Why Software Architecture Matters?

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.

Abstract — This work investigates the leader-follower formation control of multi-robot systems, an important field in robotics with broad applications in agriculture, logistics, and surveillance. The work illustrates the efficacy of geometric, potential field, and behavior-based control techniques in producing and sustaining desirable shapes through a thorough implementation utilizing the Turtlesim simulator in ROS. The technological difficulties posed by dynamic and unstructured environments are discussed, emphasizing the ongoing need for advancements in coordination and control algorithms [2] [3]. This work critically assesses the ethical ramifications of using autonomous robots in larger social contexts, going beyond their technical limitations. Safeguarding privacy to preserve sensitive data, removing algorithmic bias to ensure fairness, addressing the socioeconomic effects of potential employment displacement, and assuring safety to prevent accidents are among the main challenges [5]. The project intends to pave the road for the responsible and sustainable integration of these technologies into society, ensuring they contribute positively while reducing potential negative effects, by including ethical considerations into the design and deployment of autonomous robots.

Keywords — autonomous robots, geometric control, multi-robot systems, ROS, Turtlesim, algorithmic bias, safety, privacy, ethical concerns, urban deployment, autonomous navigation, robotic coordination, leader-follower formation control.

Author: Waqas Javaid

Introduction:

In this project, a groundbreaking single-phase bidirectional current-source AC/DC converter tailored for Vehicle-to-Grid (V2G) applications is unveiled. The converter is ingeniously designed; comprising a line frequency commutated unfolding bridge and an interleaved buck-boost stage. Notably, the semiconductor losses within the line frequency commutated unfolding bridge contribute to the converter’s commendable efficiency. The interleaved buck-boost stage further enhances performance with its dual capabilities of buck and boost operating modes, facilitating seamless operation across a broad battery voltage range [2]. The interleaved structure of this stage significantly reduces battery current ripple. Beyond these advantages, the converter ensures sinusoidal input current, bidirectional power flow, and the capability for reactive power compensation. This project delves into the intricate topology and operational principles of this innovative converter, shedding light on its potential impact in the realm of V2G applications.

In the dynamic and ever-evolving field of electrical engineering, effective project management is crucial for ensuring successful outcomes. However, even experienced project managers can fall…