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Visual lane analysis and higher-order tasks: a concise review

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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

  1. Suitable for stereo performance evaluation using the third-eye approach of [98].

  2. In general, we define robustness by high accuracy across a defined range of scenarios.

  3. See Fig.7.11 in [36] for an example.

  4. See hci.iwr.uni-heidelberg.de/Static/challenge2012/.

  5. The occupancy grid is called ‘evidence grid’ in [90].

  6. 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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Correspondence to Bok-Suk Shin.

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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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