Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

Hoang, Toan Minh; Hong, Hyung Gil; Vokhidov, Husan; Park, Kang Ryoung

doi:10.3390/s16081313

Cited by 38 publications

(32 citation statements)

References 25 publications

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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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Section: Introductionmentioning

confidence: 99%

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

Kim

2020

Sensors

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“…For a curve lane, few number methods used to detect curved roads such as parabola [23] and hyperbola fitting and B-Splines [29,30], Bezier Splines. To enhance the result of lane detection, the area at the bottom of an image is considered as a region of interest (ROI) [31]. Segmenting ROI will increase the efficiency of the lane detection method and eliminate the effect of the upper portion of a road image [32].…”

Section: Introductionmentioning

confidence: 99%

Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020

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The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

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“…At present, the lane detection approaches can be mainly divided into two categories, sensor-based approaches and image-processing-based approaches [2].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Hardware Implementation of Road-Lane Detection Based on Hough Transform with Parallelized Voting Procedure and Local Maximum Algorithm

Guan

Zhang

et al. 2019

IEICE Trans. Inf. & Syst.

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Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGAprototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ, θ) in Hough-space, voting procedure for each (ρ, θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time roadlane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

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Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

Cited by 38 publications

References 25 publications

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

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

Highly Curved Lane Detection Algorithms Based on Kalman Filter

Energy-Efficient Hardware Implementation of Road-Lane Detection Based on Hough Transform with Parallelized Voting Procedure and Local Maximum Algorithm

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