A Parallelized Self-Driving Vehicle Controller Using Unsupervised Machine Learning

Abstract

In this article, a self-driving vehicle controller that optimizes the path a vehicle follows from its initial position to its destination is presented. The methods include clustering-based k-means, hierarchical, Gaussian matrix model, and self-organizing mapping. The real-time parallel implementation of the unsupervised machine learning algorithms could provide fast response times of under one microsecond during the lateral, longitudinal, and angular motion control of the autonomous vehicle. It was observed that a random selection of one of the machine learning methods may not always guarantee the optimality of the position and velocity variables as compared to the desired values. The proposed parallel implementation and optimization of the algorithms could have a significant contribution towards making transportation mobility more reliable and sustainable for future vehicular systems.