Virtual assessment of sight distance limitations using LiDAR technology: Automated obstruction detection and classification.

The amount of sight distance available to drivers is an extremely critical design element with significant impacts on the safe operation of highways. If the available sight distance on a roadway is limited, this reduces the ability of drivers to safely carry out certain maneuvers such as stopping or passing. Obstructions to sight distance are often caused by a range of different factors. This includes roadside vegetation overgrowing into the road's right of way and obstructions related to the road's alignment. To ensure that sufficient sight distance is available throughout a road's service life, transportation agencies are required to conduct regular long site visits to identify the existence of any obstructions while identifying the types of those obstructions. Although many studies have attempted quantifying available sight distance in the past, not much work has been done to automatically identify and study the types of obstructions causing sight distance limitations. This information is critical to entities looking to decide on the appropriate construction activity required to alleviate sight distance limitations. To fill in this gap, this paper proposes a fully automated novel method to assess sight distance on highways scanned using mobile LiDAR technology, while also proposing a method for the automated classification of the type of obstruction detected. The algorithm is tested on multiple roadway segments in Alberta, Canada and was successful in identifying limitations in sight distance and classifying obstructions into different types of objects. • Automated LiDAR based assessment of visibility on highways. • An algorithm for automated detection of sight distance obstructions is proposed. • The algorithm also automatically classifies obstructions into different objects. • High accuracy achieved after testing on multiple highway segments in Alberta. • Obstructions due to road, signs, vegetation...etc. are detected and classified. [ABSTRACT FROM AUTHOR]