Vision-Based Unmarked Road Detection with Semantic Segmentation using Mask R-CNN for Lane Departure Warning System

Undit, Harold Japok Anak; Hassan, Mohd Fadzil Abu; Zin, Zalhan Mohd

doi:10.1109/isamsr53229.2021.9567892

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Moreover, the suggested trajectory prediction algorithms can anticipate lane change actions up to three seconds in advance and are 30% more accurate than the vehicle motion model in a time range of four seconds. Similarly, in [119], mask region CNN (mask R-CNN) is used for an LDW system. Some other works, e.g., [120], [121], [122], [123], [124], and [125], exploit CNN for the lane detection function.…”

Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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“…Traditional lane detection methods, such as edge detection and Hough transform, have shown limited success due to their sensitivity to noise, poor adaptability to varying environmental conditions, and inability to handle complex traffic scenarios [3][4][5]. With the advancement in deep learning, convolutional neural networks (CNNs) have demonstrated promising results in various computer vision tasks, including lane detection [6].…”

Section: Introductionmentioning

confidence: 99%

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

Abdrakhmanov,

Elemesova,

Zhussipbek

et al. 2023

IJACSA

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The accurate and real-time detection of road lanes is crucial for the safe navigation of autonomous vehicles (AVs). This paper presents a novel approach to lane detection by leveraging the capabilities of the Mask Region-based Convolutional Neural Network (Mask R-CNN) model. Our method adapts Mask R-CNN to specifically address the challenges posed by diverse traffic scenarios and varying environmental conditions. We introduce a robust, efficient, and scalable architecture for lane detection, which segments the lane markings and generates precise boundaries for AVs to follow. We augment the model with a custom dataset, consisting of images collected from different geographical locations, weather conditions, and road types. This comprehensive dataset ensures the model's generalizability and adaptability to real-world conditions. We also introduce a multi-scale feature extraction technique, which improves the model's ability to detect lanes in both near and far fields of view. Our proposed method significantly outperforms existing state-of-the-art techniques in terms of accuracy, processing speed, and adaptability. Extensive experiments were conducted on public datasets and our custom dataset to validate the performance of the proposed method. Results demonstrate that our Mask R-CNN-based approach achieves high precision and recall rates, ensuring reliable lane detection even in complex traffic scenarios. Additionally, our model's real-time processing capabilities make it an ideal solution for implementation in AVs, enabling safer and more efficient navigation on roads.

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A Robust Vision Based Lane Scenario Detection and Classification Using Machine Learning for SelfDriving Vehicles

Araf

Raisa

Mithika

et al. 2022

2022 25th International Conference on Computer and Information Technology (ICCIT)

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Vision-Based Unmarked Road Detection with Semantic Segmentation using Mask R-CNN for Lane Departure Warning System

Cited by 5 publications

References 22 publications

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

A Robust Vision Based Lane Scenario Detection and Classification Using Machine Learning for SelfDriving Vehicles

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