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Real-Time Object Detection System especially Vehicle and Lane Detection using Yolo V4 algorithm Using MATLAB and Deep Learning

Abstract

This article presents the development and implementation of a Real-Time Object Detection System, focusing on Vehicle and Lane Detection using the YOLOv4 algorithm integrated within MATLAB and Deep Learning frameworks. The primary objective of this research is to design, simulate, and evaluate an intelligent driving assistance system capable of detecting vehicles, identifying lane markings, and performing basic trajectory planning and lane change control in a highway driving scenario. The proposed system leverages a pre-trained YOLOv4 model for robust and accurate vehicle detection in real-time video streams. Lane detection is achieved through image pre-processing techniques, including grayscale conversion, edge detection, and Hough transform-based lane line extraction. Furthermore, the system incorporates trajectory planning algorithms and a basic proportional lane change controller, enabling lateral position adjustments based on detected objects and lane boundaries. A key contribution of this work is the seamless integration of object detection and lane detection outputs with control algorithms to simulate decision-making in autonomous highway driving. The performance of the object detection module is quantitatively assessed using standard metrics such as precision, recall, mean Average Precision (mAP), false positives, and false negatives. Lane detection accuracy is evaluated through Intersection over Union (IoU) metrics, demonstrating reliable lane identification even in complex scenarios. The system’s inference time was optimized to meet real-time processing requirements, achieving an average frame processing speed compatible with autonomous driving applications. Visualizations of detected vehicles, lane boundaries, and trajectory adjustments were implemented to enhance interpretability and user understanding. The experimental results validate the efficiency of YOLOv4 in vehicle detection tasks within the MATLAB environment, achieving high precision and recall rates, and demonstrate the feasibility of integrating lane detection and control mechanisms for highway lane management. However, the study also highlights areas for future work, such as enhancing the realism of vehicle dynamics models, integrating advanced decision-making algorithms, and extending the system to more complex urban environments. This research offers a foundational framework for further exploration in the field of autonomous vehicle perception systems, contributing to the development of advanced driver assistance systems (ADAS) and autonomous navigation technologies.

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Renewable Energy Policies in the European Union: Germany Example, Legal Regulations and Alternative Energy Sources

Global climate change and energy security are critical issues shaping modern energy policies. Following the Kigali Amendment, Germany and the European Union have intensified legal regulations and incentives to promote renewable energy. This study explores the historical development of legal regulations, including the Renewable Energy Sources Act and the national hydrogen strategy, alongside alternative energy sources such as electrolyzers, heat pumps, and geothermal energy. The analysis highlights Germany’s leadership in renewable energy transitions and the challenges ahead in achieving energy independence and carbon neutrality by 2050.

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Simulating Renewable Energy Systems Using Simulink: A Practical Approach with Design a Large Battery Storage System

This MATLAB Simulink model presents the design and implementation of a Large Battery Energy Storage System (BESS) aimed at alleviating peak power demands in Colombo, Sri Lanka. The system utilizes grid-forming control to facilitate power injection during peak times and incorporates a battery management system (BMS) for efficient operation. Additionally, a photovoltaic (PV) system is integrated to supplement power generation. The model encompasses various components such as converters, filters, and controllers to regulate power flow and ensure seamless integration with the grid. Detailed simulations evaluate system performance, validating the effectiveness of peak shaving strategies and compliance with relevant industry standards like IEEE 1547-2018 and IEEE 2030.2.1-2019. Results indicate successful peak shaving functionality and highlight the impact of time delays on system dynamics.

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