Abstract
Lane detection, lane tracking, or lane departure warning have been the earliest components of vision-based driver assistance systems. At first (in the 1990s), they have been designed and implemented for situations defined by good viewing conditions and clear lane markings on highways. Since then, accuracy for particular situations (also for challenging conditions), robustness for a wide range of scenarios, time efficiency, and integration into higher-order tasks define visual lane detection and tracking as a continuing research subject. The paper reviews past and current work in computer vision that aims at real-time lane or road understanding under a comprehensive analysis perspective, for moving on to higher-order tasks combined with various lane analysis components, and introduces related work along four independent axes as shown in Fig. 2. This concise review provides not only summarizing definitions and statements for understanding key ideas in related work, it also presents selected details of potentially applicable methods, and shows applications for illustrating progress. This review helps to plan future research which can benefit from given progress in visual lane analysis. It supports the understanding of newly emerging subjects which combine lane analysis with more complex road or traffic understanding issues. The review should help readers in selecting suitable methods for their own targeted scenario.
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Notes
Suitable for stereo performance evaluation using the third-eye approach of [98].
In general, we define robustness by high accuracy across a defined range of scenarios.
See Fig.7.11 in [36] for an example.
The occupancy grid is called ‘evidence grid’ in [90].
Vision benchmark suite of the Karlsruhe Institute of Technology and the Toyota Technological Institute at Chicago; see www.cvlibs.net/datasets/kitti/.
Abbreviations
- ACC:
-
Adaptive cruise control
- AR:
-
Augmented reality
- BPM:
-
Belief-propagation matching
- DA:
-
Driver assistance
- DEM:
-
Digital elevation map
- DT:
-
Distance transform
- ECCV:
-
European Conference Computer Vision
- EDT:
-
Euclidean distance transform
- EISATS:
-
enpeda image sequence analysis test site
- enpeda:
-
Environment perception and driver assistance
- ETRI:
-
Electronics Telecommunications Research Institute
- GCM:
-
Graph-cut matching
- GPS:
-
Global positioning system
- HT:
-
Hough transform
- HCI:
-
Heidelberg Collaboratory for Image Processing
- HUD:
-
Head-up display
- IHC:
-
Intelligent headlight control
- IPM:
-
Inverse perspective mapping
- iSGM:
-
Iterative SGM
- KITTI:
-
Karlsruhe Institute Technology and Toyota Institute
- LCW:
-
Lane change warning
- LDW:
-
Lane departure warning
- LIDAR:
-
Light detection and ranging
- MCLDW:
-
Multi-camera lane departure warning
- ODT:
-
Orientation distance transform
- PDF:
-
Point-distribution function
- RANSAC:
-
Random sample consensus
- RODT:
-
Row component of ODT
- ROI:
-
Region of interest
- SGM:
-
Semi-global matching
- SHT:
-
Statistical Hough transform
- SLAM:
-
Simultaneous localization and mapping
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Acknowledgments
We thank all the colleagues who gave their permissions for the inclusion of their figures into this survey. We also thank Ali Al-Sarraf, Mahdi Rezaei, and Junli Tao, members of the .enpeda.. group, The University of Auckland, for help in collecting references and related discussions.
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All three authors are members of the .enpeda.. (Environment Perception and Driver Assistance) project at The University of Auckland.
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Shin, BS., Xu, Z. & Klette, R. Visual lane analysis and higher-order tasks: a concise review. Machine Vision and Applications 25, 1519–1547 (2014). https://doi.org/10.1007/s00138-014-0611-8
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DOI: https://doi.org/10.1007/s00138-014-0611-8