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Real-time path planning method based on improved kstar algorithm and deep learning

A technology of real-time path planning and deep learning, which is applied in the fields of computer science and intelligent transportation systems, can solve the problems of high computational complexity of dynamic real-time planning algorithms, inability to cope with real-time traffic environment, and aggravate traffic pressure, etc., to achieve enhanced detection and tracking ability, reduce the number of planning, and reduce the effect of noise interference

Active Publication Date: 2022-08-05
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0003] Real-time optimal route planning is an important component of the intelligent transportation field, which can effectively relieve traffic pressure and reduce traffic congestion. However, the existing simple dynamic real-time planning has been unable to cope with the complex real-time traffic environment, and the complex dynamic real-time planning algorithm is computationally complex. At the same time, the existing real-time path planning system usually calculates the same path repeatedly and needs to re-plan the path after the path estimate exceeds the threshold, which will take more time
Most of the existing road evaluation formulas simply measure the time it takes to pass the road or the length of the road. There is a section of the road that is relatively congested but still guides users to this section, which will increase the pressure on traffic

Method used

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  • Real-time path planning method based on improved kstar algorithm and deep learning
  • Real-time path planning method based on improved kstar algorithm and deep learning
  • Real-time path planning method based on improved kstar algorithm and deep learning

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Embodiment Construction

[0059] A real-time path planning method based on the improved Kstar algorithm and deep learning provided by the present invention will be further described below with reference to the accompanying drawings.

[0060] see Figure 1-2 , the flow chart of this method is shown, and the specific steps are as follows:

[0061] Step (1): Set the update cycle, generally set to five minutes;

[0062] Step (2): obtain the target position according to the navigation request of the user, obtain the current position of the user according to the GPS satellite;

[0063] Step (3): Load the map between the current position and the target position and the relevant historical information of the included roads, use the improved Kstar algorithm to obtain k candidate paths, and layer the nodes on the map, according to the scope of provinces and cities. Hierarchical network. In step (3), the following steps are further included:

[0064] Step (3.1): Determine and mark the starting point s and the...

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Abstract

The invention discloses a real-time path planning method based on improved Kstar algorithm and deep learning. The present invention combines the k-shortest paths problem into real-time path planning, finds k excellent candidate paths quickly and efficiently by improving the Kstar algorithm, and proposes a more reasonable road evaluation formula, which comprehensively considers the time and cost required to pass the road. The congestion index of the road can relieve the traffic pressure while finding the optimal path. Using the improved Kstar algorithm, the road map is regionalized, which greatly improves the speed of heuristic search, constructs efficient data structures such as heap and path structure graph, and optimizes unnecessary memory waste. In the case of approaching, multiple candidate paths can be quickly obtained, effectively reducing the number of unnecessary navigation route planning.

Description

technical field [0001] The invention relates to the technical fields of computer science and intelligent transportation systems, in particular to a real-time path planning method based on improved Kstar algorithm and deep learning. Background technique [0002] With the development of cities and the popularization of vehicles, road congestion has become a major difficulty in traffic route planning. The waste of time and space and economic costs caused by congestion are increasing. Reasonable real-time optimal route planning has become an urgent development in the field of intelligent transportation. need. [0003] Real-time optimal route planning is an important component in the field of intelligent transportation, which can effectively relieve traffic pressure and reduce traffic congestion. However, the existing simple dynamic real-time planning has been unable to cope with the complex real-time traffic environment, and the complex dynamic real-time planning algorithm is co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
CPCG05D1/0276
Inventor 袁友伟周威炜葛云阳鄢腊梅
Owner HANGZHOU DIANZI UNIV
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