A Flexible Architecture for Driver Assistance Systems
- First Online:
The problems encountered in building a driver assistance system are numerous. The collection of information about real environment by sensors is erroneous and incomplete. When the sensors are mounted on a moving observer it is difficult to find out whether a detected motion was caused by ego-motion or by an independent object moving. The collected data can be analyzed by several algorithms with different features designed for different tasks. To gain the demanded information their results have to be integrated and interpreted. In order to achieve an increase in reliability of information a stabilization over time and knowledge about important features have to be applied. Different solutions for driver assistance systems have been published. An approach proposed by Rossi et al.  showed an application for a security system. An application being tested on highways has been presented by Bertozzi and Broggi . Dickmanns et al. presented a driving assistance system based on a 4D-approach . Those systems were mainly designed for highway scenarios, while the architecture presented by Franke and Görzig  has been tested in urban environment.
Unable to display preview. Download preview PDF.
- 1.M. Bertozzi and A. Broggi. GOLD: a Parallel Real-Time Stereo Vision System or Generic Obstacle and Lane Detection. In IEEE, editor, IEEE Transactions on Image Processing, volume 4(2), pages 114–136, 1997.Google Scholar
- 2.E. D. Dickmanns et al. Vehicles capable of dynamic vision. In 15th International Joint Conference on Artificial Intelligence (IJCAI), pages 1–16, Nagoya, Japan, 1997.Google Scholar
- 3.S. Goerzig and U. Franke. ANTS-Intelligent Vision in Urban Traffic. In IV’98, IEEE International Conference on Intelligent Vehicles 1998, pages 545–549, Stuttgart, Germany, 1998.Google Scholar
- 4.U. Handmann, T. Kalinke, C. Tzomakas, M. Werner, and W. von Seelen. An Image Processing System for Driver Assistance. In IV’98, IEEE International Conference on Intelligent Vehicles 1998, pages 481–486, Stuttgart, Germany, 1998.Google Scholar
- 5.U. Handmann, I. Leefken, and C. Tzomakas. A Flexible Architecture for Intelligent Cruise Control. In ITSC’99, IEEE Conference on Intelligent Transportation Systems 1999, Tokyo, Japan, 1999.Google Scholar
- 6.U. Handmann, I. Leefken, and C. Tzomakas. Eine flexible Architektur für Fahrerassistenzsysteme. In Mustererkennung 1999, Heidelberg, 1999. Springer-Verlag. DAGM’99.Google Scholar
- 7.U. Handmann, G. Lorenz, T. Schnitger, and W. von Seelen. Fusion of Different Sensors and Algorithms for Segmentation. In IV’98, IEEE International Conference on Intelligent Vehicles 1998, pages 499–504, Stuttgart, Germany, 1998.Google Scholar
- 8.M. Rossi, M. Aste, R. Cattoni, and B. Caprile. The IRST Driver’s Assistance System. Technical Report 9611-01, Instituto per la Ricerca Scientificia e Technologica, Povo, Trento, Italy, 1996.Google Scholar
- 9.R. Sukthankar. Situation Awareness for Tactical Driving. Phd thesis, Carnegie Mellon University, Pittsburgh, PA, United States of America, 1997.Google Scholar
- 10.Qiang Zhuang, Jens Gayko, and Martin Kreutz. Optimization of a Fuzzy Controller for a Driver Assistant System. In Proceedings of the Fuzzy-Neuro Systems 98, pages 376–382, München, Germany, 1998.Google Scholar