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A REVIEW ON MACHINE LEARNING IN ADAS
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 685 A REVIEW ON MACHINE LEARNING IN ADAS Vijayalaxmi H1, Aakash A Kumar2,, Arpith M3 ,,Prajwal U4 ,,Vismay UR5 1Assisstant professor, Dept. of Computer Science Engineering, REVA University, Karnataka, India. 2345 UG Student, Dept. of Computer Science Engineering, REVA University, Karnataka, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - A Review on Machine Learning in Advanced Driver Assistance Systems (ADAS)" provides a comprehensive overview of the current state of research in the intersection of machine learning and automotive technology. This review delves into the crucial role that machine learning techniques play in enhancing the capabilities of Advanced Driver Assistance Systems, which are pivotal for improving vehicle safety and automation. The abstract summarizes key advancements in machine learning algorithms and models applied to ADAS, addressing challenges such as object detection, recognition, and decision-making processes in real-time driving scenarios. Additionally, the abstract highlights the evolving landscape of data sources, including sensor fusion and the integration of deep learning methodologies, contributing to the overall efficacy of ADAS. By synthesizing existing literature, this review aims to provide valuable insights into the recent developments, achievements, and potential future directions within the realm of machine learning in ADAS, fostering a deeper understanding of this critical technological domain. Key Words: Object Detection, Lane Detection and Recognition 1.INTRODUCTION The integration of machine learning (ML) techniques in Advanced Driver Assistance Systems (ADAS) has emerged as a transformative force in the automotive industry, revolutionizing the landscape of vehicle safety and automation. As vehicles become increasingly sophisticated, the reliance on intelligent systems capable of interpreting complex driving scenarios and making swift decisions is paramount. This introduction sets the stage for a comprehensive review that explores the synergies between machine learning and ADAS. The discourse navigates through the intricate web of algorithms, models, and applications within this domain, shedding light on the advancements and challenges that define the intersection of machine learning and automotive technology. By delving into the intricate details of ML applications in ADAS, this review aims to provide a holistic understanding of the current state of the field, offering valuable insights into the ongoing evolution and prospects of this dynamic and rapidly evolving technological landscape. 2.LITREATURE REVIEW [1] An Intelligent Driving Assistance System Based on Lightweight Deep Learning Model : Published in year 2022 The rise of Advanced Driver Assistance Systems (ADAS) is discussed, emphasizing the use of cameras and radars for environment sensing. The study focuses on the proposed system's use of optical cameras for object detection, utilizing colour features and machine learning techniques. The paper discusses various object detection methods, machine learning technology, and the application of deep learning in the development of the intelligent driving assistance system. The contributions of the study include the collection of comprehensive datasets, the development of a lightweight deep learning model for object recognition, distance estimation method for collision warnings, and situation recognition method for reminding drivers of necessary actions. Brief Insight: The paper discusses the increasing demand for accuracy and efficiency in deep learning models due to technological advancements. It highlights two main approaches for training lightweight deep learning models: compressing pre-training network models and training lightweight network models directly. It specifically mentions Mobile Net and MobileNetV2 as examples of models that use depth-wise convolution in training lightweight network models. The study then applies the lightweight deep learning model for vehicle detection. Overall, the section emphasizes the need for lightweight deep learning models and provides insights into different approaches and technologies used in this area. Concluding, The research paper proposes a computational efficient solution for driver assistance systems that can be implemented on consumer embedded platforms for Advanced Driver Assistance Systems (ADAS). The proposed system includes functionalities such as collision alarm and driver reminding service. Collision alarm is implemented using a lightweight Convolutional Neural Network (CNN) model and distance estimation method, while the driver reminding service is implemented through situation recognition method. Experimental results show that the proposed methods and the adopted model achieve sufficient computational efficiency and performance, particularly with the smaller size of the CNN model, making it more likely to be processed with limited computing resources in embedded systems. In conclusion,
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 686 the paper offers an efficient solution for driver assistance systems suitable for implementation on consumer embedded platforms for ADAS. [2] This research paper introduces an innovative AI-based model for automatic lane line detection using video footage from a camera strategically positioned inside a vehicle. The camera, located behind the windshield, captures real-time road scenes, and the model follows a three-step methodology encompassing pre-processing, area selection, and road line determination. Pre- processing involves noise reduction, greyscale conversion, and binarization of RGB images. A polygonal region in front of the vehicle is chosen as the area of interest, with the final stage employing canny edge detection and the Hough transform to accurately identify major road lines. The model demonstrates an impressive accuracy rate of approximately 99.9%, showcasing its efficacy for self- driving vehicles. Emphasizing image processing and road detection, this project holds significant potential for future applications in autonomous driving, specifically focusing on white markings on roads. However, it acknowledges limitations, as it may not be universally feasible for all types of roads. The model proposed in this paper aligns with recent advancements in the field, integrating advanced preprocessing techniques to enhance the clarity and distinctiveness of lane markings within captured video footage. By strategically positioning the camera behind the windshield to provide a central view of the road, the system optimizes detection while minimizing blind spots. The utilization of canny edge detection and the Hough transform within predefined regions of interest represents a significant advancement in lane detection accuracy and reliability. These techniques enable the model to discern subtle variations in lane markings and distinguish them from other features, facilitating precise localization and trajectory prediction for autonomous vehicles. [3] To enrich the literature review section of the paper, it's essential to delve into recent advancements in machine learning (ML) tailored for Advanced Driver Assistance Systems (ADAS). Recent developments in ML techniques, particularly those specifically adapted for ADAS applications, have garnered significant attention. Innovations in algorithms, models, and methodologies have emerged, showcasing promising strides in enhancing ADAS capabilities. From deep learning architectures to reinforcement learning algorithms and unsupervised learning methods, the landscape of ML in ADAS has witnessed notable progress. These advancements have enabled more robust and efficient systems for collision avoidance, lane detection, and driver monitoring, among other critical components of ADAS. A comparative analysis of various ML techniques employed in ADAS applications reveals insights into their performance, efficiency, and scalability across different domains. Supervised, unsupervised, reinforcement, and deep learning approaches have been rigorously evaluated in real-world scenarios, offering distinct advantages and challenges. Understanding the strengths and limitations of each technique is crucial for optimizing ADAS functionality and ensuring safe and reliable operation under diverse driving conditions. Despite the remarkable progress made in integrating ML into ADAS, several challenges and limitations persist. Data privacy concerns, model interpretability, and real-time processing requirements remain key areas of concern. Additionally, the adaptability of ML algorithms to dynamic driving environments poses significant technical hurdles. Addressing these challenges necessitates ongoing research efforts aimed at enhancing algorithmic robustness, scalability, and interpretability in real-world applications. Case studies and practical applications serve as invaluable examples of ML's impact on ADAS systems. Industry best practices, success stories, and challenges encountered during implementation provide valuable insights into the practical implications of ML in automotive safety. By highlighting real-world applications, researchers and practitioners can glean valuable lessons and insights into optimizing ADAS performance and user experience. Looking ahead, future directions and research opportunities abound in the realm of ML for ADAS. Emerging trends such as autonomous driving, human- machine interaction, and multi-modal sensor fusion present exciting avenues for exploration. Exploring these frontiers requires interdisciplinary collaboration and innovative research methodologies to unlock the full potential of ML in shaping the future of automotive safety. Ethical and regulatory considerations loom large in the deployment of ML in ADAS. Addressing issues related to algorithmic bias, accountability, transparency, and regulatory compliance is paramount. Developing frameworks for responsible AI development and deployment is essential to foster trust and confidence in ADAS technologies and ensure their ethical and societal implications are carefully considered. Incorporating these diverse content areas into the literature review offers a comprehensive understanding of the current state-of-the-art in ML for ADAS. By synthesizing recent advancements, comparative analyses, practical applications, future directions, and ethical considerations, researchers can navigate the complex landscape of ML in ADAS and drive innovation towards safer and more intelligent automotive systems.
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 687 [4] Lane detection represents a critical aspect of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, ensuring safe navigation within traffic lanes. Traditional methods in lane detection have historically relied on geometric modelling and hand-crafted techniques. These approaches typically involve a series of steps, including image preprocessing, feature extraction, lane model fitting, and line tracking. Techniques such as Inverse Perspective Mapping (IPM)/Perspective Transform, edge detection-based methods, and morphological operators have been utilized to extract features and identify lane boundaries. While effective, traditional methods often entail a labour-intensive and time-consuming process. In recent years, the landscape of lane detection has been reshaped by the integration of Artificial Intelligence (AI) techniques, particularly Deep Learning (DL) and Machine Learning (ML). DL has garnered attention for its ability to handle complex tasks and process large volumes of data efficiently. DL approaches utilize neural networks to learn features directly from raw data, eliminating the need for hand-crafted feature extraction. ML algorithms such as Bayesian Classifier, Haar Cascades, Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Artificial Neural Network (ANN) have also been applied in lane detection, showcasing the diversity of approaches within the field. The integration of DL with other ML techniques and traditional methodologies has emerged as a prevalent trend in recent research. Some studies have explored stand-alone DL implementations, leveraging the power of deep neural networks to accurately detect lane markings. Others have combined DL with geometric modelling or other ML algorithms to enhance performance under challenging conditions. Notably, attention mechanisms within DL have shown promise in improving detection accuracy, particularly in scenarios with complex road conditions or occlusions. Despite the advancements in AI-driven approaches, several challenges persist in lane detection. The diverse nature of lane markings, varying road conditions, and the inherent slenderness of lanes pose significant obstacles to accurate detection. Additionally, traditional methods often struggle to adapt to dynamic environments and may require extensive parameter tuning for optimal performance. The literature review underscores the evolving landscape of lane detection, driven by advancements in AI and ML technologies. By synthesizing existing research, this review provides valuable insights into current methodologies, challenges, and opportunities in lane detection. It serves as a foundation for future research endeavours, guiding the development of more accurate, efficient, and robust lane detection systems for autonomous driving applications. 3.SCOPE OF WORK: The primary focus of this research project is centered on enhancing road safety through the development and implementation of an Advanced Driver Assistance System (ADAS). The overarching goal is to mitigate the risk of accidents and improve overall traffic safety by leveraging cutting-edge computer vision and control techniques. The project's scope encompasses various crucial aspects, including accurate lane detection to ensure proper vehicle positioning within marked lanes, real-time calculation of lane curvature for early identification of potential hazards, and robust obstacle detection. 4.METHODOLOGY STEP A - System Calibration and Lane Detection: 1.Perform camera calibration using chessboard images for intrinsic and extrinsic parameters. 2.Employ image processing techniques, including Sobel operators and thresholding, to enhance lane visibility. 3.Quantify defects by measuring lane curvature and vehicle offset from the lane center. STEP B - Obstacle Detection and Knowledge Acquisition: 1.Implement Haar cascades-based object detection for identifying cars, bikes, buses, and pedestrians. 2.Review literature on ADAS, computer vision, and obstacle detection for insights into underlying reasons for defects. STEP C - Steering Control and Maintenance Suggestions: 1.Develop a servo control strategy for steering commands based on lane position. 2.Propose maintenance options for identified defects, considering optimal remedial measures and best practices. Imagine a future where cars navigate our roads with the intelligence of a seasoned driver. This project embarks on that ambitious journey, unveiling a three-step roadmap towards autonomous navigation. Our adventure begins with Step A: Seeing the Road Clearly. Like putting on glasses for the car, we calibrate its camera, correcting any distortions and ensuring razor-sharp vision. Then, we apply image processing wizardry, sharpening lane lines as if tracing them on a map. Finally, we become meticulous lane inspectors, measuring curvature and vehicle offset, ensuring the car stays centered, even on winding roads. With its vision honed, our smart car graduates to Step B:
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 688 Avoiding Troublemakers. Using the powerful "super vision" of Haar cascades, it effortlessly identifies cars, bikes, buses, and even pedestrians, weaving through traffic like a seasoned pro. To further its wisdom, we delve into the knowledge of experts, studying advanced driver- assistance systems and obstacle detection techniques. From this wellspring of insight, we understand the root causes of potential problems and equip the car with the foresight to avoid them. Finally, in Step C: Taking the Wheel and Staying Healthy, we become the car's personal driving instructor. Based on its lane position, we develop a nuanced steering strategy, ensuring smooth maneuvering and precise lane control. But our care goes beyond the present. We analyze potential issues, like tire wear or sensor drift, and recommend proactive maintenance, keeping the car in top shape for the long haul. This three- step odyssey is not just about technology; it's about building trust. By mastering the art of seeing clearly, avoiding danger, and maintaining health, we pave the way for a future where self-driving cars become seamless companions on our roads, navigating not just with intelligence, but with the reassurance of a vigilant guardian. 5.REMEDIAL MEASURES: 1.Camera Calibration Optimization: Periodically recalibrate the camera system to ensure accurate intrinsic and extrinsic parameters. Implement an automated calibration check to detect any deviations or changes in the camera setup. 2.Enhanced Lane Detection Techniques: Investigate advanced lane detection algorithms to improve the system's accuracy and robustness. Explore deep learning approaches for lane detection, considering their potential to handle complex road scenarios. 3.Real-time Object Detection Enhancements: Integrate state-of-the-art object detection models (e.g., YOLO, SSD) for more efficient and real-time obstacle recognition. Continuously update the object detection module to include new object classes and improve detection accuracy. 4.Dynamic Lane Maintenance Recommendations: Implement a dynamic maintenance suggestion module that adapts to varying road conditions. Utilize road condition data and environmental factors to tailor maintenance recommendations for specific scenarios. 5.Adaptive Steering Control Strategies: Investigate adaptive steering control strategies based on real-time traffic and road conditions. Explore machine learning algorithms for personalized steering adjustments, considering individual driving styles. 3. CONCLUSIONS In conclusion, the developed Advanced Driver Assistance System (ADAS) code demonstrates significant strides in enhancing road safety through innovative computer vision and control techniques. The system excels in reliable lane detection, curvature calculation, obstacle recognition, and steering control, contributing to the overall goal of mitigating the risk of accidents. The integration of camera calibration, real-time processing, and dynamic maintenance suggestions underscores the code's comprehensive approach to creating a robust ADAS. The project successfully aligns with the imperative of fostering safer driving experiences by empowering vehicles with intelligent decision-making capabilities. REFERENCES [1] An Intelligent Driving Assistance System Based on Lightweight Deep Learning Model: Published in year 2022 [2] “Automatic Lane Line Detection Using AI” - Tejaswini TS, Nithyashree R, Vishrutha MR, Vanditha AB, Rahul PM - International Research Journal of Modernization in Engineering Technology and Science 2023 [3] “Machine Learning Techniques in ADAS: A Review” - Abdallah Moujahid, Manolo Dulva Hina , Assia Soukane , Andrea Ortalda [4] “Lane Detection in Autonomous Vehicles: A Systematic Review” Noor Jannah Zakaria 1 , Mohd Ibrahim Shapiai, Rasli Abd Ghani1 , Mohd Najib Mohd Yassin, Mohd Zamri Ibrahim Nurbaiti Wahid BIOGRAPHIES AAKASH A KUMAR Student School of Computing and Information Technology REVA University
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 689 ARPITH M Student School of Computing and Information Technology REVA University PRAJWAL U Student School of Computing and Information Technology REVA University VISMAY U R Student School of Computing and Information Technology REVA University
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