A Lane Departure Warning System Based on Machine Vision

Yu, Bing; Zhang, Weigong; Cai, Yingfeng

doi:10.1109/paciia.2008.142

Cited by 42 publications

(22 citation statements)

References 5 publications

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“…Numerous line detection algorithms and techniques have recently been proposed [7][8][9][10][11][12]. Among these algorithms, the Hough transform is one of the most robust and extensively used [13][14][15][16][17].The Hough transform is implemented according to (1):…”

Section: Line Detection Algorithmsmentioning

confidence: 99%

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Real-time lane departure warning system based on a single FPGA

Shang

Song

et al. 2013

J Image Video Proc

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This paper presents a camera-based lane departure warning system implemented on a field programmable gate array (FPGA) device. The system is used as a driver assistance system, which effectively prevents accidents given that it is endowed with the advantages of FPGA technology, including high performance for digital image processing applications, compactness, and low cost. The main contributions of this work are threefold. (1) An improved vanishing point-based steerable filter is introduced and implemented on an FPGA device. Using the vanishing point to guide the orientation at each pixel, this algorithm works well in complex environments. (2) An improved vanishing point-based parallel Hough transform is proposed. Unlike the traditional Hough transform, our improved version moves the coordinate origin to the estimated vanishing point to reduce storage requirements and enhance detection capability. (3) A prototype based on the FPGA is developed. With improvements in the vanishing point-based steerable filter and vanishing point-based parallel Hough transform, the prototype can be used in complex weather and lighting conditions. Experiments conducted on an evaluation platform and on actual roads illustrate the effective performance of the proposed system.

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Section: Line Detection Algorithmsmentioning

confidence: 99%

“…A number of methods that address curves have been reported [27,28], in which the most common lane detection technique used is a deformable road model [7,29]. Our system does not need to accurately detect lane markings in the distant part of a road.…”

Section: Road Curvesmentioning

confidence: 99%

Real-time lane departure warning system based on a single FPGA

Shang

Song

et al. 2013

J Image Video Proc

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“…The main advantage is to allow longer time for a driver to take a timely and effective response to an imminent crash. The conventional LDP algorithms deal with the detection and estimation of lane boundaries (e.g., lane markings and road edges), which are mainly based on Kalman filters for tracking road features using data obtained from various sensors installed on the vehicle (e.g., radar and camera) [13], [22], [30], [33], followed by threat assessment, e.g., time-toline crossing (TLC), and warning decision, e.g., a threshold for alarm activation. This paper will focus on the latter stage with regard to the more effective warning decision.…”

Section: Lane Departure Predictionmentioning

confidence: 99%

On the Use of Stochastic Driver Behavior Model in Lane Departure Warning

Angkititrakul

Terashima

Wakita

2011

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose a new framework for discriminating the initial maneuver of a lane-crossing event from a driver correction event, which is the primary reason for false warnings of lane departure prediction systems (LDPSs). The proposed algorithm validates the beginning episode of the trajectory of driving signals, i.e., whether it will cause a lane-crossing event, by employing driver behavior models of the directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver correction events. The framework utilizes only common driving signals and allows the adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS framework has a detection error with as low as a 17% equal error rate. Furthermore, the proposed algorithm reduces the false-warning rate of the original lane departure prediction system with less tradeoff for the correct prediction.Index Terms-Driver adaptation, driver behavior model, lane departure, nuisance warning, time to line crossing (TLC).

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“…Lane detection and recognition based on machine vision is one of the key techniques for intelligent vehicle driving. It is the basis to implement the LDW [2] (lane departure warning) system and lane keeping of active safety functions. There are already some successful examples of the system for lane recognition, including LOIS, GOLD, RALPH, etc.…”

Section: Introductionmentioning

confidence: 99%

Research on the Algorithms of Lane Recognition based on Machine Vision

Niu¹,

Zhang²,

Li³

2015

IJIES

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Lane recognition is a kind of technology which can reconstruct either continuous or discontinuous lanes in the driving region into the continuous curves from the images of the road containing the lane markings. It is the basis and prerequisite for the whole lane departure warning system and the key technology in intelligent traffic safety assistant driving now. Generally speaking, the road images which the camera captures are processed by three steps: image preprocessing, image threshold segment and lane recognition. This paper proposed an efficient system to detect the lanes and mainly made experimental research on the images simulation of the three kinds of algorithms of the threshold segment and used the principle of Hough Transform in the lane recognition. Finally, the relatively efficient and accurate lane recognition processing algorithm was obtained according to the experimental results. Undoubtedly, this would lay a theoretical foundation for putting lane detection algorithm into practical application.

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A Lane Departure Warning System Based on Machine Vision

Cited by 42 publications

References 5 publications

Real-time lane departure warning system based on a single FPGA

Real-time lane departure warning system based on a single FPGA

On the Use of Stochastic Driver Behavior Model in Lane Departure Warning

Research on the Algorithms of Lane Recognition based on Machine Vision

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