A method and device for processing freespace edge points

Abstract

The disclosed embodiment provides a method, device, storage medium and electronic device for processing Freespace edge points, wherein the processing method comprises screening the Freespace edge points based on first state information to obtain Freespace valid edge points; classifying the Freespace valid edge points to obtain second state information of the Freespace valid edge points; and evaluating the state information collection quality of the Freespace valid edge points based on the first state information and the second state information. The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance to improving the applicability of Freespace edge points and ensuring the safety of smart driving cars.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent driving control, and in particular to a method, device, storage medium and electronic device for processing Freespace edge points of an intelligent driving system. Background Art

[0002] Freespace technology has become an indispensable part of the current mainstream intelligent driving technology. Freespace here refers to the drivable area of ​​the car, which includes areas to avoid other cars, pedestrians, roadsides, etc. Among them, Freespace edge points refer to scattered points on the boundary of the drivable area. Generally, the resolution of Freespace edge points is 1deg. Accurate Freespace edge point information can provide effective perception input for the autonomous driving system, thereby providing data guarantee for the decision-making planning layer. However, the existing Freespace edge point information often has problems such as frequent jumps and poor stability. Summary of the invention

[0003] The purpose of the embodiments of the present disclosure is to provide a method, device, storage medium and electronic device for processing Freespace edge points to solve the problems existing in the prior art.

[0004] In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:

[0005] A method for processing a Freespace edge point, comprising:

[0006] Screening the Freespace edge points based on the first state information to obtain Freespace valid edge points;

[0007] Classifying the Freespace valid edge points to obtain second state information of the Freespace valid edge points;

[0008] The quality of state information collection of the Freespace effective edge point is evaluated based on the first state information and the second state information.

[0009] In some embodiments, before the Freespace edge points are screened based on the first state information to obtain the Freespace valid edge points, the method includes:

[0010] The first state information of the Freespace edge point is acquired by a camera device, wherein the first state information at least includes a longitudinal distance and a lateral distance in a direction where each Freespace edge point is detected.

[0011] In some embodiments, the filtering the Freespace edge points based on the first state information to obtain the Freespace valid edge points includes:

[0012] Based on the first state information of the Freespace edge point, generating a first cache queue and a second cache queue;

[0013] Determine a longitudinal threshold and a lateral threshold respectively based on the second cache queue;

[0014] determining a count value in the first cache queue based on the longitudinal threshold and the lateral threshold;

[0015] Based on the count value, a Freespace valid edge point is determined among the Fressspace edge points.

[0016] In some embodiments, generating the first cache queue and the second cache queue based on the first state information of the Freespace edge point includes:

[0017] The first cache queue and the second cache queue are generated based on the historical difference pairs of the Freespace edge points, wherein the maximum number of historical difference pairs stored in the second cache queue is k1 times the maximum number of historical difference pairs stored in the first cache queue, wherein k1 is a positive integer, and the historical difference pairs include longitudinal distance historical differences and lateral distance historical differences.

[0018] In some embodiments, the longitudinal threshold and the transverse threshold are respectively set to values ​​corresponding to predetermined percentiles of the longitudinal distance history difference and the transverse distance history difference in the second cache queue.

[0019] In some embodiments, the classifying the Freespace effective edge points to obtain the second state information of the Freespace effective edge points includes:

[0020] Constructing the original data matrix of the Freespace effective edge points;

[0021] Establishing a similarity matrix between any two of the Freespace valid edge points based on the original data matrix;

[0022] The attribute category of the Freespace effective edge point is determined based on the similarity matrix.

[0023] In some embodiments, the evaluating the state information collection quality of the Freespace valid edge point based on the first state information and the second state information includes:

[0024] Acquire a first fitting function based on the first state information of the Freespace effective edge point collected by a camera device, wherein the Freespace effective edge point has predetermined second state information;

[0025] Acquire a second fitting function based on the first state information of the Freespace effective edge point collected by a laser radar device;

[0026] The quality of state information collection of the Freespace effective edge points is evaluated based on the first fitting function and the second fitting function.

[0027] The embodiment of the present disclosure further provides a Freespace edge point processing device, which includes:

[0028] A first acquisition module, configured to screen the Freespace edge points based on the first state information to acquire Freespace valid edge points;

[0029] A second acquisition module, configured to classify the Freespace effective edge points to acquire second state information of the Freespace effective edge points;

[0030] An evaluation module is used to evaluate the state information collection quality of the Freespace effective edge point based on the first state information and the second state information.

[0031] The present disclosure also provides a storage medium storing a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0032] The present disclosure also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program on the memory.

[0033] The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance for improving the applicability of Freespace edge points and ensuring the safety of smart driving cars. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present disclosure 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, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 A schematic diagram of the steps of a method for processing Freespace edge points according to an embodiment of the present disclosure;

[0036] Figure 2 A schematic diagram of the steps of a method for processing Freespace edge points according to an embodiment of the present disclosure;

[0037] Figure 3 A schematic diagram of the steps of a method for processing Freespace edge points according to an embodiment of the present disclosure;

[0038] Figure 4 The figure is a schematic diagram of the steps of the method for processing Freespace edge points according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0040] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0041] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0042] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0043] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0044] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0045] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0046] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0047] The first embodiment of the present disclosure provides a method for processing freespace edge points for an intelligent driving system, wherein the method facilitates a vehicle to perform intelligent driving functions, such as Figure 1 As shown, it includes the following steps:

[0048] S101, screening the Freespace edge points based on first state information to obtain Freespace valid edge points.

[0049] In this step, the Freespace edge points are screened based on the first state information to obtain Freespace valid edge points. Specifically, the Freespace edge points are used to detect curbs, road boundaries, etc. on the road.

[0050] Before this step, it is necessary to first obtain the first state information of the Freespace edge point. The Freespace edge point here refers to the scattered points on the boundary of the drivable area on the road, wherein, in a specific implementation, the Freespace edge point here includes a total of 91 sampling points with a resolution of 1° within the range of ±45 degrees in the forward area of ​​the vehicle, and the data sampling frequency here can be set to 25Hz.

[0051] Furthermore, the first state information can be collected, for example, by a camera device or other device arranged on the vehicle, wherein a vehicle-mounted camera can be used as a specific example. The first state information of the Freespace edge point here includes at least data information such as the longitudinal distance, lateral distance, and quality detected in the direction of each Freespace edge point. In a specific implementation, CANoe (CAN openenvironment) and the vehicle-mounted camera can be connected via a CAN line, and a DBC (Data Base CAN) file can be loaded in CANoe to parse the data of the Freespace edge point collected by the vehicle-mounted camera to obtain the first state information of the Freespace edge point. Among them, the CANoe here is used for the development, testing and analysis of the CAN bus.

[0052] Considering the large jump of the Fresspace edge point and the poor signal stability, the Freespace edge point needs to be screened before use. For example, after obtaining the Freespace edge point and the first state information, the first state information of the Freespace edge point can be screened to obtain the Freespace valid edge point among the Freespace edge points.

[0053] Furthermore, after obtaining the first state information of the Freespace edge point, in this step, based on the first state information, a Freespace valid edge point is obtained from the Freespace edge point. Specifically, for example, after the first state information of the Freespace edge point located at the vehicle is obtained, the Freespace valid edge points are screened, and in particular, the 91 Freespace edge points within the range of ±45 degrees in the forward area of ​​the vehicle are screened respectively, such as Figure 2 As shown, the screening process here includes the following steps:

[0054] S201: Generate a first cache queue and a second cache queue based on first state information of the Freespace edge point.

[0055] In this step, based on the first state information of the Freespace edge point, a first cache queue and a second cache queue are generated. The first cache queue and the second cache queue are generated based on the historical difference pairs of the Freespace edge point, wherein the maximum number of historical difference pairs stored in the second cache queue is k1 times the maximum number of historical difference pairs stored in the first cache queue, wherein k1 is a positive integer, and the historical difference pairs include longitudinal distance historical differences and lateral distance historical differences.

[0056] Specifically, in the first cache queue generated based on the first state information of the Freespace edge point, after obtaining the data of the Freespace edge point in any direction, the difference between the previous frame and the current frame for obtaining the longitudinal distance and the lateral distance of the Freespace edge point is solved (generally the absolute value is taken) to generate the first cache queue. At the same time, if the quality information of the Freespace edge point indicates that the pixel quality of the current frame is low, the Freespace edge point in the current frame does not participate in the calculation.

[0057] Furthermore, the difference data in the first cache queue is continuously updated, and the update frequency is set to 0.02s, wherein the first cache queue can store at most M TH Historical difference pairs, wherein each historical difference pair includes a difference based on the longitudinal distance and the lateral distance of each Freespace edge point, where M TH is a fixed constant, for example, you can set M TH ≥20, too many historical differences will automatically overflow.

[0058] In addition, in this step, a second cache queue is generated based on the first state information of the Freespace edge point. Specifically, the second cache queue is also continuously updated, and the update frequency is set to 0.02s. The second cache queue can store at most M TH *k1 historical difference pairs, k1 is a positive integer, wherein each historical difference pair includes the difference between the longitudinal distance and the lateral distance based on each Freespace edge point, and too many historical differences will automatically overflow.

[0059] S202: Determine a longitudinal threshold and a transverse threshold respectively based on the second cache queue.

[0060] After the first cache queue and the second cache queue are generated based on the first state information of the Freespace edge point in step S201, in this step, a longitudinal threshold and a lateral threshold are respectively determined based on the second cache queue, wherein the longitudinal threshold and the lateral threshold are respectively set to values ​​corresponding to predetermined percentiles of the longitudinal distance historical difference and the lateral distance historical difference in the second cache queue.

[0061] Specifically, in this step, the vertical threshold needs to be set to X TH , where X TH is equal to the value corresponding to the 95% percentile of the historical difference of the longitudinal distance in the second cache queue, that is, if, for example, 95% of the historical difference of the longitudinal distance in the second cache queue is less than x, and 5% of the historical difference of the longitudinal distance is greater than x, then x is considered to be the value corresponding to the 95% percentile of the historical difference of the longitudinal distance in the second cache queue, and the horizontal threshold needs to be set to Y TH , where Y TH It is equal to the value corresponding to the 95% percentile of the historical difference of the horizontal distance in the second cache queue, and the calculation method here is the same as the above-mentioned vertical threshold.

[0062] S203: Determine a count value in the first cache queue based on the vertical threshold and the horizontal threshold.

[0063] After the longitudinal threshold and the transverse threshold are determined based on the second cache queue in step S202, in this step, a count value is determined in the first cache queue based on the longitudinal threshold and the transverse threshold. Specifically, the frequency of the magnitude of the longitudinal distance historical difference and the transverse distance historical difference in the historical difference value pair in the first cache queue exceeding the longitudinal threshold and the transverse threshold is counted, and the count value is set to N TH .

[0064] S204: Determine a Freespace valid edge point among the Freespace edge points based on the count value.

[0065] After determining the count value in the first cache queue based on the longitudinal threshold and the transverse threshold in step S203, in this step, based on the count value, a Freespace valid edge point is determined in the Freespace edge point. Specifically, when the count value N of the Freespace edge point in a specified direction is TH Greater than or equal to M TH*k2, where k2 is a proportional coefficient ranging from 0 to 1, if the Freespace edge point in this direction jumps violently near the current frame, the Freespace edge point in this direction is considered to be an invalid point until the count value N TH Less than M TH *k2, the Freespace edge point in this direction is considered to be a Freespace valid edge point.

[0066] S102: Classify the Freespace valid edge points to obtain second state information of the Freespace valid edge points.

[0067] After the Freespace edge points are screened based on the first state information to obtain the Freespace valid edge points in the above step S101, in this step, the Freespace valid edge points are classified to obtain the second state information of the Freespace valid edge points. Specifically, the Freespace valid edge points obtained through screening in the above step are classified, and the category attributes of each Freespace valid edge point are obtained to achieve feature division of the Freespace valid edge points formed by different object types. The category attributes here are used to reflect different object types, such as buildings, other vehicles, pedestrians, etc. Figure 3 As shown, the specific implementation steps are as follows:

[0068] S301, constructing the original data matrix of the Freespace effective edge points.

[0069] In this step, in the process of obtaining the second state information of the Freespace effective edge point, the original data matrix of the Freespace effective edge point is constructed, and the original data matrix is ​​expressed as follows:

[0070] A=(y ij ) n×m

[0071] Wherein, i=1,2,3, which correspond to the X coordinate, Y coordinate and movement speed of the Freespace effective edge point respectively; j=1,2,…91, which represents the sequence number of the Freespace effective edge point; y ij That is, it represents the i-th parameter of the j-th Freespace valid edge point, so n=3; m=91.

[0072] Furthermore, the data in the original data matrix of the Freespace effective edge points can be normalized, and the normalized sample set is X=(xij ) n×m ,in:

[0073]

[0074] In the formula, y jmax ,y jmin They represent the maximum and minimum values ​​of the original data of the Freespace edge points in the jth column respectively.

[0075] S302: Establish a similarity matrix between any two of the Freespace valid edge points based on the original data matrix.

[0076] After the original data matrix of the Freespace effective edge points is constructed in step S301, in this step, a similarity matrix is ​​established between any two of the Freespace effective edge points based on the original data matrix. Further, a similarity matrix is ​​established between any two of the Freespace effective edge points, specifically as follows:

[0077]

[0078] in:

[0079]

[0080]

[0081] S303: Determine the attribute category of the Freespace valid edge point based on the similarity matrix.

[0082] After establishing a similarity matrix between any two of the Freespace effective edge points based on the original data matrix in step S302, in this step, the attribute category of the Freespace effective edge point is determined based on the similarity matrix. Specifically, an R matrix is ​​established based on the similarity matrix, as shown below:

[0083] t(R)=R 2

[0084] Among them, set the following expression:

[0085] t(R)=(r′ ij ) 91×91 ;

[0086]

[0087]

[0088] Among them, take k3=0.9.

[0089] S304: Determine the second state information based on the original data matrix.

[0090] After constructing the original data matrix of the Freespace edge points in step S301, in this step, the second state information is determined based on the original data matrix. According to the original data matrix determined above, if r′ ij (λ)=1, it is considered that the i-th Freespace valid edge point and the j-th Freespace valid edge point belong to the same object, for example, the same object; in this way, after calculating and classifying all the Freespace valid edge points, the category attributes of all the Freespace valid edge points are obtained.

[0091] In addition, the processing method also includes:

[0092] S103: Evaluate the quality of state information collection of the Freespace effective edge point based on the first state information and the second state information.

[0093] After respectively acquiring the first state information and the second state information of the Freespace effective edge point through the above steps S101 and S102, in this step, the quality of the state information acquisition of the Freespace effective edge point is evaluated based on the first state information and the second state information. That is, this step is used for testing and result evaluation. Specifically, based on the output of the above steps S101-S103, the first state information of the Freespace effective edge points of objects with the same category attributes in the processed Freespace edge points, such as longitudinal distance and lateral distance information, is used, and then the vehicle is actually tested on the same object to verify the quality of the state information acquisition, such as Figure 4 As shown, specifically including:

[0094] S401, acquiring a first fitting function based on the first state information of the Freespace effective edge point collected by a camera device, wherein the Freespace effective edge point has predetermined second state information;

[0095] S402, obtaining a second fitting function based on the first state information of the Freespace effective edge point collected by a laser radar device;

[0096] S403: Evaluate the quality of state information collection of the Freespace effective edge points based on the first fitting function and the second fitting function.

[0097] In the implementation mode of the test, for example, a vehicle-mounted camera is mounted on the vehicle, and there is a road boundary in the form of a curb on the right side of the vehicle. Here, the Fressspace edge points of the road boundary with the category attribute of the curb are used. After the Freespace edge points of the road boundary are collected by the vehicle-mounted camera and processed through the above steps, the Freespace valid edge points related to the right curb are obtained, and the Freespace valid edge points of the same category attribute are fitted by a polynomial fitting method, so as to fit into a polynomial. The specific method is as follows:

[0098] The longitudinal distance and lateral distance data of the Freespace effective edge points of the same category attribute of the road boundary in the form of the right curb stone collected by the vehicle camera, for example, are formed into the following table:

[0099] Serial number k 1 2 … m Longitudinal distance x <![CDATA[x1]]> <![CDATA[x2]]> … <![CDATA[x m ]]> Horizontal distance y <![CDATA[Y1]]> <![CDATA[Y2]]> … <![CDATA[Y m ]]>

[0100] Enter the longitudinal distance data x = [x1, x2…x m ] and lateral distance data y=[Y1, Y2…Y m ], and then determine the order d of the fitting equation of the Freespace effective edge points of the road boundary by the following least squares method-based procedure, wherein the procedure can be as follows

[0101] j = 10;

[0102] for i=1:j

[0103] y2 = polyfit(x,y,i);

[0104] Y = polyval(y2,x);

[0105] if sum(Yy) 2 <0.05

[0106] d = i;

[0107] break;

[0108] end

[0109] end

[0110] In this way, the above procedure can be used to obtain the order d of the fitting equation when the sum of squared errors is less than 0.05, and further input into the function of the least squares method:

[0111] y1 = polyfit(x,y,d)

[0112] So as to obtain the coefficients of the polynomial fitting function:

[0113] a0, a1, ..., a d

[0114] Among them, d is the order of the fitting equation, a i is the corresponding x d-i The coefficients of , so the fitting function of the Freespace effective edge points of the road boundary can be obtained as follows:

[0115]

[0116] Furthermore, in order to realize the verification in this step, it is also necessary to obtain the real position information of the road boundary point as the true value for verification. For example, the RT-Range system and the computing device (such as a computer) can be installed and fixed on the motion platform. The RT-Range system and the computing device are connected by an Ethernet cable. The motion platform here is pushed to move along the right road boundary. The RT-Range system transmits the position information of the road boundary point to the computing device through the Ethernet cable to realize the collection of the road boundary point information. In this way, the longitudinal distance and lateral distance data of the road boundary point collected by the RT-Range system are obtained:

[0117] Serial number k 1 2 … q Longitudinal distance x <![CDATA[x1]]> <![CDATA[x2]]> … <![CDATA[x q ]]> Horizontal distance y <![CDATA[Y1]]> <![CDATA[Y2]]> … <![CDATA[Y q ]]>

[0118] When re-entering the longitudinal distance data x=[x1, x2…x q ], horizontal distance data y = [Y1, Y2…Y q ] and then enter the least squares function:

[0119] y2=polyfit(x,y,d)

[0120] At the same time, obtain the coefficients of the polynomial fitting function:

[0121] b0, b1, ..., b d

[0122] Among them, b i is the corresponding x d-i The coefficients of , in this way, the fitting function of the road boundary points measured by the RT-Range system can be obtained:

[0123]

[0124] The a0, a1, ..., a d and b0, b1, ..., b d After the parameter calculation is completed, the results are analyzed in order to evaluate the quality of the Freespace effective edge point information after the output processing in step S101. The quality factor is defined here:

[0125]

[0126] If Q≤Q TH , here Q TH is the quality evaluation threshold, and the value is considered to be good. If Q>Q TH , it is considered that the quality of the information of the Freespace effective edge points after the output processing is poor, and the screening method of the Freespace edge points needs to be optimized.

[0127] The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance for improving the applicability of Freespace edge points and ensuring the safety of smart driving cars.

[0128] The second embodiment of the present disclosure relates to a Freespace edge point processing device, which is used to execute the processing method in the first embodiment, and includes a first acquisition module, a second acquisition module and an evaluation module, and the modules are coupled to each other, wherein:

[0129] The first acquisition module is used to screen the Freespace edge points based on the first state information to acquire Freespace valid edge points;

[0130] The second acquisition module is used to classify the Freespace valid edge points to acquire second state information of the Freespace valid edge points;

[0131] The evaluation module is used to evaluate the state information collection quality of the Freespace effective edge point based on the first state information and the second state information.

[0132] The first acquisition module is further used to acquire first state information of the Freespace edge point through a camera device, wherein the first state information at least includes a longitudinal distance and a lateral distance in a direction where each Freespace edge point is detected.

[0133] The first acquisition module includes:

[0134] A generating unit, configured to generate a first cache queue and a second cache queue based on the first state information of the Freespace edge point;

[0135] a threshold determination unit, configured to determine a longitudinal threshold and a lateral threshold respectively based on the second cache queue;

[0136] a count value determining unit, configured to determine a count value in the first cache queue based on the longitudinal threshold and the transverse threshold;

[0137] The effective point determination unit is used to determine the Freespace effective edge point among the Freespace edge points based on the count value.

[0138] The generating unit is specifically used to generate the first cache queue and the second cache queue based on the historical difference pairs of the Freespace edge points, wherein the maximum number of historical difference pairs stored in the second cache queue is k1 times the maximum number of historical difference pairs stored in the first cache queue, wherein k1 is a positive integer, and the historical difference pairs include longitudinal distance historical differences and lateral distance historical differences.

[0139] Furthermore, the longitudinal threshold and the transverse threshold are respectively set to values ​​corresponding to predetermined percentiles of the longitudinal distance history difference and the transverse distance history difference in the second cache queue.

[0140] The second acquisition module includes:

[0141] A construction unit, used to construct an original data matrix of the Freespace effective edge points;

[0142] An establishing unit, used for establishing a similarity matrix between any two of the Freespace valid edge points based on the original data matrix;

[0143] The attribute category determination unit is used to determine the attribute category of the Freespace effective edge point based on the similarity matrix.

[0144] The evaluation module comprises:

[0145] A first fitting function acquisition unit, configured to acquire a first fitting function based on the first state information of the Freespace effective edge point acquired by a camera device, wherein the Freespace effective edge point has predetermined second state information;

[0146] A second fitting function acquisition unit, used to acquire a second fitting function based on the first state information of the Freespace effective edge point collected by the laser radar device;

[0147] An evaluation unit is used to evaluate the state information collection quality of the Freespace effective edge point based on the first fitting function and the second fitting function.

[0148] The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance for improving the applicability of Freespace edge points and ensuring the safety of smart driving cars.

[0149] The third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the first embodiment of the present disclosure is implemented, including the following steps S11 to S13:

[0150] S11, screening the Freespace edge points based on the first state information to obtain Freespace valid edge points;

[0151] S12, classifying the Freespace valid edge points to obtain second state information of the Freespace valid edge points;

[0152] S13: Evaluate the quality of state information collection of the Freespace effective edge point based on the first state information and the second state information.

[0153] Furthermore, when the computer program is executed by the processor, other methods provided by the first embodiment of the present disclosure are implemented.

[0154] The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance for improving the applicability of Freespace edge points and ensuring the safety of smart driving cars.

[0155] The fourth embodiment of the present disclosure provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method provided in any embodiment of the present disclosure when executing the computer program in the memory. Exemplarily, the steps of the electronic device computer program are as follows S21 to S23:

[0156] S21, screening the Freespace edge points based on the first state information to obtain Freespace valid edge points;

[0157] S22, classifying the Freespace valid edge points to obtain second state information of the Freespace valid edge points;

[0158] S23: Evaluate the quality of state information collection of the Freespace effective edge point based on the first state information and the second state information.

[0159] Furthermore, the processor also executes the computer program in the fourth embodiment

[0160] The disclosed embodiment can filter invalid points in Freespace edge points through an effective screening algorithm, thereby forming stable and effective Freespace information, which is of great significance for improving the applicability of Freespace edge points and ensuring the safety of smart driving cars.

[0161] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0162] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0163] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0164] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, 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 passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via 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).

[0165] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, 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 the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0166] 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 disclosure. In this regard, each square 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 implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square 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.

[0167] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0168] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0169] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0170] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0171] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0172] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0173] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A method for processing Freespace edge points, characterized in that: include: The Freespace edge points are screened based on the first state information to obtain the Freespace valid edge points, wherein the first state information at least includes the longitudinal distance and the lateral distance in the direction where each Freespace edge point is detected; Classifying the Freespace valid edge points to obtain second state information of the Freespace valid edge points, where the second state information at least includes a category attribute of the Freespace valid edge points; Evaluate the state information collection quality of the Freespace effective edge point based on the first state information and the second state information; The evaluating the state information collection quality of the Freespace effective edge point based on the first state information and the second state information includes: Acquire a first fitting function based on the first state information of the Freespace effective edge point collected by a camera device, wherein the Freespace effective edge point has predetermined second state information; Acquire a second fitting function based on the first state information of the Freespace effective edge point collected by a laser radar device; Evaluate the quality of state information collection of the Freespace effective edge points based on the first fitting function and the second fitting function; The evaluating the state information collection quality of the Freespace effective edge points based on the first fitting function and the second fitting function includes: fitting the Freespace effective edge points of the same category attributes to obtain a fitting function and a fitting function coefficient; fitting the road boundary points based on the real position information of the road boundary points to obtain a fitting function and a fitting function coefficient; determining a quality factor based on the difference between the two fitting function coefficients and evaluating the state information collection quality of the Freespace effective edge points based on the quality factor.

2. The processing method according to claim 1, characterized in that: Before the Freespace edge points are screened based on the first state information to obtain the Freespace valid edge points, the method includes: The first state information of the Freespace edge point is obtained through a camera device.

3. The processing method according to claim 1, characterized in that: The filtering the Freespace edge points based on the first state information to obtain the Freespace valid edge points includes: Based on the first state information of the Freespace edge point, generating a first cache queue and a second cache queue; Determine a longitudinal threshold and a lateral threshold respectively based on the second cache queue; determining a count value in the first cache queue based on the longitudinal threshold and the lateral threshold; Based on the count value, a Freespace valid edge point is determined among the Freespace edge points.

4. The processing method according to claim 3, characterized in that: The generating a first cache queue and a second cache queue based on the first state information of the Freespace edge point includes: The first cache queue and the second cache queue are generated based on the historical difference pairs of the Freespace edge points, wherein the maximum number of historical difference pairs stored in the second cache queue is k1 times the maximum number of historical difference pairs stored in the first cache queue, wherein k1 is a positive integer, and the historical difference pairs include longitudinal distance historical differences and lateral distance historical differences.

5. The processing method according to claim 4, characterized in that: The longitudinal threshold and the transverse threshold are respectively set to values ​​corresponding to predetermined percentiles of the longitudinal distance history difference and the transverse distance history difference in the second cache queue.

6. The processing method according to claim 1, characterized in that: The classifying the Freespace valid edge points to obtain the second state information of the Freespace valid edge points includes: Constructing the original data matrix of the Freespace effective edge points; Establishing a similarity matrix between any two of the Freespace valid edge points based on the original data matrix; The attribute category of the Freespace effective edge point is determined based on the similarity matrix.

7. A Freespace edge point processing device, characterized in that: include: A first acquisition module is used to screen the Freespace edge points based on first state information to acquire Freespace valid edge points, wherein the first state information at least includes a longitudinal distance and a lateral distance in a direction where each Freespace edge point is detected; A second acquisition module, configured to classify the Freespace effective edge points to acquire second state information of the Freespace effective edge points, wherein the second state information at least includes a category attribute of the Freespace effective edge points; An evaluation module, configured to evaluate the quality of state information collection of the Freespace effective edge point based on the first state information and the second state information; The evaluation module comprises: A first fitting function acquisition unit, configured to acquire a first fitting function based on the first state information of the Freespace effective edge point acquired by a camera device, wherein the Freespace effective edge point has predetermined second state information; A second fitting function acquisition unit, used to acquire a second fitting function based on the first state information of the Freespace effective edge point collected by the laser radar device; An evaluation unit, configured to evaluate the quality of state information collection of the Freespace effective edge points based on the first fitting function and the second fitting function; The evaluation unit is specifically used to fit the Freespace effective edge points of the same category attributes to obtain a fitting function and a fitting function coefficient; fit the road boundary points based on the real position information of the road boundary points to obtain a fitting function and a fitting function coefficient; determine the quality factor based on the difference between the two fitting function coefficients and evaluate the state information collection quality of the Freespace effective edge points based on the quality factor.

8. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program on the memory.

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