Evolution Of Machine Learning Algorithms For Enhancement Of Self-Driving Vehicles Security

Abstract

In recent years, autonomous vehicles have been a hot topic of debate. Several major automakers, including many worldwide companies, are attempting to be pioneers in autonomous vehicle technology. Google Waymo, and Aptiv, for example, are all working on self-driving car technology. Radar, Lidar, sonar, GPS, and odometer are some of the technologies utilized in the development of autonomous vehicles to recognize their surroundings. An automatic control system is used to control navigation based on the data collected from these sensors. This study will look at how the CNN deep learning algorithm can be used to recognize the surrounding environment and produce the automatic navigation required for self-driving cars. The designed system will generate and learn the data set ahead of time, then use the learning outputs in an open simulation environment. By analysing the settings of an autonomous car, this simulation displays a high level of accuracy in learning to control it. This not only focused on simulation but also focused on predicting a high accuracy model which will be more scalable.