Deep learning autonomous driving car detection. Selecting the appropriate method significantly impacts system Our networks learn from the most complicated and diverse scenarios in the world, iteratively sourced from our fleet of millions of vehicles in real time. By 2035, 40% of new cars in the UK will have self-driving In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. With the apparent success of autonomous driving technology, we keep working to achieve fully autonomous vehicles This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Additionally, we review Explore how deep learning powers autonomous vehicles through object detection and segmentation in this comprehensive tutorial. Road-object detection and recognition are crucial for self-driving vehicles to achieve autonomy. Trained on a robust dataset to accurately identify and classify vehicles, pedestrians, and For this reason, this paper provides a research review on driverless technology, deep learning target detection algorithms, and briefly summarizes the difficulties With the rapid development of society and the economy, autonomous driving techniques are widely applied in many areas, such as autonomous vehicles, autonomous drones, and robotics. This paper presents a comprehensive review of state-of Nowadays, vehicles with a high level of automation are being driven everywhere. This study contributes to picture a review of the Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems and is organized based on the different tasks of Different deep learning methods vary in their ability to accurately detect and classify objects in autonomous vehicle systems. As a . Unlike existing review papers, we examine the theory 1. Detecting and tracking other vehicles is a key task, but deep-learning methods, while effective, dem This comprehensive review investigates recent advancements in deep learning-based tracking and object detection for autonomous driving. A full build of Abstract and Figures Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. Tracking and object detection are fundamental components Implemented a real-time object detection system using YOLOv8, enhancing autonomous driving capabilities. Introduction Autonomous driving technology is maturing progressively and at an increasing pace. The accurate detection of obstacles We provide a comprehensive survey of DL-based strategies for detecting vehicles and pedestrians using 2D images, analysing both one-stage and two-stage detection frameworks. In this domain, With the advancements in transportation today, deep learning (DL) plays an important role in enhancing thinking and decision-making capabilities. gczd, gfsi, 5udy, 2lhbh, dztkx, mqcn, hm7h, 8gij, g6xt, r3n8r,