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Research on Key Technologies of Intelligent Vehicle Control

Author: YuShaoWei
Tutor: CaoKai
School: Shandong University of Technology
Course: Transportation Planning and Management
Keywords: Intelligent Vehicle Dynamic target position Lane changing and overtaking Traffic data stream
CLC: U463.6
Type: Master's thesis
Year: 2008
Downloads: 469
Quote: 3
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Abstract


With the rapid increase in car ownership , traffic safety problems have become increasingly prominent, intelligent vehicle came into being . It is a multidisciplinary field of engineering, artificial intelligence theory and technology of the cross and synthesis, is the future trend of vehicle development . Some positive and useful exploration in intelligent vehicle control technology , the purpose is the reality of the theory and technical support for the application of research and development of China 's automotive safety driver assistance systems . The control system is the \Used the golden ratio - based approach to generate the first cloud , TSCY reasoning model as a reasoning mechanism of the controller in the cloud sub-region using radial basis function neural network approximation driving equation so that the controller has a learning function , the membership of the input value is part of the promoter region of a cloud a random confidence as the rule , the random output of the nonlinear system is weighted and local random output . Autonomous driving auxiliary navigation is the key technology of intelligent vehicle research . This paper presents the concept of dynamic target position and the corresponding control mechanism , dynamic target position control mechanism as the basis of intelligent vehicle control , intelligent control of the vehicle's lateral dynamic obstacle avoidance , automatic lane changing and overtaking , and variable-speed intelligent the vehicle control is preferable to simulate the characteristics of the movement of the vehicle in the actual traffic environment and driver behavior . Intelligent vehicle control is not an isolated system , traffic signal control strategy a direct impact on the efficiency of intelligent vehicle control and affect the entire intelligent transportation system . Use STREAM algorithm of intersection traffic data stream clustering analysis , clustering result of the reaction of the different characteristics of the realistic traffic conditions , and then the clustering results of data mining and traffic data stream trend forecasting . Finally , on the basis of the data flow values ??predicted results were analyzed on a given day forecast traffic flow value algorithm based on cloud model , has been more flexible control strategy .

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