Optical Road Monitoring of the Future Smart Roads – Preliminary Results

It has been shown that in most accidents the driver is responsible due to being distracted or misjudging the situation. In order to solve such problems research has been dedicated to developing driver assistance systems that are able to monitor the traffic situation around the vehicle. This paper presents methods for recognizing several circumstances on a road. The methods use both the in-vehicle warning systems and the roadside infrastructure. Preliminary evaluation results for fog and ice-on-road detection are presented. The ice detection results are based on data recorded in a test track dedicated to tyre friction testing. The achieved results anticipate that ice detection could work at a performance of 70% detection with the right setup, which is a good foundation for implementation. However, the full benefit of the presented cooperative system is achieved by fusing the outputs of multiple data sources, which is the key point of discussion behind this publication.

• Maria Jokela
• Matti Kutila
• Jukka Laitinen
• Florian Ahlers
• Nicolas Hautière
• TobiasSchendzielorz

• Smart roads
• traffic monitoring
• traffic scenedetection.

[1] J. Fridthjof, J. A Device for Detection of Road Surface Condition. Patent
number: WO/2004/081897. 2004.
[2] Hautiere, N., Tarel, J.-P., Lavenant, J. & Aubert, D. Automatic fog
detection and estimation of visibility distance through use of an onboard
camera. Machine Vision and Applications, Vol. 17, Issue 1, pp. 8-20.
2006
[3] Yang-s Scientific Research Institute www-page. www.yangsky.com.
cited in (15 Mar 2007).
[4] Väre, Hunta, Martin. "Eläinten kulkujärjestelyt tiealueen poikki".
Tiehallinnon selvityksiä 36/2003. Tiehallinto, Helsinki 2003.
[5] N. Hautière, R. Labayrade and D. Aubert. "Real-Time Disparity Contrast
Combination for Onboard Estimation of the Visibility Distance". IEEE
Transactions on Intelligent Transportation Systems, 7(2):201-212, June
2006.
[6] N. Hautière and D. Aubert. "Contrast Restoration of Foggy Images
through use of an Onboard Camera". In IEEE Conference on Intelligent
Transportation Systems (ITSC-05), Vienna, Austria, pages 1090-1095,
September 2005.
[7] Hall, D. L. / McMullen, S. A. H. (2004): Mathematical Techniques in
Multisensor Data Fusion, 2. Edition, Norwood, 2004
[8] W. Yao-Jan, H, Chun-Po, L. Feng-Li and C. Tang-Hsien. "Vision-based
driving environment identification for autonomous highway vehicles".
Proceedings of the 2004 IEEE International Conference on Networking,
Sensing and Control, Taipei, Taiwan, pp. 1323 - 1328, March 2004.
[9] S.G. Narasimhan, S.K. Nayar, "Shedding light on the weather" IEEE
Computer Society Conference on Computer Vision and Pattern
Recognition, Vol. 1, pp.665-672, 2003.