Real time lane detection for autonomous vehicles
An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is base...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/5896/ http://irep.iium.edu.my/5896/ http://irep.iium.edu.my/5896/ http://irep.iium.edu.my/5896/1/04580573.pdf |
| Summary: | An increasing safety and reducing road
accidents, thereby saving lives are one of great interest
in the context of Advanced Driver Assistance Systems.
Apparently, among the complex and challenging tasks
of future road vehicles is road lane detection or road
boundaries detection. It is based on lane detection
(which includes the localization of the road, the
determination of the relative position between vehicle
and road, and the analysis of the vehicle’s heading
direction). One of the principal approaches to detect
road boundaries and lanes using vision system on the
vehicle. However, lane detection is a difficult problem
because of the varying road conditions that one can
encounter while driving. In this paper, a vision-based
lane detection approach capable of reaching real time
operation with robustness to lighting change and
shadows is presented. The system acquires the front
view using a camera mounted on the vehicle then
applying few processes in order to detect the lanes.
Using a pair of hyperbolas which are fitting to the
edges of the lane, those lanes are extracted using
Hough transform. The proposed lane detection system
can be applied on both painted and unpainted road as
well as curved and straight road in different weather
conditions. This approach was tested and the
experimental results show that the proposed scheme
was robust and fast enough for real time requirements.
Eventually, a critical overview of the methods were
discussed, their potential for future deployment were
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