Autonomous Driving through Intelligent Image Processing and Machine Learning (original) (raw)

Abstract

This abstract describes the current research in the area of autonomously driving of a vehicle along different road courses [1]. The focus of this paper are two main aspects: firstly, parameters of the environment are being extracted from a video image coming from one single camera which is installed in or in front of the vehicle which is to drive along the road course; secondly, the incoming images from the camera need to be processed by a computer system that way, that not only Steering Commands for the vehicle are being generated (for accelerator / brake as well as the steering wheel) but the appropriateness of those Steering Commands is being constantly weighed and continuously improved over time. Consequently, the current work focuses on a system which is able to learn and to develop completely on its own the ability to steer different vehicles in different environments and combines research in the areas of Intelligent Image Processing, Machine Learning and Robotics.

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References

  1. M. Krödel, K.-D. Kuhnert, Towards a Learning Autonomous Driver System, IEEE International Conference on Industrial Electronics, Control and Instrumentation, October 22–28, 2000, Nagoya, Japan
    Google Scholar
  2. T.M. Jochem, D.A. Pomerleau, C.E. Thorpe, Vision Guided Lane Transition, Intelligent Vehicles’ 95 Symposium, September 25–26, 1995, Detroit/MI, USA
    Google Scholar
  3. T.M. Jochem, D.A. Pomerleau, C.E. Thorpe. MANIAC: A Next Generation Neurally Based Autonomous Road Follower, IAS-3, Int. Conference on Intelligent autonomous Systems, Feb. 15–18, 1993, Pittsburgh/PA, USA, F.C.A. Groen, S. Hirose, C.E. Thorpe (eds), IOS Press, Washington, Oxford, Amsterdam, Tokyo, 1993
    Google Scholar
  4. Expectation-based selective attention for visual monitoring and control of a robot vehicle, S. Baluja, D.A. Pomerleau, Robotics and Autonomous System, Vol.22, No.3–4, December 1997
    Google Scholar
  5. E.D. Dickmanns, A. Zapp, Autonomous High Speed Road Vehicle Guidance by Computer Vision, Preprints of the 10th World Congress on Automatic Control, Vol.4, International Federation of Automatic Control, Munich, Germany, July 27–31, 1987
    Google Scholar
  6. L. Baird III, Reinforcement Learning through Gradient Descent, Dissertation, Carnegie Mellon University, 1999 Pittsburgh, USA
    Google Scholar

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Author information

Authors and Affiliations

  1. Institute for Real-Time-Systems, Universität - GH Siegen, Hölderlinstrasse 3, D-57068, Siegen, Germany
    Michael Krödel & Klaus-Dieter Kuhnert

Authors

  1. Michael Krödel
  2. Klaus-Dieter Kuhnert

Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany
    Bernd Reusch

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© 2001 Springer-Verlag Berlin Heidelberg

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Krödel, M., Kuhnert, KD. (2001). Autonomous Driving through Intelligent Image Processing and Machine Learning. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4\_70

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