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Vision-based Vehicle Guidance

  • Textbook
  • © 1992

Overview

Editors:
  1. Ichiro Masaki

    1. General Motors Research Laboratories, Computer Science Department, Warren, USA

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Part of the book series: Springer Series in Perception Engineering (SSPERCEPTION)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Vision-based Autonomous Road Vehicles

    • Volker Graefe, Klaus-Dieter Kuhnert
    Pages 1-29

  3. The New Generation System for the CMU Navlab

    • Charles Thorpe, Martial Hebert, Takeo Kanade, Steven Shafer
    Pages 30-82

  4. Algorithms for Road Navigation

    • Larry S. Davis, Daniel DeMenthon, Sven Dickinson, Philip Veatch
    Pages 83-110

  5. A Visual Control System Using Image Processing and Fuzzy Theory

    • Hiroshi Kamada, Masumi Yoshida
    Pages 111-128

  6. Local Processing as a Cue for Decreasing 3-D Structure Computation

    • Patrick Stelmaszyk, Hiroshi Ishiguro, Roland Pesty, Saburo Tsuji
    Pages 129-147

  7. Object Detection Using Model-based Prediction and Motion Parallax

    • Stefan Carlsson, Jan-Olof Eklundh
    Pages 148-161

  8. Road Sign Recognition: A Study of Vision-based Decision Making for Road Environment Recognition

    • Marie de Saint Blancard
    Pages 162-172

  9. From Self-Navigation to Driver’s Associate: An Application of Mobile Robot Vision to a Vehicle Information System

    • Kohji Kamejima, Tomoyuki Hamada, Masahiro Tsuchiya, Yuriko C. Watanabe
    Pages 173-203

  10. Recent Progress in Mobile Robot Harunobu-4

    • Hideo Mori
    Pages 204-221

  11. Visual Navigation of an Autonomous On-Road Vehicle: Autonomous Cruising on Highways

    • Akihiro Okuno, Kenji Fujita, Atsushi Kutami
    Pages 222-237

  12. Finding Road Lane Boundaries for Vision-guided Vehicle Navigation

    • L. T. Schaaser, B. T. Thomas
    Pages 238-254

  13. An Extracting Method of the Optical Flow for an Anticollision System

    • Toshio Ito, Shiro Kawakatsu
    Pages 255-267

  14. Obstacle Avoidance and Trajectory Planning for an Indoor Mobile Robot Using Stereo Vision and Delaunay Triangulation

    • Michel Buffa, Olivier D. Faugeras, Zhengyou Zhang
    Pages 268-283

  15. A Parallel Architecture for Curvature-based Road Scene Classification

    • Amy Polk, Ramesh Jain
    Pages 284-299

  16. Mobile Robot Perception Using Vertical Line Stereo

    • James L. Crowley, Philippe Bobet, Karen Sarachik
    Pages 300-324

  17. Back Matter

    Pages 325-332
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Keywords

About this book

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.

Editors and Affiliations

  • General Motors Research Laboratories, Computer Science Department, Warren, USA

    Ichiro Masaki

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