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Driving path planning method and system based on accident risk cost

A technology for accident risk and path planning, applied in data processing applications, forecasting, instruments, etc., can solve problems such as strong subjectivity, few path induction models, and inaccurate and objective evaluation, so as to overcome excessive subjectivity and reduce traffic accidents Risk, the effect of improving driving safety

Pending Publication Date: 2022-05-24
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the method of questionnaire survey has a large subjectivity, which is greatly affected by the risk preference and experience of the scoring experts. It is highly subjective, and the lack of data support leads to inaccurate and objective evaluations.
[0012] In terms of route planning research, conventional route guidance systems often take the minimum mileage or travel time as the optimal goal. Although there have been researches on route guidance that consider driver choice preferences and driving safety, the route construction based on the risk cost of road accidents There are still few studies on induction models

Method used

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  • Driving path planning method and system based on accident risk cost
  • Driving path planning method and system based on accident risk cost
  • Driving path planning method and system based on accident risk cost

Examples

Experimental program
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Effect test

Embodiment 1

[0048] This embodiment provides a driving path planning method based on accident risk cost;

[0049] like figure 1 As shown, the driving path planning method based on accident risk cost includes:

[0050] S101: Obtain historical traffic accident data, and preprocess the data;

[0051] S102: According to historical traffic accident data, establish an accident risk cost evaluation index system;

[0052] S103: Determine the weight of each indicator, and determine the accident risk cost of each road section according to the accident risk quantification model;

[0053] S104: Construct an example network, and based on the starting point, the ending point, the accident risk cost of each road section, and the travel time of each road section, K shortest path solving algorithms are used to obtain the optimal path.

[0054] Further, the S101: obtain historical traffic accident data; wherein, the historical traffic accident data includes: state descriptions such as personnel, vehicles...

Embodiment 2

[0134] This embodiment provides a driving path planning system based on accident risk cost;

[0135] Driving path planning system based on accident risk cost, including:

[0136] an acquisition module, which is configured to: acquire historical traffic accident data, and preprocess the data;

[0137] An evaluation index system establishment module, which is configured to: establish an accident risk cost evaluation index system according to historical traffic accident data;

[0138] an accident risk cost determination module, which is configured to: determine the weight of each index, and determine the accident risk cost of each road section according to the accident risk quantification model;

[0139] The optimal path solving module is configured to: construct a calculation example network, and use K shortest path solving algorithms to obtain the optimal path based on the starting point, the ending point, the accident risk cost of each road section, and the travel time of eac...

Embodiment 3

[0144] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in the memory, when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in the first embodiment.

[0145] It should be understood that, in this embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors, DSPs, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices. , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or th...

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PUM

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Abstract

The invention discloses a driving path planning method and system based on accident risk cost, and the method comprises the steps: obtaining historical traffic accident data, and carrying out the preprocessing of the data; establishing an accident risk cost evaluation index system according to historical traffic accident data; determining the weight of each index, and determining the accident risk cost of each road section according to an accident risk quantification model; and constructing an example network, and based on the starting point, the terminal point, the accident risk cost of each road section and the travel time of each road section, adopting a K shortest path solving algorithm to obtain an optimal path. According to traffic accident data, a risk evaluation system is constructed from accident characteristics, an accident risk quantification model based on an entropy weight method is established, and a path planning algorithm comprehensively considering risk cost and passing time is designed, so that route guidance is provided for driving of a driver, and driving safety is improved.

Description

technical field [0001] The present invention relates to the technical field of path planning, in particular to a driving path planning method and system based on accident risk cost. Background technique [0002] The statements in this section merely mention background related to the present invention and do not necessarily constitute prior art. [0003] Advanced driver assistance systems can provide drivers with route planning and intelligent guidance. However, the conventional route planning mostly takes the minimum driving mileage or driving time as the optimization goal, and there are relatively few studies based on the risk cost of road accidents. [0004] In recent years, there have been many research results on accident risk, which can be roughly divided into two categories: [0005] 1) Research on road traffic safety evaluation based on traffic accident data, using Bayesian network, accident rate method and other methods; [0006] 2) Establish an index evaluation s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06
CPCG06Q10/047G06Q10/0635G06Q10/06393
Inventor 王旭廖小棱周童景峻万青松房宏基迟猛
Owner SHANDONG UNIV
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