Real-time vehicle navigation using modified A∗ algorithm

2017 ◽  
Author(s):  
Snehal Nayak ◽  
Meera Narvekar
Keyword(s):  
Real Time ◽  
Vehicle Navigation ◽  
A Algorithm

Pedestrian Navigation Services: Challenges and Current Trends

GEOMATICA ◽  
2013 ◽  
Vol 67 (4) ◽  
pp. 259-271 ◽  
Author(s):  
Hassan A. Karimi ◽  
Ming Jiang ◽  
Rui Zhu
Keyword(s):  
Real Time ◽  
Road Networks ◽  
Vehicle Navigation ◽  
Main Campus ◽  
Pedestrian Navigation ◽  
Current Trends ◽  
Major Shortcoming ◽  
The University

With the success and popularity of vehicle navigation services, the demand for Pedestrian Navigation Services (PNS) has increased in recent years. PNS, while overlap in functionality with vehicle navigation services, must be designed specifically for the wayfinding and navigational needs and preferences of pedestrians. One major shortcoming of most existing PNS in outdoors is that they utilize and provide services based on road networks, resulting in PNS that do not effectively and properly track pedestrians as they usually walk on sidewalks, which have more segments and are narrower than roads. Challenges in building PNS include constructing appropriate sidewalk networks, continually tracking users in real time on sidewalks without interruption, and providing personalized routes as well as directions. In this paper, these challenges are highlighted and current trends in PNS, for both outdoors and indoors, are discussed and analyzed. A prototype PNS designed for the University of Pittsburg’s main campus sidewalk network (PNS-Pitt) is also discussed.

A Simplified Real-Time Road Network Model Considering Intersection Delay and its Application on Vehicle Navigation

2011 ◽  
Vol 58-60 ◽  
pp. 1959-1965 ◽  
Author(s):  
Zheng Yu Zhu ◽  
Wei Liu ◽  
Lin Liu ◽  
Ming Cui ◽  
Jin Yan Li
Keyword(s):  
Real Time ◽  
Network Model ◽  
Road Network ◽  
Intelligent Transportation System ◽  
Optimal Path ◽  
Network Models ◽  
Traffic Information ◽  
Vehicle Navigation ◽  
Traffic Data ◽  
New Model

The complexity of a real road network structure of a city and the variability of its real traffic information make a city’s intelligent transportation system (ITS) hard to meet the needs of the city’s vehicle navigation. This paper has proposed a simplified real-time road network model which can take into account the influence of intersection delay on the guidance for vehicles but avoid the calculation of intersection delay and troublesome collection of a city’s traffic data. Based on the new model, a navigation system has been presented, which can plan a dynamic optimal path for a vehicle according to the real-time traffic data received periodically from the city’s traffic center. A simulated experiment has been given. Compared with previous real-time road network models, the new model is much simpler and more effective on the calculation of vehicle navigation.

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.

Application of A* algorithm for real-time path re-planning of an unmanned surface vehicle avoiding underwater obstacles

2014 ◽  
Vol 13 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Thanapong Phanthong ◽  
Toshihiro Maki ◽  
Tamaki Ura ◽  
Takashi Sakamaki ◽  
Pattara Aiyarak
Keyword(s):  
Real Time ◽  
A Algorithm ◽  
Unmanned Surface Vehicle ◽  
Time Path

Advanced Vehicle Navigation applied in the BMW Real Time Light Simulation

2000 ◽  
Vol 53 (1) ◽  
pp. 30-41 ◽  
Author(s):  
J. P. Löwenau ◽  
P. J. Th. Venhovens ◽  
J. H. Bernasch
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Driving Simulator ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Route Guidance ◽  
Differential Gps ◽  
Night Time ◽  
Cd Rom ◽  
Positioning Systems

Advanced vehicle navigation based on the US Global Positioning Systems (GPS) will play a major role in future vehicle control systems. Contemporary vehicle navigation systems generally consist of vehicle positioning using satellites and location and orientation of the vehicle with respect to the roadway geometry using a digitised map on a CD-ROM. The standard GPS (with Selective Availability) enables positioning with an accuracy of at least 100 m and is sufficiently accurate for most route guidance tasks. More accurate, precision navigation can be obtained by Differential GPS techniques. A new light concept called Adaptive Light Control (ALC) has been developed with the aim to improve night-time traffic safety. ALC improves the headlamp illumination by means of continuous adaptation of the headlamps according to the current driving situation and current environment. In order to ensure rapid prototyping and early testing, the step from offline to online (real-time) simulation of light distributions has been successfully completed in the driving simulator. The solutions are directly ported to real vehicles to allow further testing with natural road conditions.

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