Methods and apparatus for characterizing point cloud data for autonomous vehicle systems

Abstract

According to one aspect, an autonomous vehicle that includes a lidar unit collects lidar point cloud data that includes false returns or false positives, and characterizes the data associated with the false returns or false positives as drivable or not drivable. The false returns or false positives may be phantom points that are not associated with actual objects which may pose collision risks. Analyzing lidar point cloud data to characterize false returns or false positives as either drivable or not drivable enables an autonomous vehicle to operate efficiently by not having to avoid non-existent collision risks. False positives may be indicated when a wet or icy road surface acts as a mirror which reflects objects, and when precipitation such as raindrops appear as objects. Characterizing such false positives as drivable facilitates the efficient operation of an autonomous vehicle as the autonomous vehicle may drive over a mirror and/or through precipitation.