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

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



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

References

Mysliwetz B. (1990): Parallelrechner-basierte Bildfolgen-Interpretation zur autonomen Fahrzeugsteuerung. Dissertation, UniBwM / LRT

Bruedigam C (1994). Intelligente Fahrmanoever sehender autonomer Fahrzeuge in autobahnaehnlicher Umgebung. Dissertation UniBwM / LRT

Behringer R (1996). Visuelle Erkennung und Interpretation des Fahrspurverlaufes durch Rechnersehen für ein autonomes Straßenfahrzeug. Dissertation, UniBwM / LRT

Lützeler M, Dickmanns ED (1998). Road recognition with MarVEye. Intern. Conf. on Intelligent Vehicles, Stuttgart.

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)

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

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

Dickmanns ED (2007). Dynamic Vision for Perception and Control of Motion. Springer-Verlag, (Section 11.3.4.1)