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Revolutionary Enhanced Lane Departure Detection Techniques for Autonomous Vehicle Safety using ADAS.

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 9
Year of Publication : 2024
Authors : S. D. Vidya Sagar, C. J. Prabhakar
10.14445/23488549/IJECE-V11I9P103
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How to Cite?

S. D. Vidya Sagar, C. J. Prabhakar, "Revolutionary Enhanced Lane Departure Detection Techniques for Autonomous Vehicle Safety using ADAS.," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 9, pp. 22-35, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I9P103

Abstract:

This paper introduces an Enhanced Lane Departure Warning System (ELDWS) leveraging cutting-edge vision technology to boost road safety for ADAS enabled vehicles. Our novel approach utilizes a combination of Phase Stretch Transform for edge detection, Curved and Straight Lane Detectors for accurate lane Detection, and a modified Kalman Filter for dynamic lane tracking, collectively aimed at improving vehicle safety through precise lane departure estimation. Unlike traditional systems that rely heavily on clear lane markings and favorable environmental conditions, our model excels in various lighting and road scenarios, including curved paths and challenging weather conditions. The research demonstrates the system's efficacy in real-world simulations, where it outperforms existing technologies in detecting and alerting potential lane departures. Through meticulous integration of advanced image processing techniques and machine learning algorithms, our model offers a significant leap towards achieving robust lane-keeping assistance in autonomous and semi-autonomous vehicles. Furthermore, the paper discusses the system's ability to adapt to different environmental conditions and road types, making it a versatile tool for enhancing driving safety. By addressing the limitations of current LDWS technologies, such as sensitivity to weather conditions and the reliance on high-contrast lane markings, our approach sets a new standard for safety in the autonomous driving domain. This paper shows that by overcoming the problems of older lane departure warning systems, like their struggle with bad weather and dependency on clear road markings, our system sets a new standard for keeping cars safely within their lanes, especially in self-driving and semi-self-driving cars. We offer a detailed solution that combines new detection methods with advanced tracking techniques for better accuracy and reliability. The accuracy of our ELDWS is quantitatively measured using a custom dataset, revealing impressive results. During daytime conditions, our system achieved a high accuracy rate of 95.81%, correctly detecting lanes in 7665 out of 8000 frames. This demonstrates the system's robustness in optimal lighting conditions. The accuracy remains commendable in left and right departure scenarios during the day, with rates of 83.26% and 84.83%, respectively, showcasing the system's capability to recognize lane departures effectively. This research is important for making cars safer and provides useful information for developing better driving assistance systems in the future.

Keywords:

ADAS, Curved and Straight lane detector, Enhanced lane departure warning system.

References:

[1] Noor Jannah Zakaria et al., “Lane Detection in Autonomous Vehicles: A Systematic Review,” IEEE Access, vol. 11, pp. 3729-3765, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xingyu Zhou et al., “Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach,” IEEE/ASME Transactions on Mechatronics, vol. 28, no. 6, pp. 3017-3028, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Manav Garg, Apeksha Sehrawat, and P. Savaridassan, “Vehicle Lane Detection for Accident Prevention and Smart Autodrive Using OpenCV,” 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Steffen Maurer et al., “Designing A Guardian Angel: Giving an Automated Vehicle the Possibility to Override its Driver,” Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 341-350, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Vijay Gaikwad, and Shashikant Lokhande, “Lane Departure Identification for Advanced Driver Assistance,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 910-918, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Em Poh Ping et al., “Vision-based Lane Departure Warning Framework,” Heliyon, vol. 5, no. 8, pp. 1-18, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jongin Son et al., “Real-Time Illumination Invariant Lane Detection for Lane Departure Warning System,” Expert Systems with Applications, vol. 42, no. 4, pp. 1816-1824, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jinsheng Xiaoet al., “Lane Detection Based on Road Module and Extended Kalman Filter,” Image and Video Technology: 8th Pacific-Rim Symposium, PSIVT 2017, Wuhan, China, pp. 382-395, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] A.V. Vinuchandran, and R. Shanmughasundaram, “A Real-Time Lane Departure Warning and Vehicle Detection System using Monoscopic Camera,” 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kerala, India, pp. 1565-1569, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wenshuo Wang et al., “A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model,” IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9145-9157, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yassin Kortli, Mehrez Marzougui, and Mohamed Atri, “Efficient Implementation of a Real-Time Lane Departure Warning System,” 2016 International Image Processing, Applications and Systems (IPAS), Hammamet, Tunisia, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Kaiming He, Jian Sun, and Xiaoou Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Abdelhamid Mammeri, Azzedine Boukerche, and Zongzhi Tang, “A Real-Time Lane Marking Localization, Tracking and Communication System,” Computer Communications, vol. 73, pp. 132-143, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Marcos Nieto et al., “Real-Time Lane Tracking using Rao-Blackwellized Particle Filter,” Journal of Real-Time Image Processing, vol. 11, pp. 179-191, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mohamed Aly, “Real Time Detection of Lane Markers in Urban Streets,” 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands, pp. 7-12, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Davy Neven et al., “Towards End-to-End Lane Detection: An Instance Segmentation Approach,” 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, pp. 286-291, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Tobias Kühnl, Franz Kummert, and Jannik Fritsch, “Spatial Ray Features for Real-Time Ego-Lane Extraction,” 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, USA, pp. 288-293, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fang Zheng et al., “Improved Lane Line Detection Algorithm Based on Hough Transform,” Pattern Recognition and Image Analysis, vol. 28, pp. 254-260, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Uppalapati Vamsi Krishna et al., “Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation,” International Research Journal of Multidisciplinary Technovation, vol. 6, no. 2, pp. 51-69, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ankita Kamble, and Sandhya Potadar, “Lane Departure Warning System for Advanced Drivers Assistance,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 1775-1778, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Maurício Braga de Paula, and Claudio Rosito Jung, “Real-Time Detection and Classification of Road Lane Markings,” 2013 XXVI Conference on Graphics, Patterns and Images, Arequipa, Peru, pp. 83-90, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[22] E.V.N.Jyothi et al., “A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency,” Journal of Electrical Systems, vol. 19, no. 4, pp. 336-349, 2023.
[Google Scholar] [Publisher Link]
[23] Christian Brynning, A. Schirrer, and S. Jakubek, “Transfer Learning for Agile Pedestrian Dynamics Analysis: Enabling Real-Time Safety at Zebra Crossings,” Synthesis: A Multidisciplinary Research Journal, vol. 1, no. 1, pp. 22-31, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] J. Lampkins, Z. Huang, and Radwan, “Multimodal Perception for Dynamic Traffic Sign Understanding in Autonomous Driving,” Frontiers in Collaborative Research, vol. 1, no. 1, pp. 22-34, 2023.
[Google Scholar] [Publisher Link]