Lane Recognition And Tracking Of Intelligent Electric Vehicle Based On Monocular Vision

The unmanned ground vehicle,as a set of Mechanical technology,electronic technology,automatic control technology,artificial intelligence technology,machine vision technology,new energy technology,new drive technology and many other technology as one,is a Product of the high development of computer science.It is also an important symbol to measure a country's scientific research strength and industrial level.Autonomous Navigation and intelligent driving control technology is the core technology of unmanned aerial vehicles.The complex and changeable external environment makes it the focus and difficulty of unmanned aerial vehicles.In this paper,taking the driving environment of the park as the research background,the low speed,low cost,real-time and reliable unmanned technology,The hardware and software of the motion control system of intelligent electric vehicle,the obstacle avoidance technology of multi sensor data fusion and the lane detection of monocular vision are studied.This paper mainly to lane detection algorithm,its main contents include the processing of the image,IPM view conversion,filtering,linear detection,improved Hough transform based on prior knowledge to extract the line.and RANSAC spline curve fitting,the fitting curve of the expansion.The relative position of the intelligent electric vehicle and the lane line can be determined by the lane line,and the warning of the lane departure can be realized.The fusion of the extracted information and other sensors of the environment sensing module is designed to accomplish the autonomous navigation of the intelligent electric vehicle.At the end of the lane detection algorithm was verified.The test results show that this improved lane detection algorithm is stable and accurate for vehicle occlusion,shadows of trees,uncleared lane,obvious curved lane and other typical road conditions can accurately detect the lane line,provides the feasibility of intelligent electric independent car driving in the park environment.