Abstract
Lane width evaluation is one of the crucial aspects in road safety inspection, especially in work zones where a narrow lane width can result in a reduced roadway capacity and also, increase the probability of severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information along road surface. This paper presents an approach to derive lane width estimates using point clouds acquired from a geometrically-calibrated mobile mapping system. Starting from an accurate LiDAR point cloud, the road surface is extracted with the assistance of trajectory elevation data. Lane markings are identified based on the intensity data. Next, the lane marking centerline is derived and clustered to identify areas with ambiguous or missing lane markings and finally, use the normal (or, unambiguous) lane markings to estimate the lane width. The derived lane width estimates are used to develop a reporting mechanism for areas with narrow lanes, ambiguous lane markings, missing lane markings, and/or wide lanes.
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