Automatic parking path planning system based on image recognition

Abstract

The invention relates to the technical field of parking path planning, in particular to an automatic parking path planning system based on image recognition, and the system comprises a parking demand condition extraction module which combines a current vehicle position area corresponding to a target vehicle in a constructed three-dimensional environment model, and obtaining relative position information of the parking calibration target area of the target vehicle based on the current vehicle position area, and generating a parking demand condition. According to the method, the surrounding environment information of the target vehicle is considered, accurate locking of the parking area is achieved, the parking path is planned based on different data sources through a multi-process parallel analysis mode and multiple planning modes, real-time feedback of the automatic parking planning path is achieved, and the parking efficiency is improved. And the response speed and the response result precision of the automatic parking planning path are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of parking path planning, and in particular to an automatic parking path planning system based on image recognition. Background Art

[0002] With the rapid development of autonomous driving technology, the automatic parking system, as an important part of autonomous driving technology, has received extensive attention and research. The automatic parking system integrates multiple sensors (such as cameras, radars, ultrasonic sensors, etc.) and advanced algorithms to achieve the automatic parking function of the vehicle without human intervention. Among them, the automatic parking path planning system based on image recognition has become a hot topic of current research due to its intuitive and efficient characteristics.

[0003] Existing automatic parking path planning systems based on image recognition often use image recognition technology to collect parking space location and obstacle location information around the vehicle, and directly plan the corresponding automatic parking path through a path planning algorithm. This method does not take into account the impact of incomplete collected parking space markings on parking position locking; and does not dynamically update obstacle information based on environmental information around the locked parking position, thereby affecting the subsequent automatic parking path planning results; at the same time, the existing automatic parking path has a single planning method, which cannot effectively take into account the response speed and response result accuracy of the automatic parking planning path; therefore, the existing technology has major defects. Summary of the invention

[0004] The purpose of the present invention is to provide an automatic parking path planning system based on image recognition to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an automatic parking path planning system based on image recognition, the system comprising: a parking environment model building module, a parking area dynamic locking module, a parking requirement condition extraction module and a parking path intelligent planning module,

[0006] The parking environment model building module collects environmental information around the target vehicle in real time through the built-in vehicle radar and high-definition camera on the target vehicle, and builds a three-dimensional environmental model around the target vehicle;

[0007] The parking area dynamic locking module identifies obstacle position information and parking line position information in a three-dimensional environment model around the target vehicle, and locks the parking demarcation target area of ​​the target vehicle;

[0008] The parking requirement condition extraction module obtains the relative position information of the parking calibration target area of ​​the target vehicle based on the current vehicle position area in combination with the current vehicle position area corresponding to the target vehicle in the constructed three-dimensional environment model, and generates the parking requirement condition;

[0009] The parking path intelligent planning module adopts a multi-process method to analyze the parking path solutions corresponding to the first parking path planning method and the second parking path planning method in parallel, generates parking plan path feedback information and feeds it back to the target vehicle display terminal, and after the driver confirms, automatically parks the target vehicle according to the parking plan path confirmed by the driver;

[0010] The first parking path planning method obtains the historical parking records of the target vehicle, calculates the adaptation value between the parking requirement condition and each historical parking record, and obtains the historical parking path planning scheme according to the adaptation value;

[0011] The second parking path planning method obtains parking requirements and obstacle position information in a three-dimensional environment model around the target vehicle, and iteratively generates an optimal reverse path planning solution for the target vehicle position based on the obstacle position information to obtain the optimal parking planning path for the target vehicle.

[0012] In the present invention, when planning the automatic parking path of the target vehicle, not only the environmental information around the target vehicle is taken into account to achieve accurate locking of the parking area, but also a multi-process parallel analysis method is used to simultaneously adopt a plurality of planning methods to plan the parking path based on different data sources (a first parking path planning method and a second parking path planning method), thereby achieving real-time feedback on the automatic parking planning path and improving the response speed and response result accuracy of the automatic parking planning path. In the present invention, as the response time increases, the response result accuracy of the automatic parking planning path will also continue to improve, and in the response process, the driver of the target vehicle can independently select the automatic parking planning path according to his own needs (response time requirements and parking path planning method requirements), thereby achieving effective management of the automatic parking path planning data.

[0013] Furthermore, the environmental information includes images and videos collected by built-in on-board radar and high-definition camera on the target vehicle within a preset radius area around the target vehicle;

[0014] The three-dimensional environment model around the target vehicle is constructed based on the collected environmental information within a preset radius area around the target vehicle through image recognition technology and 3D generation technology of 2D images.

[0015] Furthermore, the obstacle position information includes a vector formed based on the center point of the target vehicle for each position point in the three-dimensional environment model around the target vehicle whose corresponding height difference with the reference plane is greater than a preset height; the preset height is the minimum height difference between the target vehicle and the ground; the reference plane is a plane parallel to the plane where the bottom crossbeam of the target vehicle is located and whose corresponding height is lower than the preset height of the plane where the bottom crossbeam of the target vehicle is located;

[0016] The position information of the stop line includes each stop line area identified by image recognition technology in the three-dimensional environment model around the target vehicle and a vector formed by each position point in each stop line area based on the center point of the target vehicle;

[0017] When the parking calibration target area of ​​the target vehicle is locked, the minimum parking space area corresponding to the target vehicle preset in the database is obtained and recorded as the reference contour area; the parking marking position information in the three-dimensional environment model around the target vehicle is calibrated according to the contour models of each parking space marking preset in the database to obtain the parking marking position calibration information;

[0018] Randomly delineate reference contour areas at different positions in the three-dimensional environment model around the target vehicle, and the intersection of each random delineation scheme for the reference contour area and the corresponding obstacle position point in the obstacle position information is empty; obtain the calibration matching value corresponding to each random delineation scheme for the reference contour area, recorded as BD; the locking result of the parking calibration target area of ​​the target vehicle is the reference contour area in the random delineation scheme with the largest calibration matching value;

[0019] The BD=MU / MZ+r·L,

[0020] Wherein, MU represents the intersection area between the reference contour area in the corresponding random delineation scheme and the area corresponding to the parking mark position calibration information; MZ represents the area corresponding to the parking mark position calibration information; L represents the shortest distance between the reference contour area in the corresponding random delineation scheme and the obstacle position point in the three-dimensional environment model around the target vehicle; r represents the preset calibration matching conversion factor;

[0021] In the process of calibrating the parking line position information in the three-dimensional environment model around the target vehicle according to each parking line contour model preset in the database, the contour model matching value of the parking line position information in the three-dimensional environment model around the target vehicle based on the parking line contour model is calculated, which is recorded as P, and the area covered by each preset parking line contour model in the three-dimensional environment model around the target vehicle when the corresponding value in each P is the largest is used as the calibrated parking line position information;

[0022] The P is equal to the ratio of the number of position points in the parking line position information in the three-dimensional environment model around the target vehicle that belong to the preset corresponding parking line contour model to the total number of position points in the corresponding parking line position information when assuming that the parking line contour model corresponding to the parking line position information in the three-dimensional environment model around the target vehicle is the preset corresponding parking line contour model.

[0023] The present invention queries the minimum parking area corresponding to the target vehicle preset in the database, considering that the vehicle body parameters corresponding to different models of the target vehicles may also be different, which in turn leads to differences in the minimum parking area corresponding to the corresponding vehicles when they are parked.

[0024] Further, the parking calibration target area of ​​the target vehicle is obtained based on the relative position information of the current vehicle position area, which is a set of the corresponding current vehicle position area and the parking calibration target area of ​​the target vehicle in the three-dimensional environment model;

[0025] The parking requirement condition is a relative obstacle area in the parking requirement reference area corresponding to the target vehicle parking environment risk of 0 based on the locked target vehicle parking calibration target area;

[0026] When obtaining the target vehicle parking environment risk based on the locked target vehicle parking calibration target area, extract the union area of ​​the area whose distance from the locked target vehicle parking calibration area is less than or equal to the first associated distance and the locked target vehicle parking calibration area in the three-dimensional environment model around the target vehicle, and record it as the parking demand reference area;

[0027] The first associated distance is the maximum value of the distances between the front and rear ends of the vehicle and the nearest vehicle wheel respectively;

[0028] The position point with the lowest horizontal height in the parking demand reference area is recorded as the obstacle reference point, and the area consisting of all the position points in the parking demand reference area whose difference between the corresponding horizontal height and the horizontal height of the obstacle reference point is greater than the minimum height difference between the target vehicle and the ground is extracted and recorded as the relative obstacle area;

[0029] If the relative obstacle area in the parking demand reference area is an empty set, the target vehicle parking environment risk of the locked target vehicle parking calibration target area is determined to be 0; otherwise, the target vehicle parking environment risk of the locked target vehicle parking calibration target area is determined to be 1;

[0030] When the target vehicle parking environment risk of the locked target vehicle parking calibration target area is 1, the random demarcation scheme corresponding to the target vehicle parking calibration target area is marked, and the dynamic locking module of the parking area is jumped again. After the marked random scheme is eliminated, the parking calibration target area of ​​the target vehicle is locked again.

[0031] In the process of locking the parking calibration target area of ​​the target vehicle in the present invention, the body parameters of the target vehicle and the obstacle information in the surrounding environment of the target vehicle compared to the target vehicle are taken into consideration to achieve effective planning of the parking area of ​​the target vehicle, providing data support for planning the automatic parking path of the target vehicle in subsequent steps.

[0032] Furthermore, the parking planning path feedback information includes feedback time, a parking path plan corresponding to the first parking path planning method, and a parking path plan corresponding to the second parking path planning method;

[0033] The parking planning path feedback information adopts a real-time feedback mode, and the parking planning path feedback information corresponding to different feedback times has different contents;

[0034] There is a case where the parking path solution corresponding to the first parking path planning method and the parking path solution corresponding to the second parking path planning method in the parking planning path feedback information have an empty set.

[0035] Furthermore, when calculating the adaptation value between the parking requirement condition and each historical parking record,

[0036] Obtain the historical parking records of the target vehicle, extract the vehicle parking route corresponding to each parking record in the historical data, and record the adaptation value between the parking requirement condition and the kth parking record as SPk.

[0037] SPk=PCk·YCk·YZk,

[0038] Wherein, PCk represents the parking area deviation value between the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area; the value of PCk is equal to the quotient of the intersection area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area divided by the quotient of the union area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area;

[0039] YCk represents the impact value of the relative obstacle area in the parking demand reference area of ​​the target vehicle after executing the vehicle parking route corresponding to the kth parking record;

[0040] If the intersection of the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record and the relative obstacle area in the parking demand reference area is an empty set, then YCk=0; otherwise, YCk=1;

[0041] YZk represents the influence value of the obstacle position point in the obstacle position information in the three-dimensional environment model around the target vehicle after the target vehicle executes the vehicle parking route corresponding to the kth parking record;

[0042] If the intersection of the set of obstacle position points in the obstacle position information in the three-dimensional environment model around the target vehicle and the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record is an empty set, then YZk=0; otherwise, YZk=1;

[0043] When the adaptation value to the parking requirement condition is greater than the preset adaptation value, the vehicle parking route corresponding to each parking record is recorded as a historical parking route planning alternative;

[0044] The historical parking path planning solution is the historical parking path planning solution with the largest adaptation value to the parking requirement condition among all the historical parking path planning solution obtained;

[0045] If there is no historical parking path planning alternative, the historical parking path planning solution is empty.

[0046] Furthermore, when obtaining the optimal parking planning path for the target vehicle,

[0047] Obtaining parking demand conditions and obstacle position information in a three-dimensional environment model around the target vehicle, simulating a parking planning path of the target vehicle from an initial state based on a locked parking calibration target area to a current vehicle position state, and using the obtained simulated parking planning path as an optimal parking planning path for the target vehicle;

[0048] The initial state indicates that the center point of the target vehicle is aligned with the center point of the parking calibration target area of ​​the locked target vehicle, and the central axis of the target vehicle from the front to the parking space is parallel to the long marking line in the parking calibration target area of ​​the target vehicle;

[0049] In the process of simulating the parking path planning of the target vehicle from the initial state based on the locked target vehicle parking calibration target area to the current vehicle position state, each time the intersection between the forward route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the reverse operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; each time the intersection between the reverse route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the forward operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; and the optimal reverse path planning solution of the target vehicle position based on the obstacle position information is generated in an iterative manner;

[0050] When the target vehicle performs a forward operation or a reverse operation during the simulation, the deflection angle of the target vehicle is obtained by matching the forward deflection angle and the reverse deflection angle corresponding to the relative position between the simulated vehicle position in the database and the actual position of the current vehicle.

[0051] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention not only takes into account the environmental information around the target vehicle to achieve accurate locking of the parking area, but also uses a multi-process parallel analysis method and a variety of planning methods to plan the parking path based on different data sources, thereby achieving real-time feedback on the automatic parking planning path, thereby improving the response speed and accuracy of the response result of the automatic parking planning path;

[0052] In the present invention, as the response time increases, the accuracy of the response result of the automatic parking planning path will continue to improve, and during the response process, the driver of the target vehicle can independently select the automatic parking planning path according to his own needs, thereby realizing effective management of the automatic parking path planning data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a structural schematic diagram of an automatic parking path planning system based on image recognition in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 The present invention provides a technical solution: an automatic parking path planning system based on image recognition, the system comprising:

[0057] A parking environment model building module, wherein the parking environment model building module collects environmental information around the target vehicle in real time through a vehicle-mounted radar and a high-definition camera built into the target vehicle, and builds a three-dimensional environmental model around the target vehicle;

[0058] The environmental information includes images and videos collected by the built-in vehicle radar and high-definition camera on the target vehicle within a preset radius around the target vehicle;

[0059] The three-dimensional environment model around the target vehicle is constructed based on the collected environmental information within a preset radius area around the target vehicle through image recognition technology and 3D generation technology of 2D images.

[0060] The 3D generation technology of the 2D image collected in this embodiment includes SV3D (Stable Video 3D) released by Stability AI or the Make-It-3D method (creating high-fidelity 3D objects from a single image by using a 2D diffusion model as a 3D-aware prior. This framework does not require multi-view images for training and can be applied to any input image).

[0061] A parking area dynamic locking module, which identifies obstacle position information and parking line position information in a three-dimensional environment model around the target vehicle and locks the parking demarcation target area of ​​the target vehicle;

[0062] The obstacle position information includes a vector formed based on the center point of the target vehicle for each position point in the three-dimensional environment model around the target vehicle whose corresponding height difference with the reference plane is greater than a preset height; the preset height is the minimum height difference between the target vehicle and the ground; the reference plane is a plane parallel to the plane where the bottom crossbeam of the target vehicle is located and whose corresponding height is lower than the preset height of the plane where the bottom crossbeam of the target vehicle is located;

[0063] The position information of the stop line includes each stop line area identified by image recognition technology in the three-dimensional environment model around the target vehicle and a vector formed by each position point in each stop line area based on the center point of the target vehicle;

[0064] When the parking calibration target area of ​​the target vehicle is locked, the minimum parking space area corresponding to the target vehicle preset in the database is obtained and recorded as the reference contour area; the parking marking position information in the three-dimensional environment model around the target vehicle is calibrated according to the contour models of each parking space marking preset in the database to obtain the parking marking position calibration information;

[0065] Randomly delineate reference contour areas at different positions in the three-dimensional environment model around the target vehicle, and the intersection of each random delineation scheme for the reference contour area and the corresponding obstacle position point in the obstacle position information is empty; obtain the calibration matching value corresponding to each random delineation scheme for the reference contour area, recorded as BD; the locking result of the parking calibration target area of ​​the target vehicle is the reference contour area in the random delineation scheme with the largest calibration matching value;

[0066] The BD=MU / MZ+r·L,

[0067] Wherein, MU represents the intersection area between the reference contour area in the corresponding random delineation scheme and the area corresponding to the parking mark position calibration information; MZ represents the area corresponding to the parking mark position calibration information; L represents the shortest distance between the reference contour area in the corresponding random delineation scheme and the obstacle position point in the three-dimensional environment model around the target vehicle; r represents the preset calibration matching conversion factor;

[0068] In the process of calibrating the parking line position information in the three-dimensional environment model around the target vehicle according to each parking line contour model preset in the database, the contour model matching value of the parking line position information in the three-dimensional environment model around the target vehicle based on the parking line contour model is calculated, which is recorded as P, and the area covered by each preset parking line contour model in the three-dimensional environment model around the target vehicle when the corresponding value in each P is the largest is used as the calibrated parking line position information;

[0069] The P is equal to the ratio of the number of position points in the parking line position information in the three-dimensional environment model around the target vehicle that belong to the preset corresponding parking line contour model to the total number of position points in the corresponding parking line position information when assuming that the parking line contour model corresponding to the parking line position information in the three-dimensional environment model around the target vehicle is the preset corresponding parking line contour model.

[0070] The query of the minimum parking area corresponding to the target vehicle preset in the database takes into account that the vehicle body parameters corresponding to different models of the target vehicle may also be different, which in turn leads to differences in the minimum parking area corresponding to the corresponding vehicle when it is parked.

[0071] A parking requirement condition extraction module, wherein the parking requirement condition extraction module obtains relative position information of the parking calibration target area of ​​the target vehicle based on the current vehicle position area in combination with the current vehicle position area corresponding to the target vehicle in the constructed three-dimensional environment model, and generates a parking requirement condition;

[0072] The parking calibration target area of ​​the target vehicle is obtained based on the relative position information of the current vehicle position area, which is a set of the current vehicle position area corresponding to the three-dimensional environment model and the parking calibration target area of ​​the target vehicle;

[0073] The parking requirement condition is a relative obstacle area in the parking requirement reference area corresponding to the target vehicle parking environment risk of 0 based on the locked target vehicle parking calibration target area;

[0074] When obtaining the target vehicle parking environment risk based on the locked target vehicle parking calibration target area, extract the union area of ​​the area whose distance from the locked target vehicle parking calibration area is less than or equal to the first associated distance and the locked target vehicle parking calibration area in the three-dimensional environment model around the target vehicle, and record it as the parking demand reference area;

[0075] The first associated distance is the maximum value of the distances between the front and rear ends of the vehicle and the nearest vehicle wheel respectively;

[0076] The position point with the lowest horizontal height in the parking demand reference area is recorded as the obstacle reference point, and the area consisting of all the position points in the parking demand reference area whose difference between the corresponding horizontal height and the horizontal height of the obstacle reference point is greater than the minimum height difference between the target vehicle and the ground is extracted and recorded as the relative obstacle area;

[0077] If the relative obstacle area in the parking demand reference area is an empty set, the target vehicle parking environment risk of the locked target vehicle parking calibration target area is determined to be 0; otherwise, the target vehicle parking environment risk of the locked target vehicle parking calibration target area is determined to be 1;

[0078] When the target vehicle parking environment risk of the locked target vehicle parking calibration target area is 1, the random demarcation scheme corresponding to the target vehicle parking calibration target area is marked, and the dynamic locking module of the parking area is jumped again. After the marked random scheme is eliminated, the parking calibration target area of ​​the target vehicle is locked again.

[0079] A parking path intelligent planning module, wherein the parking path intelligent planning module adopts a multi-process method to analyze the parking path solutions corresponding to the first parking path planning method and the second parking path planning method in parallel, generates parking plan path feedback information and feeds it back to the target vehicle display terminal, and after the driver confirms, automatically parks the target vehicle according to the parking plan path confirmed by the driver;

[0080] The parking planning path feedback information includes feedback time, a parking path plan corresponding to the first parking path planning method, and a parking path plan corresponding to the second parking path planning method;

[0081] The parking planning path feedback information adopts a real-time feedback mode, and the parking planning path feedback information corresponding to different feedback times has different contents;

[0082] There is a case where the parking path solution corresponding to the first parking path planning method and the parking path solution corresponding to the second parking path planning method in the parking planning path feedback information have an empty set.

[0083] The first parking path planning method obtains the historical parking records of the target vehicle, calculates the adaptation value between the parking requirement condition and each historical parking record, and obtains the historical parking path planning scheme according to the adaptation value;

[0084] When calculating the adaptation value of the parking demand condition and each historical parking record,

[0085] Obtain the historical parking records of the target vehicle, extract the vehicle parking route corresponding to each parking record in the historical data, and record the adaptation value between the parking requirement condition and the kth parking record as SPk.

[0086] SPk=PCk·YCk·YZk,

[0087] Wherein, PCk represents the parking area deviation value between the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area; the value of PCk is equal to the quotient of the intersection area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area divided by the quotient of the union area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area;

[0088] YCk represents the impact value of the relative obstacle area in the parking demand reference area of ​​the target vehicle after executing the vehicle parking route corresponding to the kth parking record;

[0089] If the intersection of the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record and the relative obstacle area in the parking demand reference area is an empty set, then YCk=0; otherwise, YCk=1;

[0090] YZk represents the influence value of the obstacle position point in the obstacle position information in the three-dimensional environment model around the target vehicle after the target vehicle executes the vehicle parking route corresponding to the kth parking record;

[0091] If the intersection of the set of obstacle position points in the obstacle position information in the three-dimensional environment model around the target vehicle and the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record is an empty set, then YZk=0; otherwise, YZk=1;

[0092] When the adaptation value to the parking requirement condition is greater than the preset adaptation value, the vehicle parking route corresponding to each parking record is recorded as a historical parking route planning alternative;

[0093] The historical parking path planning solution is the historical parking path planning solution with the largest adaptation value to the parking requirement condition among all the historical parking path planning solution obtained;

[0094] If there is no historical parking path planning alternative, the historical parking path planning solution is empty.

[0095] The second parking path planning method obtains the parking requirement conditions and the obstacle position information in the three-dimensional environment model around the target vehicle, and iteratively generates the best reverse path planning solution of the target vehicle position based on the obstacle position information to obtain the best parking planning path of the target vehicle;

[0096] When the optimal parking planning path of the target vehicle is obtained,

[0097] Obtaining parking demand conditions and obstacle position information in a three-dimensional environment model around the target vehicle, simulating a parking planning path of the target vehicle from an initial state based on a locked parking calibration target area to a current vehicle position state, and using the obtained simulated parking planning path as an optimal parking planning path for the target vehicle;

[0098] The initial state indicates that the center point of the target vehicle is aligned with the center point of the parking calibration target area of ​​the locked target vehicle, and the central axis of the target vehicle from the front to the parking space is parallel to the long marking line in the parking calibration target area of ​​the target vehicle;

[0099] In the process of simulating the parking path planning of the target vehicle from the initial state based on the locked target vehicle parking calibration target area to the current vehicle position state, each time the intersection between the forward route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the reverse operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; each time the intersection between the reverse route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the forward operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; and the optimal reverse path planning solution of the target vehicle position based on the obstacle position information is generated in an iterative manner;

[0100] When the target vehicle performs a forward operation or a reverse operation during the simulation, the deflection angle of the target vehicle is obtained by matching the forward deflection angle and the reverse deflection angle corresponding to the relative position between the simulated vehicle position in the database and the actual position of the current vehicle.

[0101] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0102] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic parking path planning system based on image recognition, characterized in that: The system includes: a parking environment model building module, a parking area dynamic locking module, a parking requirement condition extraction module and a parking path intelligent planning module. The parking environment model building module collects environmental information around the target vehicle in real time through the built-in vehicle radar and high-definition camera on the target vehicle, and builds a three-dimensional environmental model around the target vehicle; The parking area dynamic locking module identifies obstacle position information and parking line position information in a three-dimensional environment model around the target vehicle, and locks the parking demarcation target area of ​​the target vehicle; The parking requirement condition extraction module obtains the relative position information of the parking calibration target area of ​​the target vehicle based on the current vehicle position area in combination with the current vehicle position area corresponding to the target vehicle in the constructed three-dimensional environment model, and generates the parking requirement condition; The parking path intelligent planning module adopts a multi-process method to analyze the parking path solutions corresponding to the first parking path planning method and the second parking path planning method in parallel, generates parking plan path feedback information and feeds it back to the target vehicle display terminal, and after the driver confirms, automatically parks the target vehicle according to the parking plan path confirmed by the driver; The first parking path planning method obtains the historical parking records of the target vehicle, calculates the adaptation value between the parking requirement condition and each historical parking record, and obtains the historical parking path planning scheme according to the adaptation value; The second parking path planning method obtains parking requirements and obstacle position information in a three-dimensional environment model around the target vehicle, and iteratively generates an optimal reverse path planning solution for the target vehicle position based on the obstacle position information to obtain the optimal parking planning path for the target vehicle.

2. The automatic parking path planning system based on image recognition according to claim 1, characterized in that: The environmental information includes images and videos collected by the built-in vehicle radar and high-definition camera on the target vehicle within a preset radius around the target vehicle; The three-dimensional environment model around the target vehicle is constructed based on the collected environmental information within a preset radius area around the target vehicle through image recognition technology and 3D generation technology of 2D images.

3. The automatic parking path planning system based on image recognition according to claim 1, characterized in that: The obstacle position information includes a vector formed based on the center point of the target vehicle for each position point in the three-dimensional environment model around the target vehicle whose corresponding height difference with the reference plane is greater than a preset height; the preset height is the minimum height difference between the target vehicle and the ground; the reference plane is a plane parallel to the plane where the bottom crossbeam of the target vehicle is located and whose corresponding height is lower than the preset height of the plane where the bottom crossbeam of the target vehicle is located; The position information of the stop line includes each stop line area identified by image recognition technology in the three-dimensional environment model around the target vehicle and a vector formed by each position point in each stop line area based on the center point of the target vehicle; When the parking calibration target area of ​​the target vehicle is locked, the minimum parking space area corresponding to the target vehicle preset in the database is obtained and recorded as the reference contour area; the parking marking position information in the three-dimensional environment model around the target vehicle is calibrated according to the contour models of each parking space marking preset in the database to obtain the parking marking position calibration information; Randomly delineate reference contour areas at different positions in the three-dimensional environment model around the target vehicle, and the intersection of each random delineation scheme for the reference contour area and the corresponding obstacle position point in the obstacle position information is empty; obtain the calibration matching value corresponding to each random delineation scheme for the reference contour area, recorded as BD; the locking result of the parking calibration target area of ​​the target vehicle is the reference contour area in the random delineation scheme with the largest calibration matching value; The BD=MU / MZ+r·L, Wherein, MU represents the intersection area between the reference contour area in the corresponding random delineation scheme and the area corresponding to the parking mark position calibration information; MZ represents the area corresponding to the parking mark position calibration information; L represents the shortest distance between the reference contour area in the corresponding random delineation scheme and the obstacle position point in the three-dimensional environment model around the target vehicle; r represents the preset calibration matching conversion factor; In the process of calibrating the parking line position information in the three-dimensional environment model around the target vehicle according to each parking line contour model preset in the database, the contour model matching value of the parking line position information in the three-dimensional environment model around the target vehicle based on the parking line contour model is calculated, which is recorded as P, and the area covered by each preset parking line contour model in the three-dimensional environment model around the target vehicle when the corresponding value in each P is the largest is used as the calibrated parking line position information; The P is equal to the ratio of the number of position points in the parking line position information in the three-dimensional environment model around the target vehicle that belong to the preset corresponding parking line contour model to the total number of position points in the corresponding parking line position information when assuming that the parking line contour model corresponding to the parking line position information in the three-dimensional environment model around the target vehicle is the preset corresponding parking line contour model.

4. The automatic parking path planning system based on image recognition according to claim 3, characterized in that: The parking calibration target area of ​​the target vehicle is obtained based on the relative position information of the current vehicle position area, which is a set of the current vehicle position area corresponding to the three-dimensional environment model and the parking calibration target area of ​​the target vehicle; The parking requirement condition is a relative obstacle area in the parking requirement reference area corresponding to the target vehicle parking environment risk of 0 based on the locked target vehicle parking calibration target area; When obtaining the target vehicle parking environment risk based on the locked target vehicle parking calibration target area, extract the union area of ​​the area whose distance from the locked target vehicle parking calibration area is less than or equal to the first associated distance and the locked target vehicle parking calibration area in the three-dimensional environment model around the target vehicle, and record it as the parking demand reference area; The first associated distance is the maximum value of the distances between the front and rear ends of the vehicle and the nearest vehicle wheel respectively; The position point with the lowest horizontal height in the parking demand reference area is recorded as the obstacle reference point, and the area consisting of all the position points in the parking demand reference area whose difference between the corresponding horizontal height and the horizontal height of the obstacle reference point is greater than the minimum height difference between the target vehicle and the ground is extracted and recorded as the relative obstacle area; If the relative obstacle area in the parking demand reference area is an empty set, the target vehicle parking environment risk in the locked target vehicle parking calibration target area is determined to be 0; Otherwise, it is determined that the target vehicle parking environment risk of the locked target vehicle parking calibration target area is 1; When the target vehicle parking environment risk of the locked target vehicle parking calibration target area is 1, the random demarcation scheme corresponding to the target vehicle parking calibration target area is marked, and the dynamic locking module of the parking area is jumped again. After the marked random scheme is eliminated, the parking calibration target area of ​​the target vehicle is locked again.

5. The automatic parking path planning system based on image recognition according to claim 4, characterized in that: The parking planning path feedback information includes feedback time, a parking path plan corresponding to the first parking path planning method, and a parking path plan corresponding to the second parking path planning method; The parking planning path feedback information adopts a real-time feedback mode, and the parking planning path feedback information corresponding to different feedback times has different contents; There is a case where the parking path solution corresponding to the first parking path planning method and the parking path solution corresponding to the second parking path planning method in the parking planning path feedback information have an empty set.

6. The automatic parking path planning system based on image recognition according to claim 1, characterized in that: When calculating the adaptation value of the parking demand condition and each historical parking record, Obtain the historical parking records of the target vehicle, extract the vehicle parking route corresponding to each parking record in the historical data, and record the adaptation value between the parking requirement condition and the kth parking record as SPk. SPk=PCk·YCk·YZk, Wherein, PCk represents the parking area deviation value between the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area; the value of PCk is equal to the quotient of the intersection area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area divided by the quotient of the union area of ​​the parking area after the target vehicle executes the vehicle parking route corresponding to the kth parking record and the locked target vehicle parking calibration target area; YCk represents the impact value of the relative obstacle area in the parking demand reference area of ​​the target vehicle after executing the vehicle parking route corresponding to the kth parking record; If the intersection of the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record and the relative obstacle area in the parking demand reference area is an empty set, then YCk=0; otherwise, YCk=1; YZk represents the influence value of the obstacle position point in the obstacle position information in the three-dimensional environment model around the target vehicle after the target vehicle executes the vehicle parking route corresponding to the kth parking record; If the intersection of the set of obstacle position points in the obstacle position information in the three-dimensional environment model around the target vehicle and the area passed by the target vehicle after executing the vehicle parking route corresponding to the kth parking record is an empty set, then YZk=0; otherwise, YZk=1; When the adaptation value to the parking requirement condition is greater than the preset adaptation value, the vehicle parking route corresponding to each parking record is recorded as a historical parking route planning alternative; The historical parking path planning solution is the historical parking path planning solution with the largest adaptation value to the parking requirement condition among all the historical parking path planning solution obtained; If there is no historical parking path planning alternative, the historical parking path planning solution is empty.

7. The automatic parking path planning system based on image recognition according to claim 1, characterized in that: When the optimal parking planning path of the target vehicle is obtained, Obtaining parking demand conditions and obstacle position information in a three-dimensional environment model around the target vehicle, simulating a parking planning path of the target vehicle from an initial state based on a locked parking calibration target area to a current vehicle position state, and using the obtained simulated parking planning path as an optimal parking planning path for the target vehicle; The initial state indicates that the center point of the target vehicle is aligned with the center point of the parking calibration target area of ​​the locked target vehicle, and the central axis of the target vehicle from the front to the parking space is parallel to the long marking line in the parking calibration target area of ​​the target vehicle; In the process of simulating the parking path planning of the target vehicle from the initial state based on the locked target vehicle parking calibration target area to the current vehicle position state, each time the intersection between the forward route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the reverse operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; each time the intersection between the reverse route area simulated by the target vehicle and the parking requirement condition or the obstacle position information in the three-dimensional environment model around the target vehicle is not an empty set, the forward operation is triggered in advance when the distance between the target vehicle and the obstacle is a preset obstacle distance; and the optimal reverse path planning solution of the target vehicle position based on the obstacle position information is generated in an iterative manner; When the target vehicle performs a forward operation or a reverse operation during the simulation, the deflection angle of the target vehicle is obtained by matching the forward deflection angle and the reverse deflection angle corresponding to the relative position between the simulated vehicle position in the database and the actual position of the current vehicle.

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