Lane Detection Based on Connection of Various Feature Extraction Methods

Li, Mingfa; Li, Yuanyuan; Jiang, Min

doi:10.1155/2018/8320207

Cited by 31 publications

(11 citation statements)

References 24 publications

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“…Image smoothing is done to blur the noisy details diminishing the impact of pixels which are not part of the lane markings. The most widely used smoothing filters for road lane extraction are Gaussian filter [16], [17] and Median filter [18] or both [19], [20]. It can be seen in Figure 2 (c, d), that both median and Gaussian filters blur out noise and unnecessary details, yet they can sometimes eradicate information crucial for lane detection, such as in Figure 2(c) lane markings information is lost.…”

Section: ) Image Smoothing Sharpening and Shadow Removalmentioning

confidence: 99%

Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

et al. 2021

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Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, 'computer-vision-based approach' and 'imitation-learning-based approach'. To the best of our knowledge, at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons.

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Section: ) Image Smoothing Sharpening and Shadow Removalmentioning

confidence: 99%

Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

et al. 2021

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“…Niu et al [2], [28] used a modified Hough transform to extract lane profile segments and used DBSCAN (Density-Based Spatial Clustering of applications with noise) clustering algorithm. 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.…”

Section: Related Workmentioning

confidence: 99%

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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“…Then, a tracking Bouguet (2001) is used to validate and detect the lanes. In Li et al (2018), the authors proposed another method based on the connection of various feature extraction. The main goal is to use the HSV color space to extract the white features of the road.…”

Section: Straight Lanesmentioning

confidence: 99%

Bayesian curved lane estimation for autonomous driving

Fakhfakh

Chaâri

Fakhfakh

2020

J Ambient Intell Human Comput

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Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also doesn't meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection. Keywords Autonomous driving • embedded camera • road marking • multi-hyperbola • Bayesian framework

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Lane Detection Based on Connection of Various Feature Extraction Methods

Cited by 31 publications

References 24 publications

Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Bayesian curved lane estimation for autonomous driving

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