Dynamic integration and online evaluation of vision-based lane detection algorithms

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 st...

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Veröffentlicht in:IET intelligent transport systems Jg. 13; H. 1; S. 55 - 62
Hauptverfasser: Xing, Yang, Lv, Chen, Wang, Huaji, Cao, Dongpu, Velenis, Efstathios
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.01.2019
Schlagworte:
advance driver assistance systems
cameras
computer vision
dynamic integration
edge detection
ego lanes
false detections
feature extraction
Gaussian mixture model
Gaussian processes
Hough transform algorithm
Hough transforms
image segmentation
integration system
lane geometry knowledge
lane missing scenarios
lane sampling
low detection confidence
object detection
online evaluation
primary lane detection module
real time
road vehicles
robust lane detection system
Special Issue: Recent Advancements on Electrified, Low Emission and Intelligent Vehicle-Systems
steerable filter
SVT system
traffic engineering computing
vision‐based lane detection algorithms
voting technique
Hough transform algorithm
dynamic integration
advance driver assistance systems
steerable filter
false detections
integration system
object detection
vision-based lane detection algorithms
SVT system
Gaussian mixture model
cameras
robust lane detection system
ego lanes
lane missing scenarios
road vehicles
feature extraction
image segmentation
edge detection
real time
low detection confidence
traffic engineering computing
voting technique
online evaluation
Hough transforms
computer vision
Gaussian processes
lane sampling
lane geometry knowledge
primary lane detection module
ISSN:1751-956X, 1751-9578
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Beschreibung
Zusammenfassung: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.
ISSN:1751-956X
1751-9578
DOI:10.1049/iet-its.2018.5256