Paper Info
Title
Drivable Area Segmentation in Deteriorating Road Regions for Autonomous Vehicles using 3D LiDAR Sensor
Abstract
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 9938% in detection and classifying road irregularities.
Year
DOI
Venue
2021
10.1109/IV48863.2021.9575552
2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV)
DocType
ISSN
Citations 
Conference
1931-0587
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Abdelrahman Ali100.34
Mark Gergis200.34
Slim Abdennadher339460.95
Amr El Mougy400.68