A robust, real-time and calibration-free lane departure warning system

Gamal, Islam; Badawy, Abdulrahman; Al-Habal, Awab; Adawy, Mohammed E. K.; Khalil, Keroles K.; El-Moursy, Magdy A.; Khattab, Ahmed

doi:10.1016/j.micpro.2019.102874

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Autonomous driving refers to the process of autonomously completing environmental perception and action execution for which the visual based environmental perception is an important source of information. Its main technologies are: detection of vehicles, pedestrians, and nonmotorized vehicles on the road [71] [72] , traffic sign detection [73], lane detection [74], departure warning [75], drivable area detection [76],3D detection [77] [78], map 3D reconstruction [79], and object ranging [80], etc. Among them, lane detection is an important link to realize autonomous driving.…”

Section: Intelligent Drivingmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

186

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Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

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Section: Intelligent Drivingmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

186

View full text Add to dashboard Cite

show abstract

“…A lane departure identification method used three lane-related parameters, including the Euclidean distances between every two points of the Hough origin H o , the midpoints mp 1 and mp 2 of the identified left and right lane-markings to identify the state of departure [11][12][13]. Besides, algorithms judging the (ρ, θ) patterns or just one of the detected left, and right lane-markings determined the left or right lane departure situation [14][15][16][17][18][19][20][21][22][23][24]. The recent study conducted by Lin et al determines lane departure also by the information of the detected lane-markings only, and it uses a state machine to recognize the "left," "right," and "normal" status, which can reduce the false alarms when the lane-marking is blocked by obstacles [3].…”

Section: Related Workmentioning

confidence: 99%

Lane Departure Assessment via Enhanced Single Lane-Marking

Luo

Shi

et al. 2022

Sensors

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Vision-based Lane departure warning system (LDWS) has been widely used in modern vehicles to improve drivability and safety. In this paper, a novel LDWS with precise positioning is proposed. Calibration strategy is first presented through a 3D camera imaging model with only three parallel and equally spaced lines, where the three angles of rotation for the transformation from the camera coordinate system to the world coordinate system are deduced. Then camera height is calculated compared to the previous works using a measured one with potential errors. A criterion for lane departure warning with only one of the two lane-markings is proposed to estimate both yaw angle and distance between the lane-markings and the vehicle. Experiments show that calibration strategy can be easily set up and achieve an average of 98.95% accuracy on the lane departure assessment.

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“…Noise smoothing Gaussian filter to remove the noise of the mounted camera is dealt in [15]. The image pyramid approach is adopted in [16] to diminish the details and to present the high-frequency data. A four-level Gaussian pyramid model is employed to reduce the image dimensions and to make the edge drawing lines algorithm works effectively for the lowest resolution image at the top level of pyramids.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Ghanem

Kanungo

Panda

et al. 2021

Complex Intell. Syst.

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Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

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A robust, real-time and calibration-free lane departure warning system

Cited by 11 publications

References 7 publications

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Lane Departure Assessment via Enhanced Single Lane-Marking

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

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