Intelligent imaging systems for automotive applications

In common with many other application areas, visual signals are becoming an increasingly important information source for many automotive applications. For several years CCD cameras have been used as research tools for a range of automotive applications. Infrared cameras, RADAR and LIDAR are other types of imaging sensors that have also been widely investigated for use in cars. This paper will describe work in this field performed in C2VIP over the last decade - starting with Night Vision Systems and looking at various other Advanced Driver Assistance Systems. Emerging from this experience, we make the following observations which are crucial for "intelligent’ imaging systems: 1. Careful arrangement of sensor array. 2. Dynamic-Self-Calibration. 3. Networking and processing. 4. Fusion with other imaging sensors, both at the image level and the feature level, provides much more flexibility and reliability in complex situations. We will discuss how these problems can be addressed and what are the outstanding issues.

[1]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[2]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[3]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  W. Enkelmann,et al.  Robust obstacle detection and tracking by motion analysis , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[6]  Nicola Ancona.  A Fast Obstacle Detection Method based on Optical Flow , 1992, ECCV.

[7]  L. L. Nagy.  Electromagnetic reflectivity characteristics of road surfaces , 1974 .

[8]  Uwe Franke,et al.  Real-time stereo vision for urban traffic scene understanding , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[9]  Kamal Sarabandi,et al.  Polarimetric characterization of debris and faults in the highway environment at millimeter-wave frequencies , 2000 .

[10]  Thomas Zielke,et al.  An integrated obstacle detection framework for intelligent cruise control on motorways , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[11]  Thomas Kalinke,et al.  A Texture-based Object Detection and an adaptive Model-based Classification , 1998 .

[12]  L. Eriksson,et al.  A high performance automotive radar for automatic AICC , 1995, Proceedings International Radar Conference.

[13]  Zhencheng Hu,et al.  Tracking cycle: a new concept for simultaneous tracking of multiple moving objects in a typical traffic scene , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[14]  B.-O. As,et al.  Automotive radar for adaptive cruise control and collision warning/avoidance , 1997 .

[15]  S. Heinrich,et al.  Fast obstacle detection for urban traffic situations , 2002, IEEE Trans. Intell. Transp. Syst..

[16]  Tatsuya Suzuki,et al.  Measurement of vehicle motion and orientation using optical flow , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[17]  Larry S. Davis,et al.  Multiple vehicle detection and tracking in hard real-time , 1996, Proceedings of Conference on Intelligent Vehicles.

[18]  Jack J. Ference,et al.  Millimeter wave scattering characteristics and radar cross section measurements of common roadway objects , 1995, Other Conferences.

[19]  W. D. Jones,et al.  Keeping cars from crashing , 2001 .

[20]  Lars H. Eriksson,et al.  High performance automotive radar , 1996 .