scholarly journals On the Implementation of a Semantic Model for Intelligent Vehicle Navigation

2022 ◽  
pp. 47-54
Author(s):  
Alessandro Correa Victorino ◽  
Marie-Hélène Abel
Keyword(s):  
Intelligent Vehicle ◽  
Vehicle Navigation ◽  
Semantic Model

scholarly journals The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gaining Han ◽  
Weiping Fu ◽  
Wen Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Fitness Function ◽  
Particle Swarm ◽  
Intelligent Vehicle ◽  
Vehicle Navigation ◽  
Dynamics Model ◽  
Swarm Optimization ◽  
Behavior Dynamics ◽  
Navigation Path

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

EDITORIAL: Special Issue: Intelligent Vehicle Navigation (Part1)

2008 ◽  
Vol 12 (4) ◽  
pp. 157-158 ◽  
Author(s):  
Mohammed A. Quddus ◽  
Washington Y. Ochieng ◽  
Hongchao Liu
Keyword(s):  
Intelligent Vehicle ◽  
Vehicle Navigation ◽  
Special Issue ◽  
Editorial Special Issue

Lane Markers Detection Based on Consecutive Threshold Segmentation

2011 ◽  
Vol 317-319 ◽  
pp. 881-885
Author(s):  
Huan Wang ◽  
Su Lin Shao
Keyword(s):  
Detection Method ◽  
Image Sequences ◽  
Intelligent Vehicle ◽  
Vehicle Navigation ◽  
Threshold Segmentation ◽  
Run Length ◽  
Complex Image ◽  
Multiple Threshold ◽  
Effectiveness And Efficiency ◽  
Real Complex

This paper proposed a simple and robust lane markers detection method for intelligent vehicle navigation. It needs not calculate inverse perspective map. The method uses multiple threshold segmentation instead of single threshold segmentation. And straight and curve lane markers are directly extracted in Run-Length accumulation (RLA) images. It performs well in various complex conditions and costs less than 50 ms for a 352 by 288 image. Experiments on many kinds of real complex image sequences demonstrate the effectiveness and efficiency of the proposed method.

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