Application of Machine learning Algorithms in Autonomous Vehicles Navigation System

Abstract

Autonomous vehicles are the future of transportation and it also expected to become a fully-fledged reality within a decade. The major giants in the automotive industry like Toyota with Microsoft and Amazon, Mercedes with Bosch, Audi with Huawei, etc. are hard pressing their transition from conventional vehicle to autonomous vehicles. The state of Karnataka, for instance, had approximately 205,200 registered taxis higher than Madhya Pradesh 174,900 registered cabs from 2014 to 2015. This results in higher traffic congestion, pollution and high fuel consumption due to unorganized driving practice. As a consequence, this presents a great deal of opportunities for autonomous cars as they can significantly reduce the accident rate and improve effective and stress free parking, optimal running time, fuel economy etc. It also reduces traffic congestion as all the autonomous cars can be synchronized together through cloud. But the crucial part is their navigation system. Although a number of sophisticated forms of technology like such as radars, lasers, and high definition cameras for mapping, localization, obstacle detection, etc, are used, autonomous vehicles are still trying to reach perfection to navigate precisely under uninformed terrains and dynamics obstacles on the way like pedestrians, erratic drivers, traffic and climatic conditions. Events like heavy snow and rain, improper lane marking makes it hard to detect an obstacle on the road for evasive maneuver within limited response time period. Machine learning is one of the fast growing technologies that provide optimal solution to overcome the challenges in the autonomous navigation system. This paper focuses on the application of different machine learning algorithms and compares the use, challenge and scope of the same. The paper also focuses on the future direction of machine learning with reference to autonomous navigation system.