Machine Learning based Enhanced Autonomous Driving for Autonomous Vehicles

In particular, the emphasis of our study is on the most important difficulties associated with finding data-driven representations of nonlinear systems that allow for linear analysis is a key step towards deriving dynamics from data. Many issues plaguing modern dynamical systems are explored and the innovative methods being created in data science and machine learning to address them. Nonlinear dynamics, on the one hand, and unknown or only partially understood dynamics, on the other, are the two greatest challenges. Machine learning is providing cutting-edge, effective answers to all of these issues. Methods of reducing the number of dimensions are used to visualize dynamical processes in a more compact format. It is computationally efficient to use these methods on real-world data. Finding the fundamental equations fundamental principles of physics requires models that are themselves driven by evidence. By using deep learning techniques, dynamical systems are identified correctly, and that in turn enables accurate inference about physical systems. Several cutting-edge and very successful new approaches to nonlinear dynamics haw emerged from the area of machine learning. Autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are only some of the cutting-edge deep learning approaches used to simulate dynamical systems.