Three-feature based automatic lane detection algorithm (tfalda) for autonomous driving

Yim, Young Uk; Oh, Se‐Young

doi:10.1109/tits.2003.821339

Cited by 116 publications

(5 citation statements)

References 19 publications

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“…Lane features such as edges are extracted from the input image. [14][15][16], and Bertozzi et al extracted features using region filters [17]. From the extracted features, line detection selects lane features and removes non-lane features.…”

Section: General Methods For Vision-based Lane Detectionmentioning

confidence: 99%

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Lee¹,

Park

2022

Electronics

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Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements.

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Section: General Methods For Vision-based Lane Detectionmentioning

confidence: 99%

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Lee¹,

Park

2022

Electronics

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“…Multiple studies have proposed methods to extract lane marking extraction for autonomous driving and advanced driver assistance systems (ADAS) with less focus placed on actually refining those results. Early attempts to extract lane marking information were based on handcrafted features including geometric attributes, intensity information, and color (22)(23)(24)(25)(26)(27)(28). These features were used to set specific thresholds and segment lane marking information.…”

Section: Previous Workmentioning

confidence: 99%

“…For instance, authors in ( 22 ) and ( 23 ) perform lane detection based on both grayscale and RGB images. Images are also used in ( 25 ) and ( 26 ) to extract lane features based on intensity information. In works such as ( 24 ) and ( 28 ), particle filtering is used for lane detection and tracking.…”

Section: Previous Workmentioning

confidence: 99%

Automatically Refining Degraded and Occluded Lane Marking Information to Enhance the quality of High Definition Maps for Autonomous Vehicles

Gargoum,

Gargoum

2023

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicle technology has been advancing over the past decade with many challenges and obstacles. One obstacle to achieving fully autonomous driving is the feasibility of efficiently producing robust high definition (HD) maps of existing road infrastructure in a sustainable manner. HD maps are digital twins of road infrastructure that include 3-D representation of road features critical to a vehicle’s ability to navigate a road and maintain a consistent trajectory. This includes lane markings, barriers, and road edges. HD maps are often produced by first surveying roads using remote sensing technology. The collected data are then segmented using computer vision and machine learning algorithms to produce an HD map of the features of concern. One common challenge that is specific to extracting lane marking information is that the markings are occasionally occluded or degraded by high traffic volumes. This results in failure to detect lane markings and the existence of significant gaps in the extracted information. These gaps are manually filled in by quality control staff in a tedious and time-consuming process. To help overcome such challenges, this paper proposes a novel algorithm through which Kalman filtering is combined with Bézier curve fitting to automatically refine lane marking information. The proposed algorithm is tested on multiple roads where mobile lidar data were collected and segmentation was first applied using deep learning technology. The proposed algorithm was then used to refine the lane marking information which helped close all the gaps and recreate missing portions of the extracted lane markings.

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“…Ref. [2] uses three-feature based method to detect and track lane markers, which can only adapt to the slow varying of lanes. Color segmentation method is applied to preprocess and highlight lane pixels in [3,4], but it is sensitive to the change in ambient light color.…”

Section: Introductionmentioning

confidence: 99%

Lane Markers Detection Based on Consecutive Threshold Segmentation

Wang

Shao

2011

AMR

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This paper proposed a simple and robust lane markers detection method for intelligent vehicle navigation. It needs not calculate inverse perspective map. The method uses multiple threshold segmentation instead of single threshold segmentation. And straight and curve lane markers are directly extracted in Run-Length accumulation (RLA) images. It performs well in various complex conditions and costs less than 50 ms for a 352 by 288 image. Experiments on many kinds of real complex image sequences demonstrate the effectiveness and efficiency of the proposed method.

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Three-feature based automatic lane detection algorithm (tfalda) for autonomous driving

Cited by 116 publications

References 19 publications

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Automatically Refining Degraded and Occluded Lane Marking Information to Enhance the quality of High Definition Maps for Autonomous Vehicles

Lane Markers Detection Based on Consecutive Threshold Segmentation

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