Curve recognition method and device, equipment and storage medium

Abstract

The invention discloses a curve recognition method, device and equipment and a storage medium, and relates to the technical field of automatic driving, and the curve recognition method comprises the steps: obtaining the coordinates of a data point of a lane virtual center line within a preset distance in front of a vehicle in a vehicle coordinate system; determining a filtering curvature radius data set of the lane virtual center line according to the coordinates under the own vehicle coordinate system; traversing the filtering curvature radius data set, acquiring sampling point index information and sampling point filtering curvature radius of a current sampling point, and determining curve section starting point information and curve section ending point information according to the sampling point index information and the sampling point filtering curvature radius; and determining target curve section information according to the curve section starting point information and the curve section ending point information so as to complete curve recognition. According to the scheme, the continuous curve information in a certain range in front of the vehicle can be recognized in real time.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to curve recognition methods, devices, equipment and storage media. Background Art

[0002] Autonomous driving technology is developing rapidly. Vehicles can achieve intelligent perception of roads, traffic and environment during driving to improve driving safety and comfort. During driving, curves are a challenging part, especially when driving at high speeds or in complex road conditions. Passing curves is crucial to driving safety.

[0003] Existing autonomous driving or advanced driver assistance systems (ADAS) usually monitor the road conditions in front of the vehicle in real time through sensors such as cameras and lidar. Although some systems can detect curvature changes before entering a curve and trigger vehicle speed adjustment, the reaction time is limited, and there is a lack of advance judgment of the characteristics of the curve, making it impossible to achieve the optimal advance deceleration and passing strategy. Therefore, how to identify the continuous curve information within a certain range in front of the vehicle in real time has become a problem to be solved.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a curve recognition method, device, equipment and storage medium, aiming to solve the technical problem of how to recognize continuous curve information within a certain range in front of the vehicle in real time.

[0006] To achieve the above objectives, the present application proposes a curve recognition method, the method comprising:

[0007] Obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system;

[0008] Determine a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system;

[0009] Traversing the filtered curvature radius data set, obtaining sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determining the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point;

[0010] The target curve section information is determined according to the curve section starting point information and the curve section ending point information to complete the curve recognition.

[0011] In one embodiment, the step of determining the curve segment start point information and the curve segment end point information according to the sampling point index information and the sampling point filtered curvature radius includes:

[0012] When the filtered curvature radius is less than a set curve curvature radius threshold, determining the current sampling point as a curve starting point, acquiring a first longitudinal distance and a first sampling point index of the curve starting point, obtaining curve segment starting point information, and initializing a curve segment curvature radius data set based on the curve segment starting point information;

[0013] The next sampling point is updated as the current sampling point, and the filtered curvature radius of the current sampling point is stored in the curve segment curvature radius data set until the filtered curvature radius is greater than or equal to the set curve curvature radius threshold, and the previous sampling point of the current sampling point is determined to be the curve termination point, and the second longitudinal distance and the second sampling point index of the curve termination point are obtained to obtain the curve segment termination point information.

[0014] In one embodiment, the step of determining the target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete the curve recognition includes:

[0015] Determining the length of the curve section according to the curve section starting point information and the curve section ending point information;

[0016] When the curve section length of the curve section is greater than or equal to the curve length threshold, taking the curve section as a target curve section;

[0017] The curve information of the target curve section is acquired to obtain the target curve section information to complete the curve recognition.

[0018] In one embodiment, the step of acquiring the curve information of the target curve section to obtain the target curve section information to complete the curve recognition includes:

[0019] Acquire a curve section curvature radius data set of the target curve section;

[0020] Determining a target curvature radius of the curve section according to the curve section curvature radius data set;

[0021] The starting point information of the curve segment, the ending point information of the curve segment and the target curvature radius of the curve segment are stored in a curve segment data set to obtain target curve segment information to complete curve recognition.

[0022] In one embodiment, the step of determining the filtered curvature radius dataset of the lane virtual centerline according to the coordinates in the vehicle coordinate system includes:

[0023] Determine the lane curvature radius of the sampling point of the lane virtual center line according to the coordinates in the vehicle coordinate system;

[0024] The lane curvature radius of the sampling point is smoothed and filtered to obtain a filtered curvature radius data set, wherein the filtered curvature radius data set includes sampling point index information and a filtered curvature radius.

[0025] In one embodiment, the step of determining the lane curvature radius of the sampling point of the lane virtual centerline according to the coordinates in the vehicle coordinate system includes:

[0026] Determine the lane curvature radius of the sampling point of the lane virtual center line according to the coordinates in the vehicle coordinate system;

[0027] Uniformly sampling the lane virtual center line according to the lane line fitting equation to obtain the rectangular coordinates of the sampling points;

[0028] The lane curvature radius of the sampling point is determined according to the rectangular coordinates of the sampling point.

[0029] In one embodiment, the step of obtaining the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system includes:

[0030] Obtain the longitude and latitude coordinates of the vehicle, the heading angle information of the vehicle, and the longitude and latitude coordinates of the shape point of the virtual center line of the lane within a preset distance in front of the vehicle;

[0031] The longitude and latitude coordinates of the shape point are converted into coordinates in the vehicle coordinate system according to the longitude and latitude coordinates of the vehicle and the heading angle information of the vehicle.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a curve recognition device, which includes:

[0033] An information acquisition module is used to obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system;

[0034] A curvature calculation module, which determines a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system;

[0035] An information identification module, used to traverse the filtered curvature radius data set, obtain the sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determine the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point;

[0036] The curve determination module is used to determine the target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete the curve recognition.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a curve recognition device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the curve recognition method described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the curve recognition method described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the curve recognition method described above are implemented.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] The coordinates of the data points of the lane virtual center line within a preset distance in front of the vehicle in the vehicle coordinate system are obtained; the filtered curvature radius data set of the lane virtual center line is determined according to the coordinates in the vehicle coordinate system; the filtered curvature radius data set is traversed to obtain the sampling point index information and the sampling point filtered curvature radius of the current sampling point, and the curve segment start point information and the curve segment end point information are determined according to the sampling point index information and the sampling point filtered curvature radius; the target curve segment information is determined according to the curve segment start point information and the curve segment end point information, so as to complete Curve recognition: by processing the coordinates of the data points of the virtual center line of the lane within a preset distance (such as 200 meters) in front of the vehicle in the vehicle coordinate system, a filtered curvature radius data set of the virtual center line of the lane can be obtained. The starting point information and the ending point information of the curve segment of multiple curve segments within the preset distance (such as 200 meters) in front can be determined according to the filtered curvature radius data set, and then multiple curve segments can be screened according to the starting point information and the ending point information of the curve segment to obtain the curve information of the target curve segment that meets the conditions, thereby realizing the early recognition of continuous curves. In the process of automatic driving of the vehicle, the continuous curve information within a certain range in front of the vehicle can be recognized in real time, and the key characteristics of the curve can be detected in advance, so as to combine the automatic driving control module and the vehicle dynamic stability module to realize appropriate deceleration control before entering the curve, reduce the safety hazards caused by excessive speed, and improve the availability and reliability of the automatic driving system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 A schematic diagram of a flow chart provided for Embodiment 1 of the curve recognition method of the present application;

[0045] Figure 2 A curve determination flow chart provided for the first embodiment of the curve identification method of the present application;

[0046] Figure 3 A flow chart of the final curve processing provided in the first embodiment of the curve recognition method of the present application;

[0047] Figure 4 A schematic diagram of a flow chart provided for Embodiment 2 of the curve recognition method of the present application;

[0048] Figure 5 This is a schematic diagram of the module structure of the curve recognition device according to an embodiment of the present application;

[0049] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the curve recognition method in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system; determine the filtered curvature radius data set of the virtual center line of the lane according to the coordinates in the vehicle coordinate system; traverse the filtered curvature radius data set to obtain the sampling point index information and the sampling point filtered curvature radius of the current sampling point, and determine the starting point information and the ending point information of the curve segment according to the sampling point index information and the sampling point filtered curvature radius; determine the target curve segment information according to the starting point information and the ending point information of the curve segment to complete curve recognition.

[0054] In this embodiment, for ease of description, the following description is based on identifying the vehicle end as the execution subject.

[0055] Autonomous driving technology is developing rapidly. Vehicles can achieve intelligent perception of roads, traffic and environment during driving to improve driving safety and comfort. During driving, curves are a challenging part, especially when driving at high speeds or in complex road conditions. Passing curves is crucial to driving safety.

[0056] Existing autonomous driving or advanced driver assistance systems (ADAS) usually monitor the road conditions in front of the vehicle in real time through sensors such as cameras and lidar. Although some systems can detect curvature changes before entering a curve and trigger vehicle speed adjustment, the reaction time is limited, and there is a lack of advance judgment of the characteristics of the curve, making it impossible to achieve the optimal advance deceleration and passing strategy. Therefore, how to identify the continuous curve information within a certain range in front of the vehicle in real time has become a problem to be solved.

[0057] The present application provides a solution that can identify continuous curve information within a certain range in front of the vehicle in real time. Multiple curves within a set distance range in front can be identified in turn, and parameter information of each curve can be given, and the curve information can be updated in real time while the vehicle is driving.

[0058] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle terminal, etc. The following takes the vehicle terminal as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the present application embodiment provides a curve recognition method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the curve recognition method of the present application.

[0060] In this embodiment, the curve recognition method includes steps S10 to S40:

[0061] Step S10, obtaining the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system;

[0062] It should be noted that the curve recognition method of the present application is aimed at the scenario of intelligent connected autonomous vehicle (ICA) slowing down in advance when passing a curve. The key parameters of the curve (such as curve distance, curvature radius and length, etc.) can be identified during the vehicle's driving process, so as to appropriately slow down according to these parameters before entering the curve. Some existing autonomous driving implementation schemes are often based on real-time lane detection and prediction to cope with the complex situation of the curve. However, due to insufficient grasp of road information, this method often has the risk of excessively high speed of entering the curve, resulting in limited curve passing rate and vehicle driving safety. Especially in small radius or continuous sharp bends, the system is prone to insufficient adjustment or slow response, thereby affecting the stability of the vehicle. The scheme of the present application can identify multiple curves in turn by obtaining the coordinates of the data points of the virtual center line of the lane within a preset distance (such as 200 meters) in front of the vehicle in the vehicle coordinate system, and give the parameter information of each curve, which is updated in real time during the vehicle driving process.

[0063] It should be noted that the virtual center line of the lane is the idealized center line of each lane, representing the center trajectory of the vehicle. The data point information of the virtual center line of the lane within a preset distance (such as 200 meters) in front of the vehicle can be obtained through the lane-level Detail (LD) map information, and the data point information in the LD map information can be converted into coordinates in the vehicle coordinate system through the vehicle positioning information. Among them, the vehicle coordinate system is the rear axle center coordinate system of the vehicle. The coordinate system takes the center of the rear axle of the vehicle as the origin, the x-axis is along the length of the vehicle, pointing to the front of the vehicle, the y-axis is along the width of the vehicle, pointing to the left side of the vehicle, and the z-axis is perpendicular to the ground and points to the top of the vehicle.

[0064] Step S20, determining a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system;

[0065] It should be noted that the filtered curvature radius data set contains the filtered curvature radius of the sampling points of the lane virtual centerline within a preset distance in front of the vehicle. The curvature radius is used to describe the curvature of the lane virtual centerline. The smaller the curvature radius, the greater the curvature of the lane virtual centerline at that point. After obtaining the coordinates of the data points of the lane virtual centerline in the vehicle coordinate system, the initial curvature radius data of the lane virtual centerline can be obtained according to a fitting algorithm such as polynomial fitting. The initial curvature radius data set is smoothed according to the filtering algorithm to reduce the influence of noise and error and obtain a more accurate filtered curvature radius data set.

[0066] In a feasible implementation, step S20 may include steps S21 to S22:

[0067] Step S21, determining the lane curvature radius of the sampling point of the lane virtual center line according to the coordinates in the vehicle coordinate system;

[0068] It should be noted that a cubic polynomial equation can be used to fit the coordinates of the data points of the virtual center line of the lane in the vehicle coordinate system to obtain the equation of the virtual center line of the lane, and then the virtual center line of the lane is uniformly sampled according to the equation to obtain the lane curvature radius of each sampling point.

[0069] In a feasible implementation, step S21 may include: performing lane line fitting on the virtual center line of the lane according to the coordinates in the vehicle coordinate system to obtain a lane line fitting equation; uniformly sampling the virtual center line of the lane according to the lane line fitting equation to obtain the rectangular coordinates of the sampling points; and determining the lane curvature radius of the sampling point according to the rectangular coordinates of the sampling point.

[0070] It should be noted that the cubic polynomial equation y=C3x 3 +C2x 2 +C1x+C0 performs lane line fitting on the coordinates of the data points of the virtual center line of the lane in the vehicle coordinate system, and the cubic fitting parameters of the virtual center line of the lane [C0, C1, C2, C3] can be obtained, and then the lane line fitting equation can be determined. Specifically, according to the coordinates of the data points of the virtual center line of the lane, a target function can be constructed by the least squares method or other optimization algorithms to measure the sum of the squares of the vertical distances from all data points to the fitting curve. By minimizing this target function, the optimal cubic fitting parameters [C0, C1, C2, C3] can be obtained.

[0071] It should be understood that the lane line fitting equation y = C3x 3 +C2x 2 After +C1x+C0, the lane virtual centerline can be uniformly sampled according to the lane fitting equation, that is, a series of equally spaced points are selected on the lane virtual centerline to obtain the rectangular coordinates [x0, y0] of each sampling point. The lane curvature value ρ of each sampling point can be calculated by the following curvature calculation formula:

[0072]

[0073] Where x0 is the X coordinate of the sampling point, and y0 is the Y coordinate calculated by the cubic polynomial equation.

[0074] After obtaining the lane curvature value ρ of each sampling point, the curvature radius R of each sampling point can be calculated by the following curvature radius calculation formula to obtain the lane curvature radius of the sampling point:

[0075] R=1 / ρ

[0076] The larger the curvature radius, the smaller the curvature degree of the virtual center line of the lane at that point; the smaller the curvature radius, the greater the curvature degree of the virtual center line of the lane at that point.

[0077] Step S22, performing smoothing filtering on the lane curvature radius of the sampling point to obtain a filtered curvature radius data set, wherein the filtered curvature radius data set includes sampling point index information and a filtered curvature radius.

[0078] It should be noted that in order to avoid the noise impact caused by a few sampling points with large and small curvatures and reduce the error in the calculation of curve parameters, it is necessary to perform smoothing filtering on the lane curvature radius of the sampling points of the lane virtual center line within a preset distance (such as 200 meters) in front of the vehicle to obtain a filtered curvature radius data set. Specifically, a smoothing filtering algorithm can be used to smooth the coordinate points uniformly sampled on the lane virtual center line, and the sliding window size is set to 5. A total of 5 sampling points are taken before and after the sampling point to be processed (including the sampling point) each time, and the curvature radius value of the sampling point to be processed is taken as the average value of the curvature radius of these 5 sampling points to obtain the filtered curvature radius of the sampling point. After the above-mentioned smoothing filtering is performed on all sampling points on the lane virtual center line in turn, a filtered curvature radius data set is obtained. The filtered curvature radius data set is stored in the form of an array, including the index of each sampling point (i.e., the sampling point index information) and the corresponding filtered curvature radius value (i.e., the filtered curvature radius).

[0079] Step S30, traversing the filtered curvature radius data set, obtaining sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determining the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point;

[0080] It should be noted that by traversing the filtered curvature radius data set, the index information and the corresponding filtered curvature radius of each sampling point can be obtained, and the filtered curvature radius of each sampling point can be compared with the small curve curvature radius threshold, so that the starting point and the ending point of multiple curve sections in the filtered curvature radius data set can be determined. Specifically, when the filtered curvature radius decreases from a larger value (i.e., a straighter road section) to below the small curve curvature radius threshold, the sampling point that is less than the small curve curvature radius threshold can be determined as the starting point of the curve section, and the curve section starting point information is obtained. Conversely, when the curvature radius recovers from a smaller value (i.e., less than the small curve curvature radius threshold) to not less than the small curve curvature radius threshold, the previous sampling point of the sampling point that is not less than the small curve curvature radius threshold can be determined as the ending point of the curve section, and the curve section ending point information is obtained.

[0081] In a feasible implementation manner, determining the curve segment starting point information and the curve segment ending point information according to the sampling point index information and the sampling point filtered curvature radius in step S30 may include: when the filtered curvature radius is less than a set curve curvature radius threshold, determining the current sampling point as the curve starting point, and obtaining a first longitudinal distance and a first sampling point index of the curve starting point to obtain the curve segment starting point information, and initializing the curve segment curvature radius data set based on the curve segment starting point information; updating the next sampling point to the current sampling point, and storing the filtered curvature radius of the current sampling point in the curve segment curvature radius data set until the filtered curvature radius is greater than or equal to the set curve curvature radius threshold, determining the previous sampling point of the current sampling point as the curve ending point, and obtaining a second longitudinal distance and a second sampling point index of the curve ending point to obtain the curve segment ending point information.

[0082] It should be noted that the threshold of the curve curvature radius is set as the threshold of the small curve curvature radius, which is used to determine whether the lane line begins to enter the curve. In the process of curve recognition, the filter curvature radius data set is traversed, and the filter curvature radius of each sampling point is compared with the set curve curvature radius threshold. When the filter curvature radius of the first current sampling point traversed is less than the set small curve curvature radius threshold, the sampling point is determined to be the starting point of the curve, and the longitudinal distance of the starting point is obtained as the first longitudinal distance. The index of the starting point in the filtered sampling point array (i.e., the filter curvature radius data set) is obtained to obtain the first sampling point index. At the same time, based on the first sampling point index, the filter curvature radius of the starting point is obtained from the filter curvature radius data set to initialize the curve segment curvature radius data set of the current curve segment.

[0083] It should be understood that after a sampling point is determined as the starting point of the curve, the filtered curvature radii of all sampling points starting from the sampling point that are less than the set small curve curvature radius threshold will be recorded in sequence according to the first sampling point index, and a curve segment curvature radius data set including all curvature radius values ​​of the current curve segment will be obtained, and stored in the form of an array until the filtered curvature radius of the current sampling point traversed is greater than or equal to the small curve curvature radius threshold, and the previous sampling point of the current sampling point is determined as the curve end point, and the longitudinal distance of the end point is obtained as the second longitudinal distance, and the index of the end point in the filtered sampling point array (i.e., the filtered curvature radius data set) is obtained to obtain the second sampling point index.

[0084] In addition, it should be understood that the first longitudinal distance is the Y coordinate of the sampling point determined as the starting point of the curve calculated by the lane line fitting equation, and the second longitudinal distance is the Y coordinate of the sampling point determined as the ending point of the curve calculated by the lane line fitting equation. By traversing each sampling point in the filtered curvature radius data set, the starting point information, the ending point information of multiple curve sections within a preset distance (such as 200 meters) in front of the vehicle and the curvature radius data set of each curve section can be obtained.

[0085] Step S40, determining target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete curve recognition.

[0086] It should be understood that the starting point information of the curve segment includes the longitudinal distance of the starting point and the index of the starting point in the filtered sampling point array (i.e., the filtered curvature radius data set), and the ending point information of the curve segment includes the longitudinal distance of the ending point and the index of the ending point in the filtered sampling point array (i.e., the filtered curvature radius data set). The length information of each curve segment can be calculated based on the starting point information of the curve segment and the ending point information of the curve segment. According to the length information of the curve segment, the shorter curve segment can be filtered to obtain the curve information of the target curve segment (such as the curve distance, radius and length, etc.), so as to complete the recognition of multiple curves within a preset distance (such as 200 meters) in front of the vehicle.

[0087] In a feasible implementation, step S40 may include steps S41 to S43:

[0088] Step S41, determining the length of the curve segment according to the curve segment starting point information and the curve segment ending point information;

[0089] It should be understood that the longitudinal distance of the starting point of the curve segment can be determined based on the starting point information of the curve segment, and the longitudinal distance of the ending point of the curve segment can be determined based on the ending point information of the curve segment. By calculating the difference in the longitudinal distance between the ending point and the starting point, a simplified representation of the curve segment length can be obtained.

[0090] Step S42, when the curve section length of the curve section is greater than or equal to the curve length threshold, taking the curve section as a target curve section;

[0091] It should be understood that after obtaining the lengths of multiple curve segments, the curve segments will be screened according to the lengths of the curve segments to filter out curves that are too short and have little impact on driving, reduce the amount of data that needs to be processed by the vehicle during autonomous driving, and improve the response speed and real-time performance of the system. The curve length threshold is the minimum curve length threshold. The curve segments that are greater than or equal to the minimum curve length threshold are qualified curves, that is, target curve segments, and the curve segments that are less than the minimum curve length threshold are shorter curves that need to be filtered.

[0092] Step S43, acquiring the curve information of the target curve section, obtaining the target curve section information, to complete curve recognition.

[0093] It should be noted that the curve information of the target curve section includes the curve section starting point information, the curve section ending point information, the minimum curvature radius and the average curvature radius of the target curve section.

[0094] In a feasible implementation, step S43 may include: obtaining a curve segment curvature radius data set of the target curve segment; determining the target curvature radius of the curve segment based on the curve segment curvature radius data set; storing the curve segment starting point information, the curve segment ending point information and the target curvature radius of the curve segment in the curve segment data set to obtain the target curve segment information to complete curve identification.

[0095] It should be understood that the average curvature radius of the target curve segment can be calculated based on the curve segment curvature radius data set of the target curve segment, and the minimum curvature radius of the current curve segment can be determined therefrom to obtain the target curvature radius of the curve segment. The curve segment starting point information, the curve segment ending point information and the target curvature radius of the curve segment of the target curve segment are stored in the curve segment data set in the form of an array, and the target curve segment information can be obtained. During the process of automatic driving of the vehicle, the curve information of the target curve segment can be identified and updated in real time. Before the vehicle enters the curve segment, the key parameters such as the curve distance, curvature radius and length of the curve segment to be entered can be obtained based on the target curve segment information updated and stored in advance. Combined with the curve radius and length information, the speed of the vehicle is dynamically adjusted. Before the vehicle approaches the curve, the vehicle is controlled to gradually decelerate to ensure that the vehicle enters the curve at a safe speed, making the driving process smoother, thereby improving the comfort of passengers.

[0096] For example, refer to Figure 2 , Figure 2 This is a curve determination flow chart provided in Example 1 of the curve identification method of this application. Figure 2As shown in the figure, after smoothing and filtering the curvature radius of the sampling point of the virtual center line of the lane to obtain the filtered curvature radius array (i.e., the filtered curvature radius data set), the curvature radius array is traversed to obtain the curvature radius of the current sampling point, and it is determined whether the curvature radius of the sampling point is less than the curvature radius threshold of the curve. If the curvature radius of the sampling point is less than the curvature radius threshold of the small curve, the curvature radius of the sampling point is stored in the curvature radius array (i.e., the curvature radius data set) to which the current curve segment belongs. If the curve flag in_curve is not true (indicating that the current sampling point is the starting point of the curve), the longitudinal distance and index information of the new starting point of the curve are stored, and in_curve is set to true to start recording the new curve segment information; if the curve flag in_curve is true (indicating that the current sampling point is a continuously recorded sampling point less than the curvature radius threshold of the small curve), the next sampling point is traversed. If the curvature radius of the sampling point is not less than the small curve curvature radius threshold, and the curve flag in_curve is true, store the longitudinal distance of the end point of the current curve segment, index information and calculate the length of the curve segment, end the curve information recording of the current curve segment, set the curve flag in_curve to false, and determine whether the curvature radius array to which the current curve segment belongs is empty. If the curvature radius array to which the current curve segment belongs is not empty, calculate and store the minimum and average curvature radii of the current curve segment (that is, the target curvature radius of the curve segment). When the length of the current curve segment is not less than the minimum curve length, take the current curve segment as the target curve segment and store the current curve segment information in the curve segment array.

[0097] In addition, it should be noted that the solution of this application will identify all curves within a preset distance (such as 200 meters) in front of the vehicle. If the sampling point at the end of the identification distance is in a curve, that is, the current curve has not ended, it is necessary to process this curve separately. Figure 3 , Figure 3 This is a flow chart of the final curve processing provided in the first embodiment of the curve recognition method of this application. Figure 3 As shown, for the sampling points of the curvature radius array after traversal and filtering, it will be determined whether the curve flag in_curve is true and the curvature radius array to which the current curve segment belongs is not empty. If the curve flag in_curve is true and the curvature radius array to which the current curve segment belongs is not empty, the longitudinal distance and index information of the end point of the current curve segment are stored, and the curve length, minimum and average curvature radii are calculated, the curve information recording of the current curve segment is ended, and it is determined whether the length of the current curve segment is not less than the minimum curve length. When the length of the current curve segment is not less than the minimum curve length, the current curve segment is determined to be the target curve segment, the current curve segment information is stored in the curve segment array, and the curve detection is ended.

[0098] The present embodiment provides a curve recognition method, which obtains the coordinates of the data points of the lane virtual center line within a preset distance in front of the vehicle in the vehicle coordinate system; determines the filtered curvature radius data set of the lane virtual center line according to the coordinates in the vehicle coordinate system; traverses the filtered curvature radius data set to obtain the sampling point index information and the sampling point filtered curvature radius of the current sampling point, and determines the curve segment start point information and the curve segment end point information according to the sampling point index information and the sampling point filtered curvature radius; determines the target according to the curve segment start point information and the curve segment end point information The curve segment information is used to complete the curve recognition. By processing the coordinates of the data points of the virtual center line of the lane within a preset distance (such as 200 meters) in front of the vehicle in the vehicle coordinate system, the filtered curvature radius data set of the virtual center line of the lane can be obtained. The curve segment starting point information and curve segment ending point information of multiple curve segments within the preset distance (such as 200 meters) in front can be determined according to the filtered curvature radius data set. Then, multiple curve segments are screened according to the curve segment starting point information and curve segment ending point information to obtain the curve information of the target curve segment that meets the conditions, thereby realizing the early recognition of continuous curves. In the process of vehicle automatic driving, the continuous curve information within a certain range in front of the vehicle can be recognized in real time, and the key characteristics of the curve can be detected in advance. In combination with the automatic driving control module and the vehicle dynamic stability module, appropriate deceleration control can be implemented before entering the curve, reducing the safety hazards caused by excessive speed, and improving the availability and reliability of the automatic driving system.

[0099] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , step S10 may include steps S11 to S12:

[0100] Step S11, obtaining the longitude and latitude coordinates of the vehicle, the heading angle information of the vehicle, and the longitude and latitude coordinates of the shape point of the lane virtual center line within a preset distance in front of the vehicle;

[0101] It should be noted that the data acquisition of the present application solution relies on map positioning technology. Through lane-level positioning and map road topology, LD map information within a preset distance (such as 200 meters) in front of the vehicle can be obtained. According to the LD map information, the lane where the vehicle is located can be located, the virtual center line of the lane where the vehicle is located can be identified, and the longitude and latitude coordinates of the shape point of the virtual center line of the lane within the preset distance in front of the vehicle can be obtained. The LD map information contains map data of detailed information on roads and lanes, such as lane topology data (such as the connection relationship between lanes, intersection information, etc.).

[0102] Specifically, based on the current Global Positioning System (GPS) coordinates of the vehicle and lane-level positioning systems such as high-precision maps combined with inertial measurement unit (IMU) sensors, laser radars, etc., the specific lane information of the vehicle can be determined. Combined with the topological structure data in the LD map information, the longitude and latitude coordinates of the shapepoint of the virtual centerline of the lane within a preset distance in front of the vehicle can be obtained. Shapepoints are data points on the virtual centerline of the lane obtained through LD map information, which are used to fit the shape of the virtual centerline of the lane, thereby describing the specific shape and position of the lane. The longitude and latitude coordinates of the shapepoints are the coordinates of the longitude and latitude positions of the corresponding data points on the virtual centerline of the lane in the LD map in the geographic coordinate system. The longitude and latitude coordinates of the vehicle are the coordinates representing the longitude and latitude positions of the current position of the vehicle in the geographic coordinate system, which can be obtained through GPS and represent the geographic location of the vehicle. The heading angle information of the vehicle includes the direction angle information of the vehicle relative to the geographic north (or the earth coordinate system), which describes the direction of the vehicle's forward direction, that is, the direction or driving direction of the vehicle, and can be measured by the vehicle's sensors such as IMU.

[0103] Step S12, converting the longitude and latitude coordinates of the shape point into coordinates in the vehicle coordinate system according to the longitude and latitude coordinates of the vehicle and the heading angle information of the vehicle.

[0104] It should be noted that, based on the longitude and latitude coordinates of the ego vehicle and the heading angle information of the ego vehicle, the longitude and latitude coordinates of each shape point on the virtual center line of the lane can be converted into coordinates in the ego vehicle coordinate system through a mathematical algorithm (such as a rotation matrix or a coordinate transformation formula). Specifically, the relative position difference between the longitude and latitude coordinates of the ego vehicle and the longitude and latitude coordinates of each shape point can be calculated to obtain the horizontal distance and vertical distance of the shape point relative to the ego vehicle. Based on the heading angle of the ego vehicle, the relative position of the shape point is rotated according to this angle to match the direction of the ego vehicle coordinate system, and the coordinates of the data points of the virtual center line of the lane in the ego vehicle coordinate system are obtained.

[0105] The present embodiment provides a curve recognition method, which obtains the longitude and latitude coordinates of the vehicle, the heading angle information of the vehicle, and the longitude and latitude coordinates of the shape point of the virtual center line of the lane within a preset distance in front of the vehicle; according to the longitude and latitude coordinates of the vehicle and the heading angle information of the vehicle, the longitude and latitude coordinates of the shape point are converted into coordinates in the vehicle coordinate system, and by accurately obtaining the geographic location (longitude and latitude coordinates) and direction (heading angle information) of the vehicle, combined with the longitude and latitude coordinates of the shape point of the virtual center line of the lane within a preset distance in front of the vehicle, the virtual center line of the lane can be accurately positioned in the vehicle coordinate system.

[0106] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the curve recognition method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0107] This application also provides a curve recognition device, please refer to Figure 5 , the curve recognition device comprises:

[0108] An information acquisition module 10 is used to obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system;

[0109] The curvature calculation module 20 determines a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system;

[0110] An information identification module 30 is used to traverse the filtered curvature radius data set, obtain the sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determine the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point;

[0111] The curve determination module 40 is used to determine the target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete the curve recognition.

[0112] In one embodiment, the information identification module 30 is further used to determine that the current sampling point is the starting point of the curve when the filtered curvature radius is less than the set curve curvature radius threshold, and obtain the first longitudinal distance and the first sampling point index of the curve starting point to obtain the curve segment starting point information, and initialize the curve segment curvature radius data set based on the curve segment starting point information; update the next sampling point to the current sampling point, and store the filtered curvature radius of the current sampling point in the curve segment curvature radius data set until the filtered curvature radius is greater than or equal to the set curve curvature radius threshold, determine that the previous sampling point of the current sampling point is the curve termination point, and obtain the second longitudinal distance and the second sampling point index of the curve termination point to obtain the curve segment termination point information.

[0113] In one embodiment, the curve determination module 40 is further used to determine the curve segment length of the curve segment according to the curve segment starting point information and the curve segment ending point information; when the curve segment length of the curve segment is greater than or equal to a curve length threshold, the curve segment is used as a target curve segment; and the curve information of the target curve segment is obtained to obtain the target curve segment information to complete curve recognition.

[0114] In one embodiment, the curve determination module 40 is further used to obtain a curve segment curvature radius data set of the target curve segment; determine the target curvature radius of the curve segment based on the curve segment curvature radius data set; store the curve segment starting point information, the curve segment ending point information and the target curvature radius of the curve segment in the curve segment data set to obtain the target curve segment information to complete curve identification.

[0115] In one embodiment, the curvature calculation module 20 is further used to determine the lane curvature radius of the sampling point of the virtual center line of the lane according to the coordinates in the self-vehicle coordinate system; smoothing filter the lane curvature radius of the sampling point to obtain a filtered curvature radius data set, wherein the filtered curvature radius data set includes sampling point index information and filtered curvature radius.

[0116] In one embodiment, the curvature calculation module 20 is further used to perform lane line fitting on the virtual center line of the lane according to the coordinates in the vehicle coordinate system to obtain a lane line fitting equation; uniformly sample the virtual center line of the lane according to the lane line fitting equation to obtain the rectangular coordinates of the sampling points; and determine the lane curvature radius of the sampling point according to the rectangular coordinates of the sampling point.

[0117] In one embodiment, the information acquisition module 10 is also used to obtain the longitude and latitude coordinates of the vehicle, the heading angle information of the vehicle, and the longitude and latitude coordinates of the shape point of the virtual center line of the lane within a preset distance in front of the vehicle; and convert the longitude and latitude coordinates of the shape point into coordinates in the vehicle coordinate system according to the longitude and latitude coordinates of the vehicle and the heading angle information of the vehicle.

[0118] The curve recognition device provided by the present application adopts the curve recognition method in the above embodiment, which can solve the technical problem of how to perform real-time recognition of continuous curve information within a certain range in front of the vehicle. Compared with the prior art, the beneficial effects of the curve recognition device provided by the present application are the same as the beneficial effects of the curve recognition method provided by the above embodiment, and other technical features in the curve recognition device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0119] The present application provides a curve recognition device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the curve recognition method in the above-mentioned embodiment one.

[0120] Reference below Figure 6, which shows a schematic diagram of the structure of a curve recognition device suitable for implementing the embodiment of the present application. The curve recognition device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The curve recognition device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] like Figure 6 As shown, the curve recognition device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the curve recognition device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the curve recognition device to communicate with other devices wirelessly or by wire to exchange data. Although the curve recognition device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0122] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0123] The curve recognition device provided by the present application adopts the curve recognition method in the above embodiment, which can solve the technical problem of how to perform real-time recognition of continuous curve information within a certain range in front of the vehicle. Compared with the prior art, the beneficial effects of the curve recognition device provided by the present application are the same as the beneficial effects of the curve recognition method provided by the above embodiment, and other technical features in the curve recognition device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0124] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0126] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the curve recognition method in the above-mentioned embodiment.

[0127] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0128] The computer-readable storage medium may be included in the curve recognition device; or may exist independently without being assembled into the curve recognition device.

[0129] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the curve recognition device, the curve recognition device: obtains the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system; determines the filtered curvature radius data set of the virtual center line of the lane according to the coordinates in the vehicle coordinate system; traverses the filtered curvature radius data set to obtain the sampling point index information and the sampling point filtered curvature radius of the current sampling point, and determines the curve segment starting point information and the curve segment ending point information according to the sampling point index information and the sampling point filtered curvature radius; determines the target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete the curve recognition.

[0130] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0133] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned curve recognition method, and can solve the technical problem of how to perform real-time recognition of continuous curve information within a certain range in front of the vehicle. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the curve recognition method provided by the above-mentioned embodiment, and will not be repeated here.

[0134] The present application also provides a computer program product, including a computer program, which implements the steps of the curve recognition method as described above when executed by a processor.

[0135] The computer program product provided by the present application can solve the technical problem of how to identify continuous curve information within a certain range in front of the vehicle in real time. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the curve recognition method provided by the above embodiment, which will not be repeated here.

[0136] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A curve recognition method, characterized in that: The curve recognition method comprises: Obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system; Determine a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system; Traversing the filtered curvature radius data set, obtaining sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determining the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point; The target curve section information is determined according to the curve section starting point information and the curve section ending point information to complete the curve recognition.

2. The method according to claim 1, characterized in that The step of determining the starting point information and the ending point information of the curve segment according to the sampling point index information and the sampling point filtering curvature radius comprises: When the filtered curvature radius is less than a set curve curvature radius threshold, determining the current sampling point as a curve starting point, acquiring a first longitudinal distance and a first sampling point index of the curve starting point, obtaining curve segment starting point information, and initializing a curve segment curvature radius data set based on the curve segment starting point information; The next sampling point is updated as the current sampling point, and the filtered curvature radius of the current sampling point is stored in the curve segment curvature radius data set until the filtered curvature radius is greater than or equal to the set curve curvature radius threshold, and the previous sampling point of the current sampling point is determined to be the curve termination point, and the second longitudinal distance and the second sampling point index of the curve termination point are obtained to obtain the curve segment termination point information.

3. The method according to claim 1, characterized in that The step of determining target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete curve recognition includes: Determining the length of the curve section according to the curve section starting point information and the curve section ending point information; When the curve section length of the curve section is greater than or equal to the curve length threshold, taking the curve section as a target curve section; The curve information of the target curve section is acquired to obtain the target curve section information to complete the curve recognition.

4. The method according to claim 3, characterized in that The step of acquiring the curve information of the target curve section to obtain the target curve section information to complete the curve recognition includes: Acquire a curve section curvature radius data set of the target curve section; Determining a target curvature radius of the curve section according to the curve section curvature radius data set; The starting point information of the curve segment, the ending point information of the curve segment and the target curvature radius of the curve segment are stored in a curve segment data set to obtain target curve segment information to complete curve recognition.

5. The method according to claim 1, characterized in that The step of determining the filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system includes: Determine the lane curvature radius of the sampling point of the lane virtual center line according to the coordinates in the vehicle coordinate system; The lane curvature radius of the sampling point is smoothed and filtered to obtain a filtered curvature radius data set, wherein the filtered curvature radius data set includes sampling point index information and a filtered curvature radius.

6. The method according to claim 5, characterized in that The step of determining the lane curvature radius of the sampling point of the lane virtual center line according to the coordinates in the vehicle coordinate system includes: Perform lane line fitting on the virtual center line of the lane according to the coordinates in the vehicle coordinate system to obtain a lane line fitting equation; Uniformly sampling the lane virtual center line according to the lane line fitting equation to obtain the rectangular coordinates of the sampling points; The lane curvature radius of the sampling point is determined according to the rectangular coordinates of the sampling point.

7. The method according to any one of claims 1 to 6, characterized in that The step of obtaining the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system includes: Obtain the longitude and latitude coordinates of the vehicle, the heading angle information of the vehicle, and the longitude and latitude coordinates of the shape point of the virtual center line of the lane within a preset distance in front of the vehicle; The longitude and latitude coordinates of the shape point are converted into coordinates in the vehicle coordinate system according to the longitude and latitude coordinates of the vehicle and the heading angle information of the vehicle.

8. A curve recognition device, characterized in that: The device comprises: An information acquisition module is used to obtain the coordinates of the data points of the virtual center line of the lane within a preset distance in front of the vehicle in the vehicle coordinate system; A curvature calculation module, which determines a filtered curvature radius data set of the lane virtual centerline according to the coordinates in the vehicle coordinate system; An information identification module, used to traverse the filtered curvature radius data set, obtain the sampling point index information and the filtered curvature radius of the sampling point of the current sampling point, and determine the starting point information and the ending point information of the curve segment according to the sampling point index information and the filtered curvature radius of the sampling point; The curve determination module is used to determine the target curve segment information according to the curve segment starting point information and the curve segment ending point information to complete the curve recognition.

9. A curve recognition device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the curve recognition method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the curve recognition method according to any one of claims 1 to 7 are implemented.

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