A.3.3 Various types of roads (with / without lane markings):

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Recognition of roads without lane markings or even roads without a sealed surface can be very difficult depending on the type of surface, the lighting- and the weather conditions.

  • Smooth surfaces with homogeneous appearance and good contrast at the boundaries are the easiest ones.
  • If there are buildings or trees near the road and sunshine casts shadows on the road, recognition of the road boundaries may become very difficult; usually, the argument of smooth continuation of road edges gives a good guideline for grouping edge elements. Edge intensity is not a good argument for selection and combination of edge elements.
  • Since boundaries of dirt roads are not smooth and crisp but rather jagged, edge detection by large masks with a wide gap between the positive and the negative parts of the mask are favorable.
  • Clothoid models are well suited for perceiving these roads also even though they might not have been designed this way.

video 03 VaMoRsCampusRoad 1987


Road fork recognition

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At larger distances, using region-based features is more robust for detecting and tracking roads including road forks (see figure):


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Gregor R, Lützeler M, Pellkofer M, Siedersberger KH, Dickmanns ED (2000). EMS-Vision: A Perceptual System for Autonomous Vehicles. Proc. Int. Symposium on Intelligent Vehicles (IV’2000), Dearborn, (MI) pdf

Luetzeler M (2002). Fahrbahnerkennung zum Manoevrieren auf Wegenetzen mit aktivem Sehen. Dissertation, UniBwM, LRT. Kurzfassg

Siedersberger K-H (2004)Komponenten zur automatischen Fahrzeugführung in sehenden (semi-) autonomen Fahrzeugen. Dissertation, UniBwM, LRT. Kurzfassg

Dickmanns ED (2007). Dynamic Vision for Perception and Control of Motion. Springer-Verlag, (Section