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Analysis of Vehicle Track Prediction Method in Lane Departure Warning System

Author: HanZuo
Tutor: ZhangRunSheng；MaLei
School: Yanshan University
Course: Vehicle Operation Engineering
Keywords: Intelligent Vehicle Lane departure warning Predicted trajectory kalman filtering Matlab simulation
CLC: U495
Type: Master's thesis
Year: 2011
Downloads: 120
Quote: 0
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Abstract


With the continuous development of the Intelligent Transportation Systems (ITS) , lane departure warning has become one of the hot areas of research . Lane departure warning system perception , including the state of roads and vehicles and lane departure of two main parts of the evaluation algorithm . In this paper, based on the image processing vehicles predicted vehicle trajectory simulation and experiment in all aspects of the research study , based on the research status analysis , deviation from the early warning system in the vehicle trajectory prediction method . First , from modeling , mathematical calculations simulation software Matlab / Simulink advantages in the field of simulation . Mathematical calibration experimental vehicles , as well as the experimental environment , including image acquisition CCD calibration , road bias setting , as well as the physical parameters of the vehicle collection . Vehicle kinematics based on mathematical modeling, mathematical model based on kinematic correction by the vehicle system dynamics of the vehicle front wheel corner . Kalman filtering theory followed by observations on the state of the system to get the current system , limited - time data by linear recursive filtering method for the calculation basis , based on the mathematical model of the system , with the previous estimate and the measured values state unbiased minimum variance estimation the proposed double observer weight distribution the overlay calculation method to solve the speed of the system . By the segmentation quality of vehicle interval , establish a number of subsystems for different mass interval , and the corresponding parameter of the correction of the various subsystems , by narrowing the interval of change in the quality of vehicle , the system error is controlled within a small range . Classification introduced based on the K-means clustering algorithm , based on the Euclidean distance between the actual trajectory within the system default trajectory , online estimate of the quality of the vehicle and boot the system output . As road curvature and front wheel angle input state observer , making the method suitable for straight and curved roads and other road conditions , the scope of the application of the method . Finally , the predicted trajectory of the vehicle and the actual trajectory coincides targeted , integrated subsystem model established for each quality , system reliability of various working conditions through simulation and experimental verification .

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Computer applications in road transport and highway projects
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