systems developed for controlled lighting conditions.
This book is based on the IEEE round-table discussion held on July 2,
1990. The round-table discussion on vision-based vehicle guidance was held
as an activity of the Intelligent Vehicle Subcommittee in the IEEE Industrial
Electronics Society. We decided to publish this book because there are no
books dedicated to the young, rapidly growing vision-based vehicle guidance
field. Please feel free to contact me for your comments, suggestions, or
questions.
Ichiro Masaki
Warren,MI
Acknowledgments
I would like to express my appreciation to all of the participants who contrib
uted to the discussions on July 2,1990, at the IEEE round-table discussion on
"Vision-based Vehicle Guidance." Special thanks are owed to the organizing
committee members listed here in alphabetic order.
J.M. Brady University of Oxford
R.A. Brooks Massachusetts Institute of Technology
P.J. Burt Stanford Research Institute
J.L. Crowly LIFIA
L.S. Davis University of Maryland
O.D. Faugeras INRIA
M. Fujie Hitachi
T. Fukuda University of Nagoya
V. Graefe Universitat der Bw Miinchen
F. Harashima University of Tokyo
H. Hosokai Science University of Tokyo
R. Jain University of Michigan
T. Kanade Carnegie-Mellon University
J.e. Latombe Stanford University
G.F. McClure Martin Marietta
Y. Shirai University of Osaka
T. Tomita MITI
S. Tsuji University of Osaka
T. Tsumura University of Osaka Prefecture
M. Yoshida Fujitsu
S. Yuta University of Tsukuba
I am indebted to G.G. Dodd and others at the General Motors Research
Laboratories for their encouragement and assistance. Financial support from
the Foundation for Promotion of Advanced Automation Technology con
tributed to a successful round-table discussion.
vii
Contents
Preface v
Acknowledgments vii
Contributors XVll
Color Plates for Chapter 7 following page 172
Vision-based Autonomous Road Vehicles
Volker Graefe and Klaus-Dieter Kuhnert
1.1 Abstract ................................................. .
1.2 Introduction .............................................. 1
1.3 Some Experimental Autonomous Road Vehicles ................ 2
1.3.1 Japan.............................................. 2
1.3.2 United States of America .............................. 3
1.3.3 United Kingdom .................................... 3
1.3.4 Germany ........................................... 3
1.4 Future Developments ...................................... 7
1.4.1 Camera Systems ..................................... 8
1.4.2 Hardware for Low-and Intermediate-Level
Real-Time Vision .................................... 8
1.4.3 Feature Extraction ................................... 9
1.4.4 Model Banks and Situation Recognition ................. 9
1.4.5 Behavioral Competence ............................... 10
1.4.6 Hardware and Software Concepts ...................... 10
1.5 Applications .............................................. 10
1.5.1 Warning and Monitoring ............................. 11
1.5.2 Intelligent Cruise Control ............................. 11
1.5.3 Fully Automatic Driving .............................. 11
1.6 Conclusions .............................................. 12
Appendix. Dynamic Vision Systems: The BVV Family ........... 12
A.l Concept of Dynamic Vision ............................. 13
A.2 Architecture for Dynamic Vision ........................ 14
A.3 Features of Dynamic Vision ............................ 15
A.4 The BVV 2 ........................................... 19
A.5 The BVV 3 ........................................... 22
A.6 Conclusions ......................................... 26
References .................................................... 26
IX
x Contents
2 The New Generation System for the eMU Navlab 30
Charles Thorpe, Martial Hebert, Takeo Kanade,
and Steven Shafer
2.1 Abstract ................................................. . 30
2.2 Introduction ............................................. . 30
2.2.1 Systems ........................................... . 32
2.2.2 Context ........................................... . 33
2.2.3 Evolution of Experimental Robots at CMU ............. . 33
2.3 Color Vision for Road Following ............................ . 35
2.3.1 SCARF ........................................... . 35
2.3.2 YARF ............................................ . 37
2.3.3 AL VINN .......................................... . 41
2.4 3-D Perception ........................................... . 43
2.4.1 Range Sensing ...................................... . 45
2.4.2 Discrete Objects and Obstacle Detection ................ . 46
2.4.3 Feature-based Terrain Modelling ...................... . 51
2.4.4 High-Resolution Terrain Models ...................... . 53
2.4.5 Discussion ......................................... . 55
2.5 Planning ................................................ . 56
2.6 Architectures ............................................ . 58
2.6.1 CODGER ......................................... . 58
2.6.2 EDDIE ........................................... . 60
2.6.3 Discussion ......................................... . 63
2.7 Maps and Missions ....................................... . 64
2.7.1 Related Work ...................................... . 65
2.7.2 Scenario ........................................... . 65
2.7.3 Tenets of Map Construction and Use ................... . 69
2.7.4 Implementation of Annotations ....................... . 70
2.7.5 Discussion ......................................... . 75
2.8 Contributions, Lessons, and Conclusions ..................... . 75
2.8.1 Contributions ...................................... . 75
2.8.2 Perception Lessons .................................. . 76
2.8.3 Systems Lessons .................................... . 77
2.8.4 Conclusions ....................................... . 80
References 81
3 Algorithms for Road Navigation 83
Larry S. Davis, Daniel DeMenthon, Sven Dickinson,
and Philip Veatch
3.1 Introduction .............................................. 83
3.2 Maryland Road Follower ................................... 85
3.2.1 System Overview .................................... 86
3.3 Recovery of Three-Dimensional Road Geometry ................ 90
3.3.1 Summary........................................... 92
3.3.2 The Matching Point Problem .......................... 94
3.3.3 Conditions for Two Image Points to Be Matching
Points ............................................. 96
Contents xi
3.3.4 Directions of Tangents to Opposite Points ....... . . . . . . . . . 97
3.3.5 Direction of a Cross Segment ..... . . . . . . . . . . . . . . . . . . . . . . 97
3.3.6 Matching Condition .................................. 97
3.3.7 Local Normal to the Road ............................. 98
3.3.8 Search for a Matching Point of a Given Image Point ....... 98
3.3.9 Dynamic Programming Road Reconstruction ............ 99
3.3.10 Experimental Results ................................. 100
3.4 Detection of Stationary Obstacles on Roads .................... 102
3.4.1 The Range Derivative Algorithm for Obstacle Detection .... 105
3.5 Conclusion ............................................... 108
References .................................................... 108
4 A Visual Control System Using Image Processing and
Fuzzy Theory 111
Hiroshi Kamada and Masumi Yoshida
4.1 Abstract.................................................. 111
4.2 Introduction .............................................. 111
4.3 Development of the System .................................. 112
4.3.1 Real-Time Marker Identification ....................... 112
4.3.2 Fast Recognition of Marker Sequence ................... 114
4.3.3 Steering Control System ............. . . . . . . . . . . . . . . . . .. 116
4.3.4 Marker Sequence Selection Method ..................... 119
4.4 Developed System ......................................... 121
4.4.1 Image Processing Board ..... . . . . . . . . . . . . . . . . . . . . . . . . .. 122
4.4.2 CPU Board ......................................... 125
4.5 Experiment ............................................... 126
4.5.1 Description ......................................... 126
4.5.2 Results ............................................. 127
4.6 Conclusion ............................................... 127
4.7 Further Study ............................................. 127
References .................................................... 127
5 Local Processing as a Cue for Decreasing 3-D Structure
Computation 129
Patrick Stelmaszyk, Hiroshi Ishiguro, Roland Pesty,
and Saburo Tsuji
5.1 Abstract.................................................. 129
5.2 Introduction .............................................. 129
5.3 Active Vision .............................................. 131
5.3.1 Navigation Strategy .................................. 131
5.3.2 Local Computation of3-D Structure .................... 133
5.3.3 Preliminary Results .................................. 138
5.4 Tracking ................................................. 139
5.4.1 Tracking for Matching ................................ 139
5.4.2 Hardware Design .................................... 140
5.4.3 Tracking for Motion Stereo ............................ 141
5.5 Conclusion ............................................... 145
References .................................................... 146