Deep Learning Based Self Driving Cars Using Computer Vision

Autonomous driving is a prominent topic in the fields of artificial intelligence and machine learning, with several studies being undertaken in order to bring driverless vehicles to the masses. By merging complex models and algorithms, artificial intelligence has revolutionized the field of autonomous cars. The current breakthroughs have problems because modeling architecture to reach state-of-the-art results is too sophisticated, making it excessively expensive and difficult to grasp. A selfdriving automobile is one that uses vehicular automation to sense its surroundings and move safely with little or no human intervention. Self-driving automobiles currently use the Automatic Land Vehicle in Neural Network (ALVINN) by way of a Naive approach, and it has a sophisticated model architecture that makes it difficult to interpret. As a result, a Self Driving Vehicle based on Deep Learning will improve and enhance the functionalities and performance of autonomous vehicles. To comprehend the input of potential failures in terms of safety. A successful autonomous driving system should produce accurate results, be easy to understand (for safety and reliability), and be inexpensive.