An automatic parking method and system for intelligent connected vehicles

Abstract

The application relates to the technical field of automatic driving, in particular to an automatic parking method and system of an intelligent networked automobile, wherein the method comprises the following contents: S100, navigation information is acquired; the navigation information comprises destination input content and parking lot selection results; S200, according to the parking lot selection results, image data of a corresponding parking lot is acquired, and idle parking spaces of the parking lot are analyzed; S300, according to the destination input content, image data of the destination is acquired; S400, walking routes between each idle parking space and the destination are analyzed, building structures on each walking route are analyzed, and walking route analysis results are generated; S500, according to the walking route analysis results, an idle parking space is screened as a target parking space; and S700, the vehicle is controlled to be parked in the target parking space. By adopting the scheme, the vehicle can be parked in the target parking space.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to an automatic parking method and system of an intelligent networked vehicle. BACKGROUND

[0002] With the popularity of cars, the problem of parking difficulty is increasingly prominent. Although people search for a destination through navigation, the navigation system will automatically provide parking lots near the destination for the driver to choose a parking location. However, the driver still needs to find an idle parking space by himself after driving to the selected parking lot. For underground parking lots, an indicator light is usually provided above each parking space to remind drivers of the idle condition of the parking space in the parking lot. However, for outdoor parking lots, it is not convenient to install an indicator light above each parking space, so people cannot know where the idle parking space is when parking outdoors, resulting in a lot of time spent searching for a parking space after arriving at the parking lot.

[0003] In addition, the road in the parking lot is often narrow and crowded, and occasionally needs to be changed, which wastes a lot of time. The driver finds an idle parking space after searching for a long time in the parking lot, but finds that it is far from the destination, and finds that there are actually many idle parking spaces when walking to the destination, so there is no need to change the parking space.

[0004] Therefore, how to accurately find an idle parking space after arriving at a parking lot, and then select a target parking space with a short distance from the destination to the parking location as the target parking space to improve the parking efficiency and shorten the time for the driver to reach the actual destination is an urgent problem to be solved. SUMMARY

[0005] The present application provides an automatic parking method and system of an intelligent networked vehicle, which can accurately find an idle parking space after arriving at a parking lot, and then select a target parking space with a short distance from the destination to the parking location as the target parking space and automatically park, thereby improving the parking efficiency and shortening the time for the driver to reach the actual destination.

[0006] The present application provides a basic scheme one:

[0007] An automatic parking method of an intelligent networked vehicle, comprising the following contents:

[0008] S100, acquiring navigation information; the navigation information comprises destination input content and parking lot selection result;

[0009] S200, acquiring image data of the corresponding parking lot according to the parking lot selection result, and analyzing the idle parking space of the parking lot;

[0010] S300, acquiring image data of the destination according to the destination input content;

[0011] S400, analyzing walking routes between each idle parking space and the destination, and building structures on each walking route, and generating a walking route analysis result; the walking route analysis result includes total distances corresponding to each walking route and unavoidable rain distances.

[0012] S500, selecting an idle parking space as a target parking space according to the walking route analysis result;

[0013] S700, controlling the vehicle to park in the target parking space.

[0014] Further, S300 includes:

[0015] S301, obtaining image data of the destination;

[0016] S302, analyzing whether it is raining at the destination according to the image data of the destination, and if so, performing S400, and if not, performing S303;

[0017] S303, analyzing whether the ground of the destination is waterlogged according to the image data of the destination, and if so, performing S400;

[0018] Further, S300 includes:

[0019] In S303, if not, performing S600.

[0020] Further, S600 includes:

[0021] S601, obtaining garage images of the parking lot according to the parking lot selection result;

[0022] S602, analyzing whether there are obstacles on each idle parking space according to the garage images of the parking lot, and excluding idle parking spaces with obstacles;

[0023] S603, analyzing distances between vehicles on both sides of each remaining idle parking space according to the garage images of the parking lot, and generating a distance sorting table;

[0024] S604, selecting an idle parking space with the largest distance between vehicles on both sides in the distance sorting table as the target parking space.

[0025] Further, S500 includes:

[0026] S502, calculating a distance average of each walking route according to total distances corresponding to each walking route;

[0027] S503, generating a distance threshold according to the distance average of each walking route;

[0028] S504: Filter the available parking spaces corresponding to the walking routes with a total distance less than the distance threshold and the shortest non-rain shelter distance, and select them as the target parking spaces.

[0029] Furthermore, S100 includes:

[0030] S101, Obtain the destination input content;

[0031] S102, based on the destination entered, analyze the parking lots within the preset range of the destination and generate a list of parking lots for the destination;

[0032] S103, retrieve parking lot selection results.

[0033] The second basic solution provided by this invention is an automatic parking system for intelligent connected vehicles, which uses the above-mentioned automatic parking method for intelligent connected vehicles.

[0034] The principle and advantages of this invention are as follows: Based on the parking lot selection results, image data of the corresponding parking lot is acquired, and the vacant parking spaces are analyzed, thereby achieving intelligent positioning of vacant parking spaces. Furthermore, the walking routes between each vacant parking space and the destination are analyzed. On the one hand, the distance of each walking route can be analyzed; on the other hand, in rainy weather or when the ground is flooded, the structure of buildings along each walking route can be analyzed to determine whether drivers and passengers will get wet or experience inconvenience from stepping on flooded ground after getting out of the car. Based on the walking route analysis results, different filtering methods are used to filter vacant parking spaces. Specifically, when it is not raining and the road surface is not flooded, priority is given to vacant parking spaces that are close to the destination, easy to get out of the car, have a large distance between cars on both sides, and have no obstructions as target parking spaces; when it is raining or the road surface is flooded, priority is given to vacant parking spaces that are convenient to walk to and relatively close as target parking spaces. In summary, this solution can intelligently and accurately locate available parking spaces upon arrival at the parking lot, select the closest parking space to the destination as the target parking space, and automatically park the car, thereby improving parking efficiency and shortening the time for drivers and passengers to reach their actual destination. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating an automatic parking method for an intelligent connected vehicle according to an embodiment of the present invention. Detailed Implementation

[0036] The following detailed description illustrates the specific implementation method:

[0037] Example 1:

[0038] Example 1 is basically as shown in the appendix. Figure 1 As shown:

[0039] An automatic parking method for intelligent connected vehicles includes the following:

[0040] S100, Obtain navigation information; the navigation information includes the destination entered and the parking lot selection result;

[0041] S100 includes:

[0042] S101, Obtain destination input content; In this example, the destination is entered by the driver or passenger on the in-vehicle smart terminal, thereby obtaining the destination input content.

[0043] S102, based on the destination entered, analyze the parking lots within the preset range of the destination and generate a list of parking lots for the destination; in this embodiment, the preset range is within a radius of 500 meters centered on the destination.

[0044] S103, Obtain parking lot selection result. In this embodiment, the driver or passenger selects any parking lot from the list of destination parking lots through the in-vehicle smart terminal, thereby obtaining the parking lot selection result.

[0045] S200 acquires image data of the corresponding parking lot based on the parking lot selection results and analyzes the available parking spaces.

[0046] S300: Enter the destination information to obtain image data of the destination.

[0047] The S300 includes:

[0048] S301, acquire image data of the destination.

[0049] S302: Based on the image data of the destination, analyze whether it is raining at the destination. If yes, proceed to S400; otherwise, proceed to S303. Specifically, use image recognition technology to analyze the weather conditions of the destination.

[0050] S303: Based on the image data of the destination, analyze whether there is water accumulation on the ground at the destination. If yes, proceed to S400; otherwise, proceed to S600. Specifically, use image recognition technology to analyze the weather conditions at the destination.

[0051] S400: Analyze the walking routes between each available parking space and the destination, as well as the building structures along each walking route, and generate walking route analysis results. These results include the total distance and the unavoidable rain shelter distance for each walking route. Specifically, based on the location of each available parking space and the location of the destination, analyze the walking routes between them and the total distance of each walking route; then acquire road images along each walking route to identify the building structures, including whether the buildings have roofs or other obstructions, and calculate the distance along the walking route without any obstructions providing rain shelter, outputting the unavoidable rain shelter distance.

[0052] S500: Based on the pedestrian route analysis results, select an available parking space as the target parking space.

[0053] The S500 includes:

[0054] S502, calculate the average distance of each walking route based on the total distance of each walking route; for example, including walking route 1, with a total distance of 100 meters and an unavoidable distance of 50 meters, walking route 2, with a total distance of 140 meters and an unavoidable distance of 10 meters, and walking route 3, with a total distance of 120 meters and an unavoidable distance of 30 meters, the average distance is 120 meters.

[0055] S503, generate a distance threshold based on the average distance of each walking route; in this embodiment, the average distance multiplied by 1.1 is the distance threshold, which is 132 meters.

[0056] S504, filter the vacant parking spaces corresponding to the walking routes with a total distance less than the distance threshold and the shortest non-rain shelter distance, and select them as target parking spaces; in this embodiment, the vacant parking space corresponding to walking route 3 is selected as the target parking space.

[0057] S600: Obtain the distance between vehicles on the left and right sides of each available parking space, generate a distance sorting table, and select an available parking space as the target parking space.

[0058] The S600 includes:

[0059] S601: Based on the parking lot selection result, obtain the garage image of the parking lot.

[0060] S602, based on the parking garage images, analyze whether there are obstacles in each vacant parking space, and eliminate vacant parking spaces with obstacles. This eliminates invalid parking spaces due to clutter, and also eliminates parking spaces where adjacent vehicles are parked beyond the lines, making parking difficult and prone to collisions.

[0061] S603, Based on the parking garage image, analyze the distance between vehicles on the left and right sides of the remaining vacant parking spaces and generate a distance sorting table; In this embodiment, image recognition technology is used to identify the distance between vehicles on the left and right sides of the vacant parking spaces, specifically identifying the distance between the closest ends of the vehicles on the left and right sides of the vacant parking spaces.

[0062] S604: Select the available parking space with the largest distance between vehicles on both sides from the spacing sorting table. This will provide sufficient space for drivers and passengers to open their doors, preventing them from scraping against vehicles on either side.

[0063] S700, control the vehicle to park in the target parking space. In this embodiment, after the vehicle arrives at the destination parking lot and navigation ends, the automatic driving mode is activated, and the vehicle is controlled to drive to the vicinity of the target parking space and park in the target parking space.

[0064] In this embodiment, the image data of the parking lot is acquired in real time. If changes in the available parking spaces are detected, the analysis is performed again to update the best parking space in real time.

[0065] An automatic parking system for intelligent connected vehicles uses the aforementioned automatic parking method for intelligent connected vehicles.

[0066] Example 2:

[0067] The basic principle of Example 2 is the same as that of Example 1, except that in S500 of Example 2, the following is also included:

[0068] S501: Acquire in-vehicle images, identify the types of shoes worn by the driver and passengers, and analyze whether the types of shoes worn by the driver and passengers include pre-stored shoe types. If yes, proceed to S505; otherwise, proceed to S502. The pre-stored shoe types include leather shoes, lace shoes, and high heels.

[0069] S505 filters available parking spaces along the shortest walking route that offers no shelter from rain, selecting these as target parking spaces. This allows for the direct selection of the shortest route, even when drivers and passengers' shoes cannot get wet or walking on slippery surfaces poses a safety risk, thus reducing travel risks and costs for drivers and passengers.

[0070] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An automatic parking method for intelligent connected vehicles, characterized in that: Includes the following: S100, Obtain navigation information; the navigation information includes the destination entered and the parking lot selection result; S200: Based on the parking lot selection results, acquire the image data of the corresponding parking lot and analyze the available parking spaces. S300: Enter the destination information to obtain image data of the destination; S400 analyzes the walking routes between each available parking space and the destination, as well as the building structures along each walking route, and generates walking route analysis results; The walking route analysis results include the total distance and unavoidable rain distance for each walking route; S500, based on the pedestrian route analysis results, select an available parking space as the target parking space; S700, controls the vehicle to park in the target parking space; The S500 includes: S502, calculate the average distance of each walking route based on the total distance of each walking route; S503 generates a distance threshold based on the average distance of each walking route; S504: Filter the available parking spaces corresponding to the walking routes with a total distance less than the distance threshold and the shortest non-rain shelter distance, and select them as the target parking spaces.

2. The automatic parking method for intelligent connected vehicles according to claim 1, characterized in that: The S300 includes: S301, acquire image data of the destination; S302, Based on the image data of the destination, analyze whether it is raining at the destination. If yes, proceed to S400; otherwise, proceed to S303. S303: Based on the image data of the destination, analyze whether there is water accumulation on the ground at the destination. If so, execute S400. It also includes S600, which obtains the distance between vehicles on the left and right sides of each available parking space, generates a distance sorting table, and selects an available parking space as the target parking space; If not in S303, then S600 is executed.

3. The automatic parking method for intelligent connected vehicles according to claim 2, characterized in that: The S600 includes: S601, Based on the parking lot selection result, obtain the garage image of the parking lot; S602, Based on the parking garage images, analyze whether there are obstacles in each vacant parking space, and exclude vacant parking spaces with obstacles; S603: Based on the parking garage image, analyze the distance between vehicles on the left and right sides of the remaining vacant parking spaces and generate a distance sorting table. S604: Select the available parking space with the largest distance between vehicles on the left and right sides from the spacing sort table.

4. The automatic parking method for intelligent connected vehicles according to claim 1, characterized in that: S100 includes: S101, Obtain the destination input content; S102, based on the destination entered, analyze the parking lots within the preset range of the destination and generate a list of parking lots for the destination; S103, retrieve parking lot selection results.

5. An automatic parking system for intelligent connected vehicles, characterized in that: An automatic parking method for intelligent connected vehicles using any one of claims 1-4.

Images