Drivable area segmentation is an important feature for autonomous driving. Currently, state of the art techniques in this area focus on segmenting roads in urban areas with near perfect conditions. Roads with deteriorating conditions have received much less attention, even though they are common and present a unique set of challenges to the road segmentation tasks. These challenges include detecting obstacles (manholes and potholes) and determining whether or not it is safe to drive over them, and detecting road boundaries while lacking proper markings. This paper proposes a new method for drivable area segmentation in roads with deteriorating conditions based on a 3D LiDAR. Our framework represents the LiDAR point cloud data in an angular grid object which splits the data into smaller point cloud objects based on the laser scan number and the projection angle of each point. We apply multiple filtration steps in our framework in order to accurately detect the road boundaries and to detect and classify any road irregularities. The experiments on our collected datasets demonstrate the performance of our framework in detecting and classifying road drivable regions accurately and robustly. We reached a maximum precision of 92.78% in detection road boundaries and a max precision of 99.38% in detection and classifying road irregularities.
Drivable Area Segmentation in Deteriorating Road Regions for Autonomous Vehicles using 3D LiDAR Sensor
2021-07-11
5112744 byte
Conference paper
Electronic Resource
English
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