Robust Lane-Detection Method for Low-Speed Environments

Li, Qingquan; Zhou, Jian; Li, Bijun; Guo, Yongqing; Xiao, Jinsheng

doi:10.3390/s18124274

Cited by 29 publications

(20 citation statements)

References 37 publications

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“…Aly [7] use inverse perspective mapping (IPM) and RANSAC model fitting to extract lane markings. Li et al [30] use symmetrical local threshold (SLT) and Bresenham line voting space (BLVS) to locate and detect the lane markings, while the Kalman filter is used to track the key points of the linear and curved parts of the lane. Hoang et al [54] use the LSD method to make the proposed system robust to occlusion.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Li et al [29] introduced a new method of pre-processing and ROI selection using the HSV (hue, saturation, value) color transformation to extract the white features and add preliminary edge feature detection in the pre-processing stage and then select ROI on the basis of the proposed pre-processing. Li et al [30] proposed geometrical model fitting combined with feature extraction and tracking to deal with low-speed environments. However, these algorithms have not been fully implemented in the efficiency and accuracy of lane detection and do not work properly at night.…”

Section: Related Workmentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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Lane departure warning systems have gained considerable research interest in the past decade for its promising usage in automotive, where lane detection and tracking is applied. However, it is a challenging task to improve the robustness of lane detection due to environmental factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, we propose a reliable vision-based real-time lane markings detection and tracking system that can adapt to various environmental conditions. The lane detection is composed of three stages: pre-processing, Adaptive Region of Interest (AROI) setting, and lane marking detection and tracking. In the pre-processing stage, smoothing and edge detection operators are applied on input frames to automatically obtain binary images, then, lane markings segmentation are carried out. After that, An Adaptive Region of Interest is extracted to reduce the computational complexity. In the subsequent detection stages, Kalman filter is employed to track road boundaries detected in the AROI using Progressive Probabilistic Hough Transform (PPHT) in the next frame. Based on road boundaries and the vehicle's position, the proposed algorithm decides if the vehicle has drifted off the lane. For the performance evaluation of lane detection and tracking, real-life datasets for both urban roads and highways in various lighting conditions are used. Applying our method to Catltech dataset, the average correct detection rate is 93.82%. In addition, the proposed method outperforms that of the state-of-the-art methods in processing time (21.54ms/frame). INDEX TERMS Advanced driving assistance systems (ADAS), lane detection and tracking, lane departure warning system (LDWS), progressive probabilistic Hough transform (PPHT), adaptive region of interest (AROI).

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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“…This field returns the ROI. The deviation difference of the angles and the obtained region is calculated and the direction of the path is calculated [9,10]. Another method is to determine the ROI by determining the skyline.…”

Section: Finding Region Of Interest (Roi)mentioning

confidence: 99%

Untitled

2019

IJMA

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Today, unmanned vehicle technologies are developing in parallel with increasing interest in technological developments. These developments aim to improve people's quality of life. Transportation, which is a part of human life, has taken its share from this developing technology. With the development of artificial intelligence, it is aimed to provide the necessary assistance to the driver in transportation and to provide ease of driving. This development has been increased with ADAS (Advanced Driver Assistance Systems) in vehicles, but it is not possible to experience a completely driverless and comfortable road. With all these demands and conditions, autonomous vehicles have quickly attracted attention. While ADAS is a warning system, all accident risks that may arise from the driver rather than the warning to the driver in autonomous vehicles are minimized by the vehicle.In this paper, we present an autonomous vehicle prototype that follows lanes via image processing techniques, which are a major part of autonomous vehicle technology. Autonomous movement capability is provided by using various image processing algorithms such as canny edge detection, Sobel filter, etc. We implemented and tested these algorithms on the vehicle. The vehicle detected and followed the determined lanes. By that way, it went to the destination successfully.

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“…Currently, the most widely used sensor for lane detection is a camera. Lane detection technology using cameras has been mainly studied to increase its recognition rate in complex environments [1][2][3][4] and to reduce the complexity for real-time lane recognition [5][6][7][8]. However, when cameras are affected by factors, such as lighting conditions, fog, and obstacles, the lane recognition rate is degraded.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors

Kim

2020

Sensors

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An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

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Robust Lane-Detection Method for Low-Speed Environments

Cited by 29 publications

References 37 publications

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Untitled

Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors

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