Real-time lane detection for autonomous vehicle

For navigation tasks it is necessary to determine position of the ego vehicle relative to the road. One of the principal approaches is to detect road boundaries and lanes using a vision system in the vehicle. This paper presents a simple and robust method designed to detect and estimate the curvature of road lane boundaries from images provided by a monocular camera. First, we use Vector-lane-concept and Non-uniform B-Spline (NUBS) interpolation method to construct the boundaries road lane. Based on lane detection result, we estimate the curvature of left and right lane boundaries for Autonomous Guided Vehicle systems application. Some experimental results based on real world road images are presented. These simulation results show the efficiency, feasibility and robustness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

New lane detection algorithm for autonomous vehicles using computer vision

Truong

Lee

2008

2008 International Conference on Control, Automation and Systems

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“…These approaches aim to match the observed images with a hypothesized road model. This technique is based on the assumption that the shapes lane can be represented by straight lines, parabolic curves, or snack-like curves which can be found in [4,5,6,7]. In this work, we present a real-time lane detection algorithm based on vector lane concept which shows robust results on various lane road shapes and effective with image noise.…”

Section: Introductionmentioning

confidence: 99%

Lane boundaries detection algorithm using vector lane concept

Truong

Lee

Heo

et al. 2008

2008 10th International Conference on Control, Automation, Robotics and Vision

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A lane detection system is an important component of many intelligent transportation systems. In this paper, we present a robust and real-time lane tracking algorithm for any curved road. We use Non Uniform B-Spline (NUBS) interpolation method to construct the boundaries road lane. First, the Canny filter is employed to obtain edge map. Second, we apply parallel thinning algorithm into edge map to get the skeleton image. Once skeleton image is obtained, we introduce a new algorithm based on Vector Lane Concept to extract feature edge points that play the role as control points for NUBS interpolation process to represent left and right lane, separated. Finally, we estimate the curvature of left and right lane boundary for lane tracking. The result of this method can be used for Autonomous Guided Vehicles (AGV) system application.

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Traffic Light Detection Using Rotated Principal Component Analysis for Video-Based Car Navigation System

Joo¹,

Kim²,

Cho³

et al. 2008

IEICE Transactions on Information and Systems

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SUMMARY This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

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Real-time lane detection for autonomous vehicle

Cited by 8 publications

References 4 publications

New lane detection algorithm for autonomous vehicles using computer vision

New lane detection algorithm for autonomous vehicles using computer vision

Lane boundaries detection algorithm using vector lane concept

Traffic Light Detection Using Rotated Principal Component Analysis for Video-Based Car Navigation System

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