Intelligent Vehicle Driving Aid System Based On Machine Vision

Along with the increasing emphasis on science and technology development by the state,active automotive safety technology has also become a hot topic for people to study.The traditional automobile manufacturing industry can no longer meet the needs of the market.Automobiles with intelligent driving assistance systems are increasingly favored by the market.The market's large demand for smart cars has become a catalyst for automotive active safety technologies,and companies that research active safety technologies for automobiles have mushroomed around the world and are committed to smart car driving research.Focus on the emergence of a large number of smart car driving companies to make the industry more and more competitive.Whoever is able to develop higher-security products at the lowest cost will certainly have a place in the smart driving market.This article studies the intelligent driving assistance system based on machine vision.Smart car driving assistance systems are an important part of active car safety and a key step towards full autonomous driving.There are many types of smart vehicle driving assistance systems,and each system has different focuses on safety details.The research in this paper focuses on the identification and positioning of vehicles in front of smart driving assistance systems to achieve the purpose of collision warning.Accurate detection of vehicles in front can not only provide driving protection for the driver of the vehicle,but also lays a foundation for the further development of emergency braking systems.It can be said that vehicle identification plays a decisive role in smart driving.This paper mainly studies the front vehicle detection technology based on machine vision.The research focuses on improving the success rate of vehicle detection in front,improving the detection speed and realizing the real-time warning.In a nutshell,the research conducted in this article includes the following aspects:For onboard camera calibration,a camera calibration system based on transmission projection was studied.The proposed system meets the requirements of the product aftermarket,specifically:inspired by the three-line calibration method,using the transmission projection principle,the three-line calibration method needs The three lines parallel to each other are transmitted to a plane in the air,thereby reducing the calibration area required for camera calibration.After repeated calibration,the correctness of this set of calibration scheme was verified.The projection method based on projection projection greatly simplifies the calibration work and lays a solid foundation for the expansion of the aftermarket.For vehicle detection,what kind of methods can be used to achieve vehicle detection more efficiently on the basis of existing hardware.After in-depth research and development,a vehicle detection scheme combining statistical learning and image features is developed.Specifically,statistical learning,because the algorithm runs for a long time,this method only provides the initial vehicle position for vehicle detection.After the position is confirmed,it is processed by the image feature algorithm to accurately detect and track the vehicle position.After a large number of simulation tests and actual road tests,the test results can be used for the study of alarm algorithms,verifying the accuracy of the program.For the collision warning,the application of Kalman filtering in the alarm module is studied,specifically:positioning and tracking of the detection vehicle,real-time detection of the position of the vehicle using calibration data,and Kalman filter to filter and calibrate the vehicle position information detected in real time.To ensure that the vehicle position will not be misaligned due to inaccurate positioning of the vehicle when performing collision warning judgment.