Real-Time Road Curb and Lane Detection for Autonomous Driving Using LiDAR Point Clouds

The commercialization of automated driving vehicles promotes the development of safer and more efficient autonomous driving technologies including lane marking detection strategy, which is considered to be the most promising feature in environmental perception technology. To reduce the tradeoff betw...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.144940-144951
Main Authors: Huang, Jing, Choudhury, Pallab K., Yin, Song, Zhu, Lingyun
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Clouds
Clustering algorithms
Commercialization
curb filtering
Datasets
Driving conditions
intensity threshold
Lane marking detection
Laser radar
Lidar
Marking
Performance measurement
point cloud
Real time
Roads
Three-dimensional displays
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The commercialization of automated driving vehicles promotes the development of safer and more efficient autonomous driving technologies including lane marking detection strategy, which is considered to be the most promising feature in environmental perception technology. To reduce the tradeoff between time consumption and detection precision, we propose a real-time lane marking detection method by using LiDAR point clouds directly. A constrained RANSAC algorithm is applied to select the regions of interest and filter the background data. Further, a road curb detection method based on the segment point density is also proposed to classify the road points and curb points. Finally, an adaptive threshold selection method is proposed to identify lane markings. In this investigation, five datasets are collected from different driving conditions that include the straight road, curved road, and uphill, to test the proposed method. The proposed method is evaluated under different performance metrics such as Precision, Recall, Dice, Jaccard as well as the average detection distance and computation time for the five datasets. The quantitative results show the efficiency and feasibility of this proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3120741