Road condition analysis method based on artificial intelligence and vehicle-mounted intelligent equipment

Abstract

The invention relates to a road condition analysis method based on artificial intelligence and vehicle-mounted intelligent equipment. The method comprises the following steps: acquiring current position information of a user and acquiring all possible road sections of the user based on a driving direction of the user; the real-time traffic flow Cari of any possible passing road section at the current moment is obtained, i represents the ith possible passing road section, the value range of i is [1, I], and I is the total number of the possible passing road sections; calculating and obtaining traffic flow congestion coefficients of all possible passing road sections based on the real-time traffic flow Cari; calculating the passing trend degree of the user to any possible passing road section based on the traffic flow congestion coefficients of all possible passing road sections; and judging whether the passing trend degree of any possible passing road section is lower than a first threshold value, and if yes, performing traffic jam risk warning on the possible passing road section. Effective assistance can be provided for driving safety of a user, the relative real-time performance of congestion analysis is high, and the use experience of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent travel, and particularly to a road condition analysis method based on artificial intelligence and an in-vehicle intelligent device. Background Art

[0002] Road congestion is a common problem faced by modern cities, especially in densely populated cities. It not only affects people's daily travel but also has a profound impact on the environment, economy, and quality of life.

[0003] Existing road condition analysis methods often rely solely on the depth of the path color given by the map APP to judge the congestion situation, and then enable users to reasonably select travel routes. Although this method can handle some application scenarios, its real-time performance is relatively low. It often requires a large number of feedbacks from APP users to be effectively updated, which is likely to affect the user experience in scenarios with high real-time requirements for users. Summary of the Invention

[0004] The purpose of the present invention is to at least solve one of the deficiencies of the prior art, and provide a road condition analysis method based on artificial intelligence and an in-vehicle intelligent device.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] Specifically, a road condition analysis method based on artificial intelligence is proposed, including the following:

[0007] Obtain the user's current location information and obtain all possible passing roads of the user based on the user's driving direction;

[0008] Obtain the real-time traffic flow Car of any possible passing road at the current moment i , where i represents the i-th possible passing road, and the value range of i is [1, I], and I is the total number of possible passing roads;

[0009] Based on the real-time traffic flow Car i Calculate and obtain the traffic flow congestion coefficients of all possible passing roads;

[0010] Calculate the passing trend degree of the user going to any possible passing road based on the traffic flow congestion coefficients of all possible passing roads;

[0011] Judge whether the passing trend degree of any possible passing road is lower than the first threshold. If so, give a traffic jam risk warning for the possible passing road.

[0012] Furthermore, specifically, obtaining the user's current location information and obtaining all possible passing roads of the user based on the user's driving direction includes

[0013] By calling a mature map APP, the current location information of the user is obtained based on the positioning module, as well as the driving direction of the user in the map, and all possible sections that the user may pass through are obtained according to the driving direction of the user in the map.

[0014] Further, specifically, the real-time traffic flow Car of any possible section to pass through at the current moment is obtained through traffic monitoring cameras, floating car data, traffic sensors or by calling the API interface of the map APP i 。

[0015] Further, specifically, the map APP called is Amap.

[0016] Further, specifically, based on the real-time traffic flow Car i Calculate and obtain the traffic congestion coefficient of all possible sections to pass through, including,

[0017]

[0018] where Tra_j i represents the traffic congestion coefficient of the i-th possible section to pass through.

[0019] Further, specifically, based on the traffic congestion coefficients of all possible sections to pass through, calculate the degree of traffic trend for the user to go to any possible section to pass through,

[0020]

[0021] where Pa_tr i represents the degree of traffic trend for the user to go to the i-th possible section to pass through.

[0022] Further, the method further includes, after calculating the degree of traffic trend for the user to go to any possible section to pass through, visualizing the degree of traffic trend of all possible sections to pass through in the map scene displayed by calling the map APP.

[0023] The present invention also proposes an in-vehicle intelligent device, which applies the above-mentioned traffic condition analysis method based on artificial intelligence. The in-vehicle intelligent device includes the following:

[0024] A location acquisition module, configured to acquire the current location information of the user and acquire all possible sections that the user may pass through based on the driving direction of the user;

[0025] A traffic flow acquisition module, configured to acquire the real-time traffic flow Car of any possible section to pass through at the current moment i , where i represents the i-th possible section to pass through, and the value range of i is [1, I], and I is the total number of possible sections to pass through;

[0026] A congestion coefficient calculation module, which is used to calculate and obtain the traffic congestion coefficients of all possible passing roads based on the real-time traffic flow Car i Calculate and obtain the traffic congestion coefficients of all possible passing roads;

[0027] A passing trend degree calculation module, which is used to calculate the passing trend degree of the user going to any possible passing road based on the traffic congestion coefficients of all possible passing roads;

[0028] An alarm module, which is used to judge whether the passing trend degree of any possible passing road is lower than the first threshold. If so, a traffic jam risk alarm is given for the possible passing road.

[0029] Furthermore, the in-vehicle intelligent device further includes,

[0030] A visualization display module, which is used to visually display the passing trend degrees of all possible passing roads in the map scene displayed by calling the map APP after calculating the passing trend degrees of the user going to any possible passing road.

[0031] The beneficial effects of the present invention are:

[0032] The present invention provides a road condition analysis method and an in-vehicle intelligent device based on artificial intelligence. By calling a mature map APP such as Amap to obtain the current location information of the user and based on the driving direction of the user to obtain all possible passing roads of the user, the passing trend degrees of the user going to each possible passing road are calculated through a preset algorithm processing flow, and then the passing trend degrees are compared with a preset first threshold to judge whether there is a traffic jam risk, and an alarm reminder is given for the possible passing roads with traffic jam risk, which can effectively assist the user's driving safety, and the congestion analysis is relatively real-time, which can improve the user experience. Description of the Drawings

[0033] By elaborating on the embodiments shown in conjunction with the drawings, the above and other features of the present disclosure will become more obvious. The same reference numerals in the drawings of the present disclosure represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0034] Figure 1 The flowchart of the road condition analysis method based on artificial intelligence of the present invention is shown. Detailed Embodiments

[0035] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in combination with embodiments and the accompanying drawings to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0036] Example 1. Referring to Figure 1 , the present invention proposes a road condition analysis method based on artificial intelligence, including the following:

[0037] Step 110: Obtain the user's current location information and obtain all possible sections that the user may pass through based on the user's driving direction;

[0038] Step 120: Obtain the real-time traffic flow Car of any possible section at the current moment i where i represents the i-th possible section, and the value range of i is [1, I], and I is the total number of possible sections;

[0039] Step 130: Calculate and obtain the traffic congestion coefficient of all possible sections based on the real-time traffic flow Car i ;

[0040] Step 140: Calculate the degree of passage trend for the user to go to any possible section based on the traffic congestion coefficients of all possible sections;

[0041] Step 150: Determine whether the degree of passage trend of any possible section is lower than the first threshold (the first threshold is set manually and can be specifically set after a large number of actual tests). If so, give a traffic jam risk warning for that possible section.

[0042] In this Example 1, by calling a mature map APP such as AutoNavi Map to obtain the user's current location information and obtain all possible sections that the user may pass through based on the user's driving direction, calculating the degree of passage trend for the user to go to each possible section through a preset algorithm processing flow, then comparing the degree of passage trend with the preset first threshold to determine whether there is a traffic jam risk, and giving an alarm reminder for the possible sections with a traffic jam risk, it can provide effective assistance for the user's driving safety, and the congestion analysis is relatively real-time, which can improve the user experience.

[0043] As a preferred implementation manner of the present invention, specifically, obtaining the user's current location information and obtaining all possible sections that the user may pass through based on the user's driving direction includes,

[0044] By calling a mature map APP, the current location information of the user and the driving direction of the user in the map are obtained based on the positioning module, and all possible sections that the user may pass through are obtained according to the driving direction of the user in the map.

[0045] As a preferred embodiment of the present invention, specifically, the real-time traffic flow Car of any possible section to pass through at the current moment is obtained through traffic monitoring cameras, floating car data, traffic sensors or by calling the API interface of the map APP. i 。

[0046] As a preferred embodiment of the present invention, specifically, the called map APP is Amap.

[0047] As a preferred embodiment of the present invention, specifically, based on the real-time traffic flow Car i calculate and obtain the traffic congestion coefficient of all possible sections to pass through, including,

[0048]

[0049] where Tra_j i represents the traffic congestion coefficient of the i-th possible section to pass through.

[0050] As a preferred embodiment of the present invention, specifically, based on the traffic congestion coefficients of all possible sections to pass through, calculate the degree of traffic trend for the user to go to any possible section to pass through,

[0051]

[0052] where Pa_tr i represents the degree of traffic trend for the user to go to the i-th possible section to pass through.

[0053] In this preferred embodiment, through the above calculation method, the higher the traffic flow of any section, the lower its traffic congestion coefficient will be. At this time, the calculated degree of traffic trend will also be lower. If the degree of traffic trend is lower than a certain level, that is, the first threshold, it means that the traffic flow is too high compared to other sections. Therefore, an alarm is issued and it is not recommended to take this section, so as to make a reasonable driving plan for the user.

[0054] As a preferred embodiment of the present invention, the method further includes, after calculating the degree of traffic trend for the user to go to any possible section to pass through, visually display the degree of traffic trend of all possible sections to pass through in the map scene displayed by calling the map APP.

[0055] In this preferred embodiment, a visualization service is also provided, which can visually display the degree of traffic trend of all sections, so that users can know the comprehensive information and make reasonable arrangements.

[0056] The present invention also provides an in-vehicle intelligent device that applies the above-described road condition analysis method based on artificial intelligence. The in-vehicle intelligent device includes the following:

[0057] A location acquisition module, configured to acquire the user's current location information and all possible road segments that the user may pass through based on the user's driving direction;

[0058] A traffic flow acquisition module, configured to acquire the real-time traffic flow Car of any possible road segment at the current moment i , where i represents the i-th possible road segment, and the value range of i is [1, I], and I is the total number of possible road segments;

[0059] A congestion coefficient calculation module, configured to calculate and obtain the traffic flow congestion coefficients of all possible road segments based on the real-time traffic flow Car i ;

[0060] A traffic trend degree calculation module, configured to calculate the traffic trend degree of the user going to any possible road segment based on the traffic flow congestion coefficients of all possible road segments;

[0061] An alarm module, configured to determine whether the traffic trend degree of any possible road segment is lower than a first threshold, and if so, give a traffic jam risk alarm for that possible road segment.

[0062] As a preferred embodiment of the present invention, the in-vehicle intelligent device further includes

[0063] A visualization display module, configured to, after calculating the traffic trend degree of the user going to any possible road segment, visualize and display the traffic trend degrees of all possible road segments in the map scene displayed by calling a map APP in the map APP.

[0064] In addition, in each embodiment of the present invention, the various functional modules may be integrated into one processing module, or each module may exist physically alone, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0065] When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or system, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc., that can carry the computer program code.

[0066] Although the description of the present invention has been quite detailed and several of the described embodiments have been described in particular, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be regarded as providing a broad interpretation of these claims in light of the prior art by reference to the appended claims, thereby effectively covering the intended scope of the present invention. In addition, the present invention has been described above in terms of embodiments foreseeable by the inventor for the purpose of providing a useful description, and non-substantive modifications to the present invention that are not currently foreseeable may still represent equivalent modifications of the present invention.

[0067] As described above, these are only the preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. As long as it achieves the technical effects of the present invention by the same means, it should fall within the protection scope of the present invention. Within the protection scope of the present invention, various different modifications and variations can be made to its technical solutions and / or embodiments.

Claims

1. A road condition analysis method based on artificial intelligence, characterized in that: These include: Get the user's current location information and obtain all possible routes the user may have passed based on the user's driving direction; Get the real-time traffic flow of any possible road section at the current time i , where i represents the i-th possible road section, the value range of i is [1, I], and I is the total number of possible road sections; Based on real-time traffic flow i Calculate and obtain the traffic congestion coefficient of all possible road sections; Calculate the degree of traffic tendency of users to any possible road section based on the traffic congestion coefficient of all possible road sections; Determine whether the traffic trend of any possible road section is lower than a first threshold, and if so, issue a traffic jam risk warning for the possible road section.

2. The method for analyzing road conditions based on artificial intelligence according to claim 1, characterized in that: Specifically, the user's current location information is obtained and all possible road sections that the user may have passed through are obtained based on the user's driving direction. include, By calling a mature map APP based on the positioning module, the user's current location information and the user's driving direction on the map are obtained, and all possible sections of the road that the user may have passed are obtained based on the user's driving direction on the map.

3. The road condition analysis method based on artificial intelligence according to claim 2 is characterized in that: Specifically, the real-time traffic flow of any possible road section at the current moment can be obtained through traffic monitoring cameras, floating car data, traffic sensors or calling the API interface of the map APP. i .

4. The method for analyzing road conditions based on artificial intelligence according to claim 3, characterized in that: Specifically, the map APP called is Amap.

5. The road condition analysis method based on artificial intelligence according to claim 1, characterized in that: Specifically, based on real-time traffic flow Car i Calculate and obtain the traffic congestion coefficient of all possible road sections, including: Among them, Tra_j i Represents the traffic congestion coefficient of the i-th possible road section.

6. The method for analyzing road conditions based on artificial intelligence according to claim 5, characterized in that: Specifically, the traffic congestion coefficient of all possible road sections is used to calculate the degree of traffic tendency of the user to any possible road section. Among them, Pa_tr i It indicates the degree of the user's tendency to go to the i-th possible road segment.

7. The method for analyzing road conditions based on artificial intelligence according to claim 6, characterized in that: The method also includes, after calculating the degree of traffic tendency of the user to any possible road section, visually displaying the traffic tendency degrees of all possible road sections by calling a map APP and displaying the map scene in the map APP.

8. The vehicle-mounted intelligent device is characterized in that: The road condition analysis method based on artificial intelligence according to any one of claims 1 to 7 is applied, and the vehicle-mounted intelligent device includes the following: A location acquisition module is used to obtain the user's current location information and obtain all possible road sections that the user may pass through based on the user's driving direction; The vehicle flow acquisition module is used to obtain the real-time vehicle flow of any possible road section at the current moment. i , where i represents the i-th possible road section, the value range of i is [1, I], and I is the total number of possible road sections; Crowding coefficient calculation module, used to calculate the traffic volume based on real-time traffic flow i Calculate and obtain the traffic congestion coefficient of all possible road sections; A traffic trend degree calculation module is used to calculate the traffic trend degree of the user to any possible road section based on the traffic congestion coefficient of all possible road sections; The alarm module is used to determine whether the traffic trend of any possible road section is lower than a first threshold value. If so, a traffic jam risk alarm is issued for the possible road section.

9. The vehicle-mounted intelligent device according to claim 8, characterized in that: The vehicle-mounted intelligent device also includes: The visualization display module is used to calculate the traffic trend degree of any possible road section that the user may pass through, and then visualize the traffic trend degree of all possible road sections in the map scene displayed in the map APP by calling the map APP.

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