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An optimal path based on an artificial neural network and a path time calculation method

An artificial neural network and optimal path technology, applied in neural learning methods, biological neural network models, computing, etc., can solve problems such as single prediction results, failure to meet the ideal needs of users, and achieve strong robustness and fault tolerance, The effect of reducing runtime

Pending Publication Date: 2019-06-28
JIANGSU UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide an optimal path based on artificial neural network and a method for calculating path time, so as to solve the problems in the prior art that the prediction results are relatively single and cannot meet the ideal needs of users

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  • An optimal path based on an artificial neural network and a path time calculation method
  • An optimal path based on an artificial neural network and a path time calculation method
  • An optimal path based on an artificial neural network and a path time calculation method

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Embodiment Construction

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0052] see Figures 1 to 4 , the present invention provides a technical solution: an optimal path based on an artificial neural network and a method for calculating path time, the optimal path selection method is:

[0053] Step A, obtain the raw data of the driving time of the car and the real-time road conditions from the machine and the network;

[0054] Step B, performing neural network analysis on the driving information of the current road information in...

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Abstract

The invention relates to the technical field of path planning, in particular to an optimal path based on an artificial neural network and a method for calculating path time, and the method for selecting the optimal path comprises the following steps: step A, obtaining original data of automobile driving time and real-time road conditions from a local machine and a network; B, performing neural network analysis on the driving information of the current road information in the local machine; C, performing data acquisition and neural network analysis on similar vehicles in the Internet host; StepD, if the difference between the two results is too large, performing re-verification, preferentially taking a local result, and if the number of local sample points is too small, taking a cloud computing result; E, planning a path according to the obtained result; F, repeatedly calling the method in the driving process to shorten the operation time and avoid the congested road section; Accordingto the method for calculating the path time, the arrival time is predicted by using the steps A, B, C and D for the path of the known path and summation is carried out. The method can shorten the operation time and avoid the congested road section.

Description

technical field [0001] The invention relates to the technical field of path planning, and specifically relates to an optimal path based on an artificial neural network and a method for calculating path time. Background technique [0002] There are many factors that affect the journey time of a car, which determines that it is difficult to establish an accurate mathematical model for the optimal method of calculating the arrival time, so the current navigation software has relatively simple prediction results for the time. However, the artificial neural network can be used to obtain the path with the shortest time based on various environmental factors and the actual driving habits of the vehicle. The artificial neural network has strong robustness and fault tolerance, and its nonlinear mapping ability is suitable for solving the modeling and prediction of nonlinear and complex systems, and the prediction results of the running time of car navigation are more realistic. Con...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08
Inventor 谭琛凯贝绍轶李波杭玉迪丁月王文豪毛坤鹏薛婷陈雪俊
Owner JIANGSU UNIV OF TECH
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