RT Journal Article
A1 Yang Xing
A1 Chen Lv
A1 Huaji Wang
A1 Dongpu Cao
A1 Efstathios Velenis

PB iet
T1 Dynamic integration and online evaluation of vision-based lane detection algorithms
JN IET Intelligent Transport Systems
VO 13
IS 1
SP 55
OP 62
AB Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera.
K1 Hough transform algorithm
K1 real time
K1 false detections
K1 online evaluation
K1 lane geometry knowledge
K1 lane sampling
K1 primary lane detection module
K1 robust lane detection system
K1 integration system
K1 lane missing scenarios
K1 vision-based lane detection algorithms
K1 voting technique
K1 SVT system
K1 low detection confidence
K1 Gaussian mixture model
K1 ego lanes
K1 advance driver assistance systems
K1 steerable filter
K1 dynamic integration
DO https://doi.org/10.1049/iet-its.2018.5256
UL https://digital-library.theiet.org/;jsessionid=1fzd0hb3h827k.x-iet-live-01content/journals/10.1049/iet-its.2018.5256
LA English
SN 1751-956X
YR 2019
OL EN