Implementation of Autonomous Cars using Machine Learning

In crowded countries like India, traffic problems are a big issue because of the large population. So, autonomous driving is becoming increasingly common and has the potential to disrupt our transportation system. In addition, self-driving cars are on their way to becoming legal, but they are still not safe enough to be used in the real world due to a lack of trust. The purpose of this survey is to describe an em-pirical study on the implementation of autonomous vehicles using machine learning algorithms. Different algorithms are used in the implementation of self-driving cars. Accuracy is used as the evaluation metric. Road Lane Detection, Support Vector Machine(SVM) for anomalies detection, and Disparity Map was used as the algorithms. From the experimental analysis, this research study has observed that these machine learning models have taken less time for processing images autonomously with model accuracies of 97% for road lane detection, and SVM has shown 98% of accuracy for anomaly detection. The proposed models have outperformed baseline models with a significant difference.