[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125264-en":3,"doc-seo-125264-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125264,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning and Computer Vision Techniques in Self-driving Cars - Open access article","This study examines how computer vision and machine learning drive major advances in self-driving vehicles, focusing on algorithms for object detection, image segmentation, behavior prediction, and adaptive learning. It emphasizes evaluation through key performance metrics covering accuracy, efficiency, and safety. Validation relies on both simulated environments and real-world testing, while results point to improved traffic flow and fewer accidents. The discussion also covers future directions for robustness, human–AI interaction, edge computing, and ethical and regulatory challenges for broad adoption.","Reviews  \nMachine Learning and Computer Vision Techniques in  \nSelf-driving Cars  \n1Saja Alaam Talib   \nControl and Systems Engineering Department / Computer Engineering Branch  \nUniversity of Technology  \nBaghdad, Iraq  \n[sajaalaam@yahoo.com](sajaalaam@yahoo.com)  \nARTICLEINFO  \nArticle History  \nReceived: 16/05/2024  \nAccepted:21/06/2024  \nPublished: 30/11/2024  \nThis is an openaccess article under the CC BY  \n4.0 license:  \nABSTRACT  \nThis study explores the remarkable advancements in self-driving vehicles achieved through the application of computer vision and machine learning techniques. We examine various algorithms designed for critical functions, such as object detection, image segmentation, behavior prediction, and adaptive learning, which are all integral components of autonomous driving systems. Our research highlights key performance metrics, emphasizing accuracy, efficiency, and safety. Simulated environments and real-world testing are essential for validating the effectiveness of these methodologies. Our findings underscore the transformative potential of self-driving technology in enhancing transportation safety and its far-reaching effect on numerous industries. Notably, self-driving cars demonstrate the ability to reduce traffic accidents and improve traffic flow, which can lead to substantial economic and social benefits. Moreover, we discuss future research avenues, including the enhancement of system robustness and safety measures, the improvement of human–AI interaction, and the utilization of edge computing and edge AI. We also address the ethical and regulatory challenges associated with the widespread adoption of autonomous vehicles.  \nOur comprehensive analysis indicates that self-driving technology is poised to revolutionize the transportation sector, offering safer, more efficient, and more accessible mobility solutions. As technology continues to evolve, ongoing research and development will be crucial in overcoming current limitations and realizing the full potential of autonomous driving systems.  \nKeywords: Autonomous Vehicle, Computer Vision, Machine Learning, Path Planning, Simulation Testing. List key index terms here. No more than 5  \n1. INTRODUCTION  \nSelf-driving cars represent a transformative leap in transportation technology, with the aim of revolutionizing mobility by reducing or eliminating the need for human intervention in driving tasks. The current landscape of self-driving cars showcases remarkable advancements, with numerous companies and paper institutions actively involved in the development and testing of autonomous vehicle systems. Central to this progress is the integration of computer vision and machine learning technologies. Computer vision enables vehicles to perceive and interpret their surroundings by analyzing visual data from cameras, LiDAR, and other sensors. Concurrently, machine learning algorithms play a crucial role in empowering autonomous vehicles to make intelligent decisions based on the interpreted data, thereby enhancing their ability to navigate safely and efficiently in various environments. Consequently, the fusion of computer vision and machine learning holds immense importance in advancing the capabilities of autonomous vehicles and driving them closer to widespread adoption [1] .  \nThis integration empowers self-driving cars to recognize and respond to various elements in their environment, including other vehicles, pedestrians, cyclists, traffic signs, and road markings. Through the utilization of computer vision techniques, autonomous vehicles can accurately detect and track objects, estimate their positions and velocities, and anticipate their future movements. Furthermore, machine learning algorithms enable these vehicles to learn from experience and adapt their behavior to different driving scenarios, thereby refining their decision-making capabilities over time [2] . As technology continues to evolve, researchers and engineers are exploring innov","cbCaii9D5B30McdQ","https://ap.wps.com/l/cbCaii9D5B30McdQ","pdf",1025746,1,15,"English","en",105,"# INTRODUCTION\n## Problem of the Work","[{\"question\":\"Which techniques are central to achieving self-driving capabilities in the study?\",\"answer\":\"The study highlights computer vision and machine learning together, using visual sensor data to perceive the environment and ML to make intelligent driving decisions.\"},{\"question\":\"What functions and algorithms are discussed for autonomous driving systems?\",\"answer\":\"The document discusses algorithms for object detection, image segmentation, behavior prediction, and adaptive learning as integral parts of autonomous driving.\"},{\"question\":\"How are the proposed methodologies validated and evaluated?\",\"answer\":\"Validation uses both simulated environments and real-world testing, assessed through metrics emphasizing accuracy, efficiency, and safety.\"}]","Machine Learning and Computer Vision Techniques in Self-driving Cars - 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