Next-Generation Autonomous Driving Using Deep Learning and OpenCV

Abstract

The objective of this paper is to design and conceptualize an autonomous car by integrating advanced technologies from key domains, namely Image Processing, Embedded Systems, and Neural Networking. As society increasingly demands both comfort and cutting-edge technology, our model seeks to merge these expectations by creating a vehicle that autonomously navigates complex environments with precision and safety.In this model, the autonomous car employs sophisticated image processing algorithms to interpret and respond to its surroundings, utilizing cameras as its primary sensors. These cameras continuously capture real-time data, which is then analyzed to recognize and react to various road conditions, obstacles, and traffic signals. This visual data serves as a critical input for the car's navigational system, which is guided by an embedded system designed to execute decisions in real-time. Furthermore, the car's decision-making process is powered by a neural network, trained to handle the diverse and dynamic challenges of real-world driving. This neural network enables the vehicle to learn from its experiences, improving its ability to predict and react to unforeseen circumstances over time. By incorporating machine learning techniques, the car adapts to different driving scenarios, ensuring a high level of safety and reliability. In conclusion, this paper presents a comprehensive approach to the development of autonomous vehicles, emphasizing the seamless integration of image processing, embedded systems, and neural networking. This integration aims to enhance human comfort by providing a technologically advanced solution for modern transportation needs.