A survey on machine vision in self-driving cars with a case study over recent crash statistics in USA

Machine vision is a field of artificial intelligence thatgenerally enables computers to understand the dynamics andpower of visual analysis of the humankind. By using algorithmsand techniques in machine vision, the machines are able torecognize and identify patterns and different features of imagesand videos. This technology is used in various fields such as facerecognition, medical image analysis, traffic detection and even inself-driving cars. Nowadays, it is widely used in the automotiveindustry, notably in self-driving cars. By collecting and processingvision data, self-driving cars have the ability to detect obstaclesaround them, identify road signs, and even anticipate ahead. Thistechnology enhances the security as well as the functionalcapabilities of the cars and allows them to move on the roadsautomatically, without human intervention. The purpose of thisresearch is to investigate how machine vision is used in self-drivingcars and how its performance can be evaluated. We exploreadvanced machine vision methods in self-driving cars and evaluatetheir performance and efficiency in different conditions. As a casestudy, we go through the California and US Self-Driving VehicleCrash Statistics as well as Traffic Safety Administration data to getinspired how machine vision in self-driving cars has affected thecommunity so far. With regard to that, we analyze two self-drivingcar accident statistics reported in USA.

Keywords:

artificial intelligence , computer vision , machine vision , self-driving , autonomous vehicles , machine learning