NDLI: Vision-based lane line detection for autonomous vehicle navigation and guidance

Content Provider	IEEE Xplore Digital Library
Author	Xinxin Du Kok Kiong Tan Kyaw Ko Ko Htet
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Xinxin Du; Kok Kiong Tan; Kyaw Ko Ko Htet)
Abstract	Vehicle pose estimation with respect to the road plays a critical role in the advances of autonomous vehicle navigation and guidance. Vision-based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the lane line detection has remained an open one, given challenging road appearances. In this paper, we propose a more robust vision-based approach by making use of ridge detector and sequential RANSAC (RANdom Sample Consensus). The pose estimation accuracy and consistency are improved by imposing parallelism constraints and fitting multi road models simultaneously. The algorithm is so robust that it is still able to work even when lane line only exists on one side of the road.
Starting Page	1
Ending Page	5
File Size	417789
Page Count	5
File Format	PDF
ISBN	9781479978625
DOI	10.1109/ASCC.2015.7244831
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-31
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Roads sequential RANSAC lane line detection and tracking Estimation vehicle pose estimation autonomous vehicle navigation and guidance Data models Robustness Mathematical model Vehicles
Content Type	Text
Resource Type	Article

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