Proceedings ArticleDOI
Multi-lane detection in urban driving environments using conditional random fields
Junhwa Hur,Seung-Nam Kang,Seung-Woo Seo +2 more
- pp 1297-1302
TLDR
This paper proposes a new multi-lane detection algorithm that works well in urban situations and shows that CRFs are very effective tools for multi- lane detection because they find an optimal association of multiple lane marks in complex and challenging urban road situations.Abstract:
Over the past few decades, the need has arisen for multi-lane detection algorithms for use in vehicle safety-related applications. In this paper we propose a new multi-lane detection algorithm that works well in urban situations. This algorithm detects four lane marks, including driving lane marks and adjacent lane marks. Conventional research assumes that lanes are parallel. In contrast, our approach does not require this assumption, thus enabling the algorithm to manage various non-parallel lane situations, such as are found at intersections, in splitting lanes, and in merging lanes. To detect multi-lane marks successfully in the absence of parallelism, we adopt Conditional Random Fields (CRFs), which are strong models for solving multiple association tasks. We show that CRFs are very effective tools for multi-lane detection because they find an optimal association of multiple lane marks in complex and challenging urban road situations. Through simulations, and by using video sequences with 752-480 resolution and Caltech Lane Datasets with runtime rates of 30 fps, we verify that our algorithm successfully detects non-parallel lanes as well as parallel lanes appearing in urban streets.read more
Citations
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Proceedings ArticleDOI
Towards End-to-End Lane Detection: an Instance Segmentation Approach
TL;DR: In this article, the authors cast the lane detection problem as an instance segmentation problem, in which each lane forms its own instance and parametrize the segmented lane instances before fitting the lane, in contrast to a fixed "bird's-eye view" transformation.
Proceedings ArticleDOI
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee,Junsik Kim,Jae Shin Yoon,Seunghak Shin,Oleksandr Bailo,Namil Kim,Tae-Hee Lee,Hyun Seok Hong,Seung-hoon Han,In So Kweon +9 more
TL;DR: In this paper, a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions is proposed.
Posted Content
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee,Junsik Kim,Jae Shin Yoon,Seunghak Shin,Oleksandr Bailo,Namil Kim,Tae-Hee Lee,Hyun Seok Hong,Seung-hoon Han,In So Kweon +9 more
TL;DR: A unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions is proposed and achieves high accuracy and robustness under various conditions in realtime.
Journal ArticleDOI
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
TL;DR: This work investigates lane detection by using multiple frames of a continuous driving scene, and proposes a hybrid deep architecture by combining the convolutional neural network and the recurrent neural network, which outperforms the competing methods in lane detection.
Journal ArticleDOI
Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision
TL;DR: In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods, and a Computational experiment-based parallel lane detection framework is proposed.
References
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Proceedings ArticleDOI
Learning a classification model for segmentation
TL;DR: A two-class classification model for grouping is proposed that defines a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation, and trains a linear classifier to combine these features.
An Introduction to Conditional Random Fields for Relational Learning
Charles Sutton,Andrew McCallum +1 more
TL;DR: A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification, and this is the approach taken by conditional random fields.
Proceedings ArticleDOI
Real time detection of lane markers in urban streets
TL;DR: In this paper, a robust and real-time approach to lane marker detection in urban streets is presented, which is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANAC algorithm for fitting Bezier Splines, which was then followed by a post-processing step.
Proceedings ArticleDOI
Real time Detection of Lane Markers in Urban Streets
TL;DR: A robust and real time approach to lane marker detection in urban streets based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RansAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step.
Journal ArticleDOI
Robust Lane Detection and Tracking in Challenging Scenarios
TL;DR: A robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes is presented.