Experimental of Vision-based Lane Markings Segmentation Methods in Lane Detection Application

Ping, Em Poh; Hossen, J.; Imaduddin, Fitrian; Wong, Eng Kiong; Sabino, Ubaidillah

doi:10.25103/jestr.121.22

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…The results showed that the real-time lane detection method on the ground of improved fusion and enhanced sampling algorithm achieved satisfactory lane detection results in lane detection frames under different road conditions. Evaluation metrics include detection rate, false positive rate (FP), and false negative rate (FN) [7]. T. T. Zhang and other researchers proposed a longitudinal scanning line LiDAR camera model to solve the problem of extracting the trajectory of urban main road intersections.…”

Section: Related Workmentioning

confidence: 99%

“…By using these parameters, the collected point cloud data can be projected onto the image to complete the registration between the point cloud and the image. The projection equation is represented as equation (7).…”

Section: Binternal Point Extraction Of Traffic Markings On the Ground...mentioning

confidence: 99%

“…In equation (7), 0 u and 0 v represent the center of the image, and x f and y f serve as the normalized focal lengths of the x and y axes. L and T serve as the rotation matrix and translation matrix, while x , y , and z represent the point cloud coordinates.…”

Section: Binternal Point Extraction Of Traffic Markings On the Ground...mentioning

confidence: 99%

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Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

Wu,

Mao

2024

IEEE Access

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With the continuous development of urban traffic and the improvement of intelligent transportation systems, the accurate extraction and analysis of road traffic markings becomes more and more important. At present, road traffic marking is extracted by collecting dense point cloud data with highthread LiDAR. However, due to the high cost of the high-thread LiDAR and the large amount of point cloud data, the production efficiency of high-precision maps is low, and updating is complex and difficult. Therefore, a traffic marking extraction algorithm based on image and point cloud data fusion is proposed. The innovation of this study lies in the introduction of image processing for traffic marking edge detection, and the adaptive threshold segmentation of point cloud data using the maximum inter class variance method. The algorithm first uses an edge detection algorithm on the ground of the Canny operator to extract edge information of traffic markings from road images. Meanwhile, it uses the maximum inter class variance method to extract the internal point coordinate information of traffic markings from point cloud data. Subsequently, clustering segmentation, geometric correction and other techniques are used to process the fused data, thereby achieving automated extraction of road traffic markings. The results show that on the TuSimple dataset, the false detection rate and missed detection rate of the edge detection algorithm on the ground of the Canny operator are only 3.84% and 2.79%, respectively. Meanwhile, in the TuSimple dataset, the peak signal-to-noise ratio and structural similarity of the maximum inter class variance method were as high as 39.25% and 9.23%, respectively. In addition, the average time it takes for the algorithm proposed by the research institute to extract the edge points of a single road traffic marking is only 5.69ms. Especially, the proposed method achieved a maximum improvement of 14.07% in the accuracy of traffic marking edge detection. The method proposed by the research institute effectively reduces false positives and missed detections, and can accurately and efficiently extract the edges of road traffic markings, providing a new and reliable solution for the automated extraction of road traffic markings.

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Section: Related Workmentioning

confidence: 99%

Section: Binternal Point Extraction Of Traffic Markings On the Ground...mentioning

confidence: 99%

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Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

Wu,

Mao

2024

IEEE Access

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“…The current lane departure warning system mainly consists of the environment detection component, namely the vision sensor, which detects the lane edge, lane mark-ing, and road contour [9]. Lane departure warnings are usually applicable only on highways with distinct lane markings, and the systems may be affected due to invalid activity conditions on the road.…”

Section: Introductionmentioning

confidence: 99%

Lane Departure Warning Estimation Using Yaw Acceleration

2020

Self Cite

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Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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Experimental of Vision-based Lane Markings Segmentation Methods in Lane Detection Application

Cited by 2 publications

References 19 publications

Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

Traffic Marking Extraction Algorithm Based on Image and Point Cloud Data

Lane Departure Warning Estimation Using Yaw Acceleration

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