CN115114494A - Freespace edge point processing method and device - Google Patents
Freespace edge point processing method and device Download PDFInfo
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The embodiment of the disclosure provides a processing method, a device, a storage medium and electronic equipment for Freespace edge points, wherein the processing method comprises the steps of screening the Freespace edge points based on first state information to obtain valid Freespace edge points; classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points; and evaluating the state information acquisition quality of the Freespace valid edge points based on the first state information and the second state information. The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
Description
Technical Field
The disclosure relates to the field of intelligent driving control, in particular to a processing method and device for Freespace edge points of an intelligent driving system, a storage medium and electronic equipment.
Background
In the mainstream technology of current intelligent driving, Freespace technology is an indispensable part, and Freespace refers to a driving area of an automobile and includes an area avoiding other automobiles, pedestrians, roadside and the like. The Freespace edge point is a scattered point on the boundary of the travelable area, and the resolution of the Freespace edge point is generally 1 deg. Accurate information of Freespace edge points can provide effective sensing input for an automatic driving system, so that data guarantee is provided for a decision planning layer. However, the existing information of the Freespace edge points often has the problems of frequent jump, poor stability and the like,
disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method and an apparatus for processing a Freespace edge point, a storage medium, and an electronic device, so as to solve the problems in the prior art.
In order to solve the technical problem, the embodiment of the present disclosure adopts the following technical solutions:
a method of processing Freespace edge points, comprising:
screening the Freespace edge points based on first state information to obtain Freespace effective edge points;
classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points;
and evaluating the state information acquisition quality of the Freespace effective edge points on the basis of the first state information and the second state information.
In some embodiments, before the filtering the Freespace edge point based on the first state information to obtain the Freespace valid edge point, the method includes:
the method comprises the steps of obtaining first state information of Freespace edge points through a camera device, wherein the first state information at least comprises a longitudinal distance and a transverse distance of each detected Freespace edge point in the direction.
In some embodiments, the screening the Freespace edge point based on the first state information to obtain a Freespace valid edge point includes:
generating a first cache queue and a second cache queue based on the first state information of the Freespace edge point;
respectively determining a longitudinal threshold value and a transverse threshold value based on the second cache queue;
determining a count value in the first buffer queue based on the vertical threshold and the horizontal threshold;
and determining Freespace effective edge points in the Freespace edge points based on the counting value.
In some embodiments, the generating a first buffer queue and a second buffer queue based on the first state information of the Freespace edge point includes:
generating the first cache queue and the second cache queue based on the historical difference value pairs of the Freespace edge points, wherein the maximum number of the historical difference value pairs stored in the second cache queue is k of the maximum number of the historical difference value pairs stored in the first cache queue 1 Multiple, wherein k 1 The historical difference value pairs comprise longitudinal distance historical difference values and transverse distance historical difference values which are positive integers.
In some embodiments, the longitudinal threshold and the lateral threshold are respectively set to values corresponding to predetermined percentiles of the longitudinal distance history difference and the lateral distance history difference in the second buffer queue.
In some embodiments, the classifying the Freespace valid edge point to obtain the second state information of the Freespace valid edge point includes:
constructing an original data matrix of the Freespace effective edge points;
establishing a similar matrix between any two Freespace effective edge points based on the original data matrix;
and determining the attribute type of the Freespace valid edge point based on the similarity matrix.
In some embodiments, the evaluating the state information acquisition quality of the Freespace valid edge point based on the first state information and the second state information includes:
acquiring a first fitting function of the first state information of the Freespace effective edge points acquired by a camera device, wherein the Freespace effective edge points have preset second state information;
acquiring a second fitting function of the first state information of the Freespace effective edge points acquired based on a laser radar device;
and evaluating the state information acquisition quality of the Freespace effective edge points on the basis of the first fitting function and the second fitting function.
The embodiment of the present disclosure further provides a processing apparatus for a Freespace edge point, which includes:
the first obtaining module is used for screening the Freespace edge points based on the first state information to obtain the Freespace effective edge points;
the second obtaining module is used for classifying the Freespace effective edge points to obtain second state information of the Freespace effective edge points;
and the evaluation module is used for evaluating the state information acquisition quality of the Freespace effective edge points on the basis of the first state information and the second state information.
The present disclosure also provides a storage medium storing a computer program which, when executed by a processor, performs the steps of any of the methods described above.
The present disclosure also provides an electronic device, at least comprising a memory and a processor, wherein the memory has a computer program stored thereon, and the processor implements the steps of any one of the above methods when executing the computer program on the memory.
The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic step diagram of a method for processing Freespace edge points according to an embodiment of the present disclosure;
FIG. 2 is a schematic step diagram of a Freespace edge point processing method according to an embodiment of the present disclosure;
FIG. 3 is a schematic step diagram illustrating a processing method of Freespace edge points according to an embodiment of the present disclosure;
fig. 4 is a schematic step diagram of a processing method of a Freespace edge point according to an embodiment of the present disclosure.
Detailed Description
Various aspects and features of the disclosure are described herein with reference to the drawings.
It will be understood that various modifications may be made to the embodiments of the present application. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the disclosure in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
A first embodiment of the present disclosure provides a processing method for Freespace edge points of an intelligent driving system, which facilitates a vehicle to perform an intelligent driving function, as shown in fig. 1, and includes the following steps:
s101, screening the Freespace edge points based on the first state information, and obtaining the Freespace effective edge points.
In the step, the Freespace edge points are screened based on the first state information, and Freespace valid edge points are obtained. Specifically, the Freespace edge points are used for detecting road edges, road boundaries and the like on roads.
Before this step, first state information of the Freespace edge point needs to be acquired first. The Freespace edge point refers to a scattered point on the boundary of a driving area on a road, wherein in a specific embodiment, the Freespace edge point includes 91 sampling points with the resolution of 1 degree in a range of ± 45 degrees in a forward area of the vehicle, and the data sampling frequency can be set to be 25 Hz.
Further, the first state information may be acquired, for example, through a camera device or other devices disposed on the vehicle, where a vehicle-mounted camera may be taken as a specific example. The first state information of the Freespace edge points at least comprises data information of longitudinal distance, transverse distance, quality and the like detected in the direction of each Freespace edge point. In a specific embodiment, a CANoe (CAN open environment) and a vehicle-mounted camera may be connected by a CAN line, and a dbc (data Base CAN) file is loaded in the CANoe to analyze data of the Freespace edge point collected by the vehicle-mounted camera to obtain first state information of the Freespace edge point. Among them, CANoe here is used for the development, testing and analysis of CAN bus.
Considering the reasons of large jump of the Freespace edge point, poor signal stability and the like, the Freespace edge point needs to be used after being screened. For example, after the Freespace edge point and the first state information are obtained, the first state information of the Freespace edge point may be filtered to obtain a Freespace valid edge point in the Freespace edge point.
Further, after the first state information of the Freespace edge points is obtained, in this step, Freespace valid edge points in the Freespace edge points are obtained based on the first state information. Specifically, for example, after the first state information of the Freespace edge points of the vehicle is obtained, a Freespace valid edge point is screened, and in particular, 91 Freespace edge points within a range of ± 45deg of a forward area of the vehicle are screened respectively, as shown in fig. 2, where the screening process includes the following steps:
s201, generating a first buffer queue and a second buffer queue based on the first state information of the Freespace edge point.
In this step, a first buffer queue and a second buffer queue are generated based on the first state information of the Freespace edge point. Wherein the first and second cache queues are generated based on historical difference pairs of the Freespace edge points, wherein a maximum number of historical difference pairs stored in the second cache queue is k of a maximum number of historical difference pairs stored in the first cache queue 1 Multiple, wherein k 1 The historical difference value pair comprises a longitudinal distance historical difference value and a transverse distance historical difference value which are positive integers.
Specifically, in a first cache queue generated based on first state information of the Freespace edge point, after data of the Freespace edge point in any direction is acquired, a difference (generally an absolute value) between a previous frame and a current frame, which is acquired by acquiring a longitudinal distance and a transverse distance of the Freespace edge point, is solved to generate the first cache queue, and meanwhile, if quality information of the Freespace edge point indicates that pixel quality of the current frame is low, the Freespace edge point in the current frame does not participate in calculation.
Further, the difference data in the first buffer queue keeps updating continuously, and the updating frequency is set to 0.02s, wherein at most M is stored in the first buffer queue TH A pair of historical difference values, wherein each pair of historical difference values comprises a difference based on a longitudinal distance and a lateral distance of each of the Freespace edge points, where M TH For a fixed constant, for example, M may be set TH And more than or equal to 20, excessive historical difference values can automatically overflow.
In addition, in this step, a second cache queue is generated based on the first state information of the Freespace edge point. Specifically, here, the second buffer queue also keeps updating continuously, the updating frequency is set to 0.02s, and the second buffer queueSave at most M TH *k 1 A historical difference value pair, k 1 Is a positive integer, wherein each historical difference value pair comprises a difference value based on the longitudinal distance and the lateral distance of each Freespace edge point, and excessive historical difference values will automatically overflow.
S202, respectively determining a longitudinal threshold value and a transverse threshold value based on the second buffer queue.
After the first buffer queue and the second buffer queue are generated based on the first state information of the Freespace edge point in the step S201, in this step, the vertical threshold and the horizontal threshold are respectively determined based on the second buffer queue. And setting the longitudinal threshold and the transverse threshold as numerical values corresponding to preset percentiles of the longitudinal distance historical difference and the transverse distance historical difference in the second cache queue respectively.
Specifically, it is necessary to set the vertical threshold value to X in this step TH X in this case TH A value corresponding to, for example, 95% percentile of the historical difference of the longitudinal distances in the second cache queue, that is, if, for example, 95% of the historical difference of the longitudinal distances in the second cache queue is smaller than x and 5% of the historical difference of the longitudinal distances in the second cache queue is greater than x, it is determined that x is a value corresponding to 95% percentile of the historical difference of the longitudinal distances in the second cache queue, and it is also necessary to set a horizontal threshold value to be Y TH Y herein TH Equal to the value corresponding to the 95% percentile of the historical difference of the lateral distances in said second buffer queue, in the same way as the above-mentioned longitudinal threshold.
S203, determining a count value in the first buffer queue based on the longitudinal threshold and the transverse threshold.
After the vertical threshold and the horizontal threshold are respectively determined based on the second buffer queue through the above step S202, in this step, a count value is determined in the first buffer queue based on the vertical threshold and the horizontal threshold. Specifically, the magnitudes of the longitudinal distance historical difference and the transverse distance historical difference in the historical difference pairs in the first cache queue exceed the longitudinal threshold and the transverse threshold respectivelyCounting the number frequency of the threshold value and setting the counting value as N TH 。
And S204, determining Freespace effective edge points in the Freespace edge points based on the count value.
After the count value is determined in the first cache queue based on the vertical threshold and the horizontal threshold in step S203, in this step, based on the count value, a Freespace valid edge point is determined in the Freespace edge points. Specifically, the count value N of the Freespace edge point in a certain specified direction TH Greater than or equal to M TH *k 2 At this point k 2 The scale factor is a proportionality coefficient, the range of the proportionality coefficient is 0-1, the Freespace edge point in the direction jumps violently near the current frame, and the Freespace edge point in the direction is considered as an invalid point until the count value N TH Less than M TH *k 2 Then, the Freespace edge point in the direction is considered to be a Freespace valid edge point.
S102, classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points.
After the Freespace edge points are screened based on the first state information in the step S101 to obtain Freespace valid edge points, in this step, the Freespace valid edge points are classified to obtain second state information of the Freespace valid edge points. Specifically, the Freespace valid edge points obtained through screening in the above steps are classified, for example, a category attribute of each Freespace valid edge point is obtained, so as to implement feature classification of Freespace valid edge points formed by different object types, where the category attribute is used for representing different object types, such as buildings, other vehicles, pedestrians, and the like, as shown in fig. 3, the specific implementation steps are as follows:
s301, constructing an original data matrix of the Freespace effective edge points.
In this step, in the process of acquiring the second state information of the Freespace valid edge point, an original data matrix of the Freespace valid edge point is constructed, where the original data matrix is represented as follows:
A＝(y ij ) n×m
wherein i is 1,2,3, which respectively correspond to the X coordinate, the Y coordinate and the motion speed of the Freespace valid edge point; j ═ 1,2, … 91, which represents the number of the Freespace valid edge point; y is ij I.e. the i-th parameter representing the j-th Freespace valid edge point, so that n is 3; and m is 91.
Further, normalization processing may be performed on data in the raw data matrix of the Freespace valid edge point, where a normalized sample set is X ═ (X) ij ) n×m Wherein:
in the formula, y jmax 、y jmin The maximum value and the minimum value of the original data of the Freespace edge point of the j column are respectively represented.
S302, establishing a similar matrix between any two Freespace effective edge points based on the original data matrix.
After the original data matrix of the Freespace valid edge points is constructed through the above step S301, in this step, a similarity matrix is established between any two Freespace valid edge points based on the original data matrix. Further, a similar matrix is established between any two Freespace effective edge points, which is specifically as follows:
wherein:
and S303, determining the attribute type of the Freespace valid edge point based on the similarity matrix.
After the similarity matrix is established between any two Freespace valid edge points based on the original data matrix in the step S302, in this step, the attribute type of the Freespace valid edge point is determined based on the similarity matrix. Specifically, an R matrix is established on the basis of the similarity matrix, as follows:
t(R)＝R 2
wherein the following expression is set:
t(R)＝(r′ ij ) 91×91 ；
wherein, take k 3 ＝0.9。
S304, determining the second state information based on the original data matrix.
After constructing the raw data matrix of the Freespace edge point through the above-described step S301, in this step, the second state information is determined based on the raw data matrix. If r 'according to the determined original data matrix' ij If (λ) is 1, the ith Freespace valid edge point and the jth Freespace valid edge point are considered to belong to the same object, for example, to belong to the same object; in this way, after all the Freespace valid edge points are calculated and subjected to class judgment, the class attributes of all the Freespace valid edge points are further obtained.
In addition, the processing method further comprises:
s103, evaluating the state information acquisition quality of the Freespace effective edge points based on the first state information and the second state information.
After the first state information and the second state information of the Freespace valid edge point are respectively acquired through the steps S101 and S102, the state information acquisition quality of the Freespace valid edge point is evaluated based on the first state information and the second state information in the step. That is, this step was used for test and result evaluation. Specifically, after the first state information, for example, including longitudinal distance information, lateral distance information, and the like, of the Freespace valid edge points of the objects having the same category attribute in the Freespace edge points after the processing is output based on the above steps S101 to S103, the vehicle is actually tested for the same object to verify the quality of state information collection, as shown in fig. 4, which specifically includes:
s401, acquiring a first fitting function of the first state information of the Freespace effective edge points acquired by a camera device, wherein the Freespace effective edge points have preset second state information;
s402, acquiring a second fitting function of the first state information of the Freespace effective edge points acquired by the laser radar device;
and S403, evaluating the state information acquisition quality of the Freespace effective edge points based on the first fitting function and the second fitting function.
In the test embodiment, for example, a vehicle-mounted camera is mounted on the vehicle, a curb-type road boundary exists on the right side of the vehicle, wherein Freespace edge points of the curb-type road boundary are adopted, Freespace edge points of the curb-type road boundary acquired by the vehicle-mounted camera are processed through the steps, Freespace effective edge points related to the right curb are acquired, and the Freespace effective edge points of the same category attribute are fitted by a polynomial fitting method to form a polynomial, and the polynomial fitting method specifically includes:
based on the longitudinal distance and the transverse distance data of the Freespace effective edge points of the same type attribute of the road boundary in the form of the right curb stone acquired by the vehicle-mounted camera on the vehicle, for example, the following table is formed:
| number k | 1 | 2 | … | m |
| Longitudinal distance x | x 1 | x 2 | … | x m |
| Transverse distance y | Y 1 | Y 2 | … | Y m |
Inputting longitudinal distance data x ═ x in fitting equation 1 、x 2 …x m ]And transverse distance data Y ═ Y 1 、Y 2 …Y m ]The order d of the fitting equation for the Freespace valid edge points of the road boundary is then determined by the following least squares based procedure, which may be as follows
j＝10；
for i＝1:j
y 2 ＝polyfit(x,y,i)；
Y＝polyval(y 2 ,x)；
if sum(Y-y) 2 <0.05
d＝i；
break；
end
end
Thus, the order d of the fitting equation when the sum of the squares of the error values is less than 0.05 can be obtained by the above procedure, and the function of the least square method is further input:
y 1 ＝polyfit(x,y,d)
the coefficients of the polynomial fit function are thus obtained:
a 0 、a 1 ……、a d
wherein d is the order of the fitting equation, a i Is corresponding to x d-i So that a fitting function of the Freespace valid edge points of the road boundary is obtained as follows:
further, in order to implement the verification in this step, it is further required to obtain the real position information of the road boundary point as a true value for performing the verification, for example, an RT-Range system and a computing device (e.g., a computer) may be mounted and fixed on the moving platform, the RT-Range system is connected with the computing device through an ethernet cable to push the moving platform to move along the right road boundary, and the RT-Range system transmits the position information of the road boundary point to the computing device through the ethernet cable to implement the collection of the road boundary point information, so as to obtain the longitudinal distance and the transverse distance data of the road boundary point collected by the RT-Range system:
| number k | 1 | 2 | … | q |
| Longitudinal distance x | x 1 | x 2 | … | x q |
| Transverse distance y | Y 1 | Y 2 | … | Y q |
After re-inputting the longitudinal distance data x ═ x 1 、x 2 …x q ]The transverse distance data Y ═ Y 1 、Y 2 …Y q ]Post-input least squares function:
y 2 ＝polyfit(x,y,d)
simultaneously obtaining polynomial fitting function coefficients:
b 0 、b 1 ……、b d
wherein, b i Is corresponding to x d-i So as to obtain a fitting function of the road boundary points measured based on the RT-Range system:
a is described 0 、a 1 ……、a d And b 0 、b 1 ……、b d After the parameter calculation is completed, analyzing the result to evaluate the quality of the Freespace valid edge point information after the output processing of step S101, where a quality factor is defined:
if Q is less than or equal to Q TH Here Q is TH The quality evaluation threshold is the quality evaluation threshold, the quality of the information of the Freespace effective edge point after the output processing is considered to be better, if Q is>Q TH If the quality of the output processed information of the Freespace valid edge point is not good, the selection mode of the Freespace edge point needs to be optimized.
The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
A second embodiment of the present disclosure relates to a processing apparatus for a Freespace edge point, which is configured to execute the processing method in the first embodiment, and includes a first obtaining module, a second obtaining module, and an evaluating module, where the first obtaining module, the second obtaining module, and the evaluating module are coupled to each other, where:
the first obtaining module is used for screening the Freespace edge points based on first state information to obtain the Freespace effective edge points;
the second obtaining module is configured to classify the Freespace valid edge point to obtain second state information of the Freespace valid edge point;
and the evaluation module is used for evaluating the state information acquisition quality of the Freespace effective edge point based on the first state information and the second state information.
The first obtaining module is further configured to obtain first state information of the Freespace edge points through a camera device, where the first state information at least includes a longitudinal distance and a transverse distance in a direction where each of the Freespace edge points is detected.
The first obtaining module includes:
the generating unit is used for generating a first cache queue and a second cache queue based on the first state information of the Freespace edge point;
a threshold determination unit, configured to determine a vertical threshold and a horizontal threshold based on the second buffer queue, respectively;
a count value determination unit configured to determine a count value in the first buffer queue based on the vertical threshold and the horizontal threshold;
and the valid point determining unit is used for determining a Freespace valid edge point from the Freespace edge points based on the counting value.
The generating unit is specifically configured to generate the first cache queue and the second cache queue based on the historical difference pair of the Freespace edge point, where a maximum number of historical difference pairs stored in the second cache queue is k of a maximum number of historical difference pairs stored in the first cache queue 1 Multiple, wherein k 1 The historical difference value pairs comprise longitudinal distance historical difference values and transverse distance historical difference values which are positive integers.
Further, the longitudinal threshold and the transverse threshold are respectively set as values corresponding to predetermined percentiles of the longitudinal distance historical difference and the transverse distance historical difference in the second cache queue.
The second obtaining module includes:
the construction unit is used for constructing an original data matrix of the Freespace effective edge point;
the establishing unit is used for establishing a similar matrix between any two Freespace effective edge points based on the original data matrix;
and the attribute type determining unit is used for determining the attribute type of the Freespace effective edge point based on the similarity matrix.
The evaluation module comprises:
a first fitting function obtaining unit, configured to obtain a first fitting function based on the first state information of the Freespace effective edge point acquired by the image pickup device, where the Freespace effective edge point has predetermined second state information;
a second fitting function obtaining unit, configured to obtain a second fitting function based on the first state information of the Freespace effective edge point acquired by the laser radar device;
and the evaluation unit is used for evaluating the state information acquisition quality of the Freespace effective edge points on the basis of the first fitting function and the second fitting function.
The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
A third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program that, when executed by a processor, implements the method provided by the first embodiment of the present disclosure, including the following steps S11 to S13:
s11, screening the Freespace edge points based on the first state information to obtain Freespace effective edge points;
s12, classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points;
s13, evaluating the state information collection quality of the Freespace effective edge point based on the first state information and the second state information.
Further, the computer program realizes the other methods provided by the first embodiment of the disclosure when being executed by the processor
The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
A fourth embodiment of the present disclosure provides an electronic device, which includes at least a memory and a processor, the memory having a computer program stored thereon, the processor implementing the method provided by any of the embodiments of the present disclosure when executing the computer program on the memory. Illustratively, the electronic device computer program steps are as follows S21-S23:
s21, screening the Freespace edge points based on the first state information to obtain Freespace effective edge points;
s22, classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points;
s23, evaluating the state information collection quality of the Freespace effective edge point based on the first state information and the second state information.
Further, the processor also executes the computer program in the fourth embodiment described above
The embodiment of the disclosure can filter invalid points in the Freespace edge points through an effective screening algorithm, so as to form stable and effective Freespace information, and has great significance for improving the Freespace edge point applicability and guaranteeing the intelligent driving safety.
The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the passenger computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any storage medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above 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 a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The 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, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.
Claims (10)
1. A method for processing Freespace edge points is characterized by comprising the following steps:
screening the Freespace edge points based on first state information to obtain Freespace effective edge points;
classifying the Freespace effective edge points to acquire second state information of the Freespace effective edge points;
and evaluating the state information acquisition quality of the Freespace effective edge points on the basis of the first state information and the second state information.
2. The processing method according to claim 1, wherein before said filtering the Freespace edge point based on the first status information and obtaining a Freespace valid edge point, the processing method comprises:
the method comprises the steps of obtaining first state information of Freespace edge points through a camera device, wherein the first state information at least comprises a longitudinal distance and a transverse distance of each detected Freespace edge point in the direction.
3. The processing method according to claim 1, wherein the filtering the Freespace edge point based on the first status information to obtain a Freespace valid edge point includes:
generating a first cache queue and a second cache queue based on the first state information of the Freespace edge point;
respectively determining a longitudinal threshold value and a transverse threshold value based on the second cache queue;
determining a count value in the first buffer queue based on the vertical threshold and the horizontal threshold;
and determining Freespace effective edge points in the Freespace edge points based on the counting value.
4. The processing method according to claim 3, wherein the generating a first buffer queue and a second buffer queue based on the first state information of the Freespace edge point comprises:
generating the first cache queue and the second cache queue based on the historical difference value pair of the Freespace edge point, wherein the maximum number of the historical difference value pairs stored in the second cache queue is k of the maximum number of the historical difference value pairs stored in the first cache queue 1 Multiple, wherein k 1 The historical difference value pairs comprise longitudinal distance historical difference values and transverse distance historical difference values which are positive integers.
5. The processing method according to claim 4, wherein the vertical threshold and the horizontal threshold are respectively set to values corresponding to predetermined percentiles of the vertical distance history difference and the horizontal distance history difference in the second buffer queue.
6. The processing method according to claim 1, wherein the classifying the Freespace valid edge point to obtain the second state information of the Freespace valid edge point comprises:
constructing an original data matrix of the Freespace effective edge points;
establishing a similar matrix between any two Freespace effective edge points based on the original data matrix;
and determining the attribute type of the Freespace valid edge point based on the similarity matrix.
7. The processing method according to claim 1, wherein said evaluating the state information collection quality of the Freespace valid edge point based on the first state information and the second state information comprises:
acquiring a first fitting function of the first state information of the Freespace effective edge points acquired by a camera device, wherein the Freespace effective edge points have preset second state information;
acquiring a second fitting function of the first state information of the Freespace effective edge points acquired based on a laser radar device;
and evaluating the state information acquisition quality of the Freespace valid edge points based on the first fitting function and the second fitting function.
8. A device for processing Freespace edge points, comprising:
the first obtaining module is used for screening the Freespace edge points based on the first state information to obtain the Freespace effective edge points;
the second obtaining module is used for classifying the Freespace effective edge points to obtain second state information of the Freespace effective edge points;
and the evaluation module is used for evaluating the state information acquisition quality of the Freespace effective edge point based on the first state information and the second state information.
9. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 7 when executed by a processor.
10. An electronic device comprising at least a memory, a processor, the memory having a computer program stored thereon, wherein the processor, when executing the computer program on the memory, is adapted to carry out the steps of the method of any of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210700010.0A CN115114494A (en) | 2022-06-20 | 2022-06-20 | Freespace edge point processing method and device |
| PCT/CN2023/092611 WO2023246342A1 (en) | 2022-06-20 | 2023-05-06 | Processing method and apparatus for freespace edge point |
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| CN202210700010.0A CN115114494A (en) | 2022-06-20 | 2022-06-20 | Freespace edge point processing method and device |
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Cited By (1)
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| WO2023246342A1 (en) * | 2022-06-20 | 2023-12-28 | 中国第一汽车股份有限公司 | Processing method and apparatus for freespace edge point |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2018204656A1 (en) * | 2017-05-03 | 2018-11-08 | Mobileye Vision Technologies Ltd. | Detection and classification systems and methods for autonomous vehicle navigation |
| CN111754583A (en) * | 2020-05-29 | 2020-10-09 | 江苏大学 | Automatic method for vehicle-mounted three-dimensional laser radar and camera external parameter combined calibration |
| CN113744518B (en) * | 2020-05-30 | 2023-04-18 | 华为技术有限公司 | Method and device for detecting vehicle travelable area |
| CN112164090A (en) * | 2020-09-04 | 2021-01-01 | 杭州海康威视系统技术有限公司 | Data processing method and device, electronic equipment and machine-readable storage medium |
| CN113064179A (en) * | 2021-03-22 | 2021-07-02 | 上海商汤临港智能科技有限公司 | Point cloud data screening method and vehicle control method and device |
| CN115114494A (en) * | 2022-06-20 | 2022-09-27 | 中国第一汽车股份有限公司 | Freespace edge point processing method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023246342A1 (en) * | 2022-06-20 | 2023-12-28 | 中国第一汽车股份有限公司 | Processing method and apparatus for freespace edge point |
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