Review of Anomaly Detection and Removal Techniques from Autonomous Car Data

Abstract

Artificial Intelligence and the development of computers has brought a huge change in our daily lives. Simple tasks that required humans to use precious time and energy are now being done by computers. One aspect of this is using AI in self-driving cars. By introducing such technologies in the world, it can be said that travelling has become a whole new experience. However, Artificial Intelligence cannot be considered perfect. It has its own faults and these faults can lead to undesired circumstances such as car accidents. One of the major faults in Artificial Intelligence is the presence of anomalies in data. Anomalies are values that are either too extreme to be considered, or are just not needed. Detecting these anomalies is a major task and requires algorithms that efficiently extract them. This paper discusses a few of these techniques and further highlights which ones can be used in autonomous self-driving cars.