A machine learning approach for detecting and tracking road boundary lanes

Satti, Satish Kumar; Devi, K. Suganya; Dhar, Prasenjit; Srinivasan, Padmini

doi:10.1016/j.icte.2020.07.007

Cited by 27 publications

(18 citation statements)

References 8 publications

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“…Considering the proposed method in this paper, we focus on the literature that used learning algorithms for lane detection. For instance, Satti et al [22] adopted a convolutional neural network and a Sobel edge detection algorithm using Huff transform for lanes detection in different situations, such as daytime, low-lit environments, and night. The authors used a supervised approach with a set of 4000 labeled data and employed data augmentation operations to increase the sample count and thus improve the training process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Automatic Lane Detection using a Deep Network

2022

IJE

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Lane detection is amongst the most important operations during the automatic driving process. This process aims to detect lane lines to control the vehicle's direction in a specific lane on the road and it can be effective in preventing accidents. Besides being online, the most requirement for lane detection methods in automatic driving is their high accuracy. The use of deep learning to create fully automated systems in lane detection has been done extensively. Automated learning-based methods used for road lane detection are often of supervised type. One of the disadvantages of these methods, despite their excellent accuracy, is that they need a set of labeled data, which limits the development procedure of the lane detection system, and most importantly establishing a standard set of labeled data is very timedemanding. The recommended solution is to use appropriate learning approaches that can be used to achieve the relative accuracy of the supervised approaches and to improve their speed. It also enables us to use different datasets without a constraint label that the tagged dataset would create in the algorithm development relative to the new dataset. In this research, we present an automatic semi-supervised learning method using deep neural networks to extract the data of lane lines, using labeled (mask for detected lines) and unlabeled datasets. The results demonstrate the suitable accuracy of the adopted method according to the proposed approach and also improves its computational complexity due to the significant reduction in the number of teachable network parameters.

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Section: Literature Reviewmentioning

confidence: 99%

A Method for Automatic Lane Detection using a Deep Network

2022

IJE

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“…Supervised Learning [3,4] approach, which is one of the Machine Learning [5,6,30] techniques, was used for lane tracking. With the help of a remote-control device, the robot vehicle was moved along the track and dataset collection was carried out through the camera on the vehicle.…”

Section: Corresponding Authormentioning

confidence: 99%

Realization of the Autonomous Driving System on the Experimental Vehicle

ALİYEV

GÜZEL

SEZER

2022

Academic Platform Journal of Engineering and Smart Systems

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Running control software on limited computing resources is considered one of the toughest problems. In this study, an autonomous driving software has been developed that can safely complete the map by tracking the lanes and avoiding obstacles on a robot vehicle with limited hardware components. The data was simplified with the image processing technique and the neural network was trained. Overfitting was prevented by hyperparameter tuning and synthetic data augmentation. In order to avoid obstacles, optical flow was calculated by detecting corners every 4 seconds and was used to find the focus of expansion of the vehicle. Time-to-collision was found with the FOE and the distance between the previous position and the current position of the detected point. Optimization was made by averaging the values of close points. The balance mechanism was created according to the TTC difference calculated on the right and left parts of the vehicle.

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“…Satti et al [20] applied a machine learning approach for the detection and tracking of road boundaries. Initially, ConvNet merged with 3×3 Sobel filters are utilized to identify edges.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Road Boundary for Intelligent Vehicles Based on DeepLabV3+ Architecture

Das¹,

Fime

Siddique

et al. 2021

IEEE Access

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Road boundary estimation is an essential task for autonomous vehicles and intelligent driving assistants. It is considerably straightforward to attain the task when roads are marked properly with indicators. However, estimating road boundary reliably without prior knowledge of the road, such as road markings, is extremely difficult. This paper proposes a method to estimate road boundaries in different environments with deep learning-based semantic segmentation, and without any predefined road markings. The proposed method employed an encoder-decoder-based DeepLab architecture for segmentation with different types of backbone networks such as VGG16, VGG19, ResNet-50, and ResNet-101 while handling the class imbalance problem by weighing the loss contribution of the model's different outputs. The performance of the proposed method is verified using the 'ICCV09DATA' dataset. The method outperformed other existing methods and achieved the accuracy, precision, recall, f-measure of 0.9596±0.0097, 0.9453±0.0118, 0.9369±0.0149, and 0.9408±0.0135 respectively while using RestNet-101 as a backbone network and Dice Coefficient as a loss function. The detailed experimental analysis confirms the feasibility of the proposed method for road boundary estimation in different challenging environments.

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A machine learning approach for detecting and tracking road boundary lanes

Cited by 27 publications

References 8 publications

A Method for Automatic Lane Detection using a Deep Network

A Method for Automatic Lane Detection using a Deep Network

Realization of the Autonomous Driving System on the Experimental Vehicle

Estimation of Road Boundary for Intelligent Vehicles Based on DeepLabV3+ Architecture

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