A multi-model lane detector that handles road singularities

Road detection through the use of an onboard camera is an essential task required for the development of advanced driving assistance systems. Although many solutions have been proposed in literature, most existing algorithms are not designed to handle road singularities such as freeway exits, double painted lines, zebra road markings, etc. Such conditions can be confusing for vision-based algorithms although they are often met in real driving situations. In order to tackle this issue, we propose the use of various instances of a road model at the same time. We first describe the generic road model used and then explain how the different instances of this model are updated. The extracted models can be used either to detect the correct vehicle lane or to obtain a more complete description of the road configuration. Experiments are presented to demonstrate the validity of the approach. An application to multi-lane detection is also presented

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