A map matching method, device, V2X terminal and storage medium

Abstract

The present application provides a map matching method, device, V2X terminal and storage medium, which relate to the field of intelligent transportation technology, and can determine the weight of each position parameter according to the actual position relationship of the traffic participant object relative to multiple sections to be matched, so as to improve the accuracy of map matching. The method includes: obtaining the motion state information of the traffic participant object and the section information of M sections to be matched; determining the position parameters of the traffic participant object relative to each section to be matched according to the motion state information and the section information of the M sections to be matched, and the position parameters are used to indicate the relative position relationship between the section to be matched and the traffic participant object; determining the position parameter weight according to the position parameter of the traffic participant object relative to the M sections to be matched; determining the target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched.

Description

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on May 10, 2021, with application number 202110507439.3 and application name “A map matching method, device, V2X terminal and storage medium”, all contents of which are incorporated by reference in this application. Technical Field

[0002] The present application relates to the field of intelligent transportation technology, and in particular to a map matching method, device, V2X terminal and storage medium. Background Art

[0003] Map matching technology is the foundation of vehicle to everything (V2X) technology in the field of vehicle-road collaboration. At present, the commonly used map matching method is: according to the vehicle's global positioning system (GPS) information and multiple road section information in the map, the direction weight, distance weight and the weight of the similarity between the shape of the road section and the vehicle trajectory of each road section are calculated. Then, the weighted sum of each weight is calculated using a pre-set weight coefficient to calculate the matching probability of each road section to be matched. And the road section to be matched with the highest matching probability is determined as the optimal road section.

[0004] However, in conventional methods, fixed weight coefficients cannot accurately match a variety of map information, and there is an inaccurate matching problem in some application scenarios. Summary of the invention

[0005] The present application provides a map matching method, device, V2X terminal and storage medium, which can solve the problem of inaccurate road matching in some map information.

[0006] In a first aspect, an embodiment of the present application provides a map matching method, the method comprising:

[0007] Obtain motion state information of a traffic participant object and section information of M sections to be matched, where M is an integer greater than or equal to 1; determine position parameters of the traffic participant object relative to each section to be matched based on the motion state information and the section information of the M sections to be matched, wherein the position parameters are used to indicate the relative position relationship between the section to be matched and the traffic participant object; determine a position parameter weight based on the position parameters of the traffic participant object relative to the M sections to be matched; and determine a target section from the M sections to be matched based on the position parameter weight and the position parameters of the M sections to be matched.

[0008] By using the map matching method provided in the embodiment of the present application, the position parameter weight can be adaptively determined according to the actual position parameters of the traffic participant relative to the M sections to be matched, so that the used position parameter weight is more consistent with the actual position relationship between the traffic participant and the multiple sections to be matched. Thus, the used position parameter weight is universal for a variety of different maps, and the map matching accuracy is improved.

[0009] In a possible implementation, obtaining the road segment information of M to-be-matched road segments includes:

[0010] Acquire map information, the map information including section information of a plurality of road sections; determine the path length and width radius of the longest section among the plurality of road sections based on the section information of the plurality of road sections; determine an adaptive matching distance based on the path length and width radius of the longest section; and filter the section information of M sections to be matched from the section information of the plurality of road sections based on the adaptive matching distance.

[0011] Based on this possible implementation, a square area centered on the traffic participant object can be determined as the search range of the road section to be matched. The V2X terminal can select M road sections located in the square area from the multiple road sections indicated by the map information as the road sections to be matched, and determine the road section information of the M road sections to be matched, thereby improving the map matching efficiency.

[0012] In a possible implementation, the location parameter includes a distance parameter, and determining the distance parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes:

[0013] For each of the to-be-matched sections, determine the actual distance from the traffic participant to the to-be-matched section based on the section information of the to-be-matched section and the motion state information; substitute the actual distance and the minimum distance into the formula The distance parameter of the traffic participant object relative to the road section to be matched is calculated; wherein, m D represents the distance parameter of the traffic participant relative to the to-be-matched road section, G represents the preset communication distance limit, D represents the actual distance, and D min represents the minimum distance, and the minimum distance is the minimum value of the actual distances from the traffic participant to the M road sections to be matched.

[0014] In a possible implementation, the position parameter includes an angle parameter, and determining the angle parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes:

[0015] For each of the road sections to be matched, the supplementary angle of the angle between the driving direction of the traffic participant and the road section to be matched is determined according to the road section information of the road section to be matched and the motion state information; the angle parameter of the traffic participant relative to the road section to be matched is determined according to the ratio of the supplementary angle and the maximum supplementary angle, and the maximum supplementary angle is the maximum value of the supplementary angles of the angles between the driving direction and the M road sections to be matched.

[0016] In a possible implementation, the position parameter includes a diagonal parameter, and determining the diagonal parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes:

[0017] For each of the road sections to be matched, the actual diagonal of the traffic participant object relative to the road section to be matched is determined according to the road section information and the motion state information of the road section to be matched; the diagonal parameter of the traffic participant object relative to the road section to be matched is determined according to the ratio of the actual diagonal and the maximum diagonal, and the maximum diagonal is the maximum value of the actual diagonals of the traffic participant object relative to the M road sections to be matched.

[0018] Based on the above three possible implementation methods, the actual distance, actual angle, and actual diagonal of the traffic participant relative to the to-be-matched road section are converted into corresponding distance parameters, angle parameters, and diagonal parameters to calculate the matching probability. This reduces the influence of the possible large numerical differences between the position parameters of each to-be-matched road section and the mutual influence of the parameters of each dimension on the calculation result of the matching probability. This improves the reliability of the calculated matching probability.

[0019] In a possible implementation, determining the position parameter weight according to the position parameter of the traffic participant object relative to the M to-be-matched road sections includes:

[0020] According to the position parameters of the traffic participant object relative to the M road sections to be matched, the position parameter standard deviation is calculated; the position parameter standard deviation is geometrically scaled to between a preset maximum constraint value and a minimum constraint value; and the position parameter weight is calculated according to the scaled position parameter standard deviation.

[0021] Based on this possible implementation, by calculating the standard deviation of the parameters of the M sections to be matched in each dimension, it can be determined whether the distribution of the parameters of each dimension is consistent. If the distribution is consistent, it means that the parameters of each dimension have little effect on distinguishing the matching probability of each section to be matched. If the distribution is very scattered, it means that the parameters of some dimensions are more unique and have a greater effect on distinguishing the matching probability of each section to be matched. Therefore, based on the standard deviation of the parameters of each dimension, weights are assigned to the parameters of each dimension, so that the assigned weights are more in line with the actual positional relationship between the traffic participants and the multiple sections to be matched, thereby improving the accuracy of the matching probability obtained by subsequent calculations.

[0022] In a possible implementation, determining a target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched includes:

[0023] For each of the to-be-matched sections, weighted sum of the position parameters of the traffic participant objects relative to the to-be-matched section is performed according to the position parameter weight to obtain a matching probability of the to-be-matched section; and a target section is determined from the M to-be-matched sections according to the matching probabilities of the M to-be-matched sections and a preset matching condition; wherein the preset condition includes at least one of the following three conditions:

[0024] Condition 1: Maximum matching probability;

[0025] Condition 2: the to-be-matched road section with the second largest matching probability and the to-be-matched road section with the largest matching probability are different road sections, and the ratio of the second largest matching probability to the largest matching probability is less than a preset threshold;

[0026] Condition three: the to-be-matched road segment with the maximum matching probability meets the preset regional restriction condition.

[0027] Based on this possible implementation method, the problem that the matching probability of the to-be-matched road section is the highest due to the parameter of one dimension being too large, but the to-be-matched road section is actually not within the matching range of the traffic participant can be avoided, thereby ensuring the validity of the selected target road section.

[0028] In a second aspect, an embodiment of the present application provides a map matching device, characterized in that it includes:

[0029] An acquisition unit, used to acquire the motion state information of the traffic participant and the section information of the M sections to be matched;

[0030] A calculation unit, used to determine, based on the motion state information and the section information of the M sections to be matched, a position parameter of the traffic participant object relative to each of the sections to be matched, wherein the position parameter is used to indicate a relative position relationship between the sections to be matched and the traffic participant object;

[0031] The calculation unit is further used to determine the position parameter weight according to the position parameter of the traffic participant object relative to the M road sections to be matched;

[0032] A matching unit is used to determine a target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched.

[0033] In a third aspect, the present application provides a V2X terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0036] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A process of an embodiment of a map matching method provided in this application Figure 1 ;

[0038] Figure 2 A process of an embodiment of a map matching method provided in this application Figure 2 ;

[0039] Figure 3 A schematic diagram of the relative position relationship between a road section and traffic participants provided for this application;

[0040] Figure 4 A schematic diagram of the structure of a control device provided in this application;

[0041] Figure 5A schematic diagram of the structure of a V2X terminal provided in this application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0044] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0045] In the currently commonly used map matching methods, a pre-designed fixed weight coefficient is usually used to perform weighted summation of parameters in different dimensions such as direction, distance, driving trajectory and section shape between the vehicle and the road section to be matched, so as to calculate the matching probability of each road section to be matched. This results in some maps having high matching accuracy and some maps having low matching accuracy. To this end, the present application provides a map matching method that is universal for a variety of map information, and determines the weights of various position parameters according to the actual position relationship of the traffic participant relative to the multiple road sections to be matched. The weights used are made more consistent with the actual position relationship between the traffic participant and the multiple road sections to be matched, thereby improving the map matching accuracy.

[0046] The map matching method provided in this application is exemplarily described below in conjunction with specific embodiments.

[0047] like Figure 1 As shown, it is a flowchart of an embodiment of a map matching method provided by the present application. The execution subject of the method is a V2X terminal carried by a traffic participant. The V2X terminal may be but not limited to various smart phones, portable notebooks, tablet computers, smart wearable devices or dedicated smart V2X terminals, etc. The communication mode that can be realized by the V2X terminal includes but is not limited to V2X, 4G, 5G, etc.

[0048] See also Figure 1 , the method may include:

[0049] S101, obtaining motion state information of traffic participants and section information of M sections to be matched.

[0050] The motion state information of the traffic participant object is used to describe the motion trajectory of the traffic participant object, and may include multiple position information in the motion trajectory of the traffic participant object. For example, coordinate position, heading, etc. Specifically, the motion state information may be obtained by the V2X terminal carried by the traffic participant object from the built-in positioning module. For example, the positioning module may be GPS, Beidou satellite navigation system, Galileo satellite navigation system, Russian global navigation satellite system, etc.

[0051] The road section information of M (M≥1, M is an integer) road sections to be matched can be obtained by the V2X terminal from the map information. The map information is the map information provided by the map application installed by the V2X terminal. The map information includes the road section information of multiple road sections within a preset range of the location of the traffic participant. The V2X terminal can use all of the multiple road sections as the road sections to be matched, or select some of the road sections from the multiple road sections as the road sections to be matched.

[0052] In order to improve the matching efficiency, this application provides a method for screening the road sections to be matched, such as Figure 2 As shown, the screening method may include:

[0053] S201, determining the path length and width radius of the longest road section among the multiple road sections according to the road section information of the multiple road sections.

[0054] In one example, the road segment information may include the width radius of the road segment, the position coordinates of the two end points, and the path length. For example, it may be the Mercator projection coordinates of the two end points of the road segment.

[0055] If the road section information does not include the path length, the path length of the road section may also be calculated based on the position coordinates of the two end points of the road section.

[0056] For example, the two endpoints of a road segment are represented as endpoint A and endpoint B, and the position coordinates of endpoint A are (x A ,y A ), the position coordinates of endpoint B are (x B ,y B ). Then the path length (distaMce) of this section can be expressed as:

[0057] After the path length of each road section is obtained, the longest path length (maxPathLeMgth) is determined, and the width radius of the longest road section is obtained from the road section information of the road section corresponding to the longest path length (ie, the longest road section).

[0058] S202: Determine an adaptive matching distance according to the path length and width radius of the longest road segment.

[0059] Exemplarily, the maximum path length (maxPathLeMgth) and the width radius (radius) may be substituted into the following formula to obtain the adaptive matching distance (maxSearchLeMgth):

[0060]

[0061] S203 , screening the road section information of M to-be-matched road sections from the road section information of the plurality of road sections according to the adaptive matching distance.

[0062] Exemplarily, according to the coordinate positions of endpoints A and B recorded in the segment information of each segment, a segment whose position coordinates of any endpoint of endpoint A and endpoint B meet the following conditions is selected as the segment to be matched:

[0063]

[0064] x max =x vehicle +maxSearchLength

[0065] x min =x vehicle -maxSearchLength

[0066] y max =y vehicle +maxSearchLength

[0067] y min =y vehicle -maxSearchLength

[0068] Among them, x vehicle is the Mercator projection abscissa of the traffic participant object, y vehicle The Mercator projection ordinate of the traffic participant object. In one example, if the location information of the traffic participant object in the motion state information obtained by the V2X terminal from the positioning module is expressed in a non-Mercator projection coordinate system, for example, in longitude and latitude, then when calculating the adaptive matching distance, the coordinate system conversion can be performed first to obtain the Mercator projection coordinate information of the traffic participant object.

[0069] Based on the above conditions, a square area centered on the traffic participant object can be determined as the search range of the road segment to be matched. max Indicates the maximum horizontal coordinate of the square area, x min Indicates the minimum horizontal coordinate of the square area, y max Indicates the maximum ordinate of the square area, y min The V2X terminal can select M road sections located in the square area as the road sections to be matched from the multiple road sections indicated by the map information, and determine the road section information of the M road sections to be matched.

[0070] S102, determining the position parameters of the traffic participant object relative to each of the to-be-matched road sections according to the motion state information and the road section information of the M to-be-matched road sections.

[0071] The position parameter is used to indicate the relative position relationship between the traffic participant object and the road section to be matched. The following uses the parameters of three dimensions, namely, distance, angle and diagonal, to exemplarily describe the relative position relationship between the traffic participant object and the road section to be matched, that is, the position parameter may include the distance parameter, the angle parameter and the diagonal parameter.

[0072] Take the road segment i to be matched as an example, see Figure 3 , is a schematic diagram of the relative positions of the road section i to be matched and the traffic participant. The road section i to be matched includes endpoints A and B. Point Q is the historical position of the traffic participant, point P is the current position of the traffic participant, and point P' is the projection point of point P on road section AB (i.e., road section i to be matched).

[0073] In this application, the distance from point P' to point P is the actual distance D from the traffic participant to the to-be-matched road segment i. In the triangle formed by point P' and road segment AB, the diagonal angle ∠APB of road segment AB is the actual diagonal angle of the traffic participant relative to the to-be-matched road segment i, and the actual angle of the traffic participant relative to the to-be-matched road segment i refers to the angle between the driving direction of the traffic participant and the to-be-matched road segment i.

[0074] Among them, the actual distance and the actual diagonal angle can reflect the degree to which the traffic participant is close to the road section to be matched. When the actual diagonal angles are equal, the smaller the actual distance, the closer the traffic participant is to the road section to be matched; when the actual distances are equal, the larger the actual angle, the closer the traffic participant is to the road section to be matched. The actual angle can reflect the similarity between the driving trajectory of the traffic participant and the road section to be matched. The smaller the actual angle, the higher the similarity between the driving trajectory of the traffic participant and the road section to be matched.

[0075] It is understandable that the smaller the actual distance, the smaller the actual angle, and the larger the actual diagonal angle, the greater the possibility that the to-be-matched section is the target section. When calculating the matching probability of a to-be-matched section, since the weighted sum is used for calculation, the parameters of each dimension need to have the same influence on the matching probability. For example, if the to-be-matched section with the highest matching probability is determined to be the target section, the parameters of each dimension of the to-be-matched section should satisfy the rule that the larger the better. If the to-be-matched section with the lowest matching probability is determined to be the target section, the parameters of each dimension of the to-be-matched section should satisfy the rule that the smaller the better.

[0076] Therefore, based on the matching probability selection rule of the target road section, the present application converts the actual distance, actual angle, and actual diagonal of the traffic participants relative to the road section to be matched into corresponding distance parameters, angle parameters, and diagonal parameters to calculate the matching probability.

[0077] The following rule is to select the to-be-matched road segment with the highest matching probability as the target road segment to calculate the parameters of each dimension (ie, to make the parameters of each dimension meet the rule that the larger the better).

[0078] For the distance parameter, the V2X terminal first calculates the actual distance from the traffic participant to each road section to be matched.

[0079] For example, taking the road section i to be matched as an example, the V2X terminal calculates the actual distance from the traffic participant to the road section i to be matched based on the road section information of the road section i to be matched and the motion state information of the traffic participant.

[0080] For example, firstly, based on the following formula (1), according to the coordinates of endpoint A, endpoint B and point P, the ratio t between the length from endpoint A to point P' and the length from endpoint A to point B (i.e., AB) is calculated. Then, the coordinates of point P' are calculated using formula (2). After obtaining the coordinates of point P', the distance D between points P and P' is calculated using formula (3), that is, the actual distance from the traffic participant to the road section i to be matched.

[0081]

[0082] P'=A+(BA)t (2)

[0083] D=|P-P'| (3)

[0084] After the V2X terminal calculates the actual distance from the traffic participant to each road section to be matched, it determines the minimum distance D from the M actual distances. min Then, according to the ratio of the actual distance from the traffic participant to each to-be-matched road section to the minimum distance value, the distance parameter of the traffic participant to each to-be-matched road section is determined.

[0085] Exemplarily, based on formula (4), the present application uses the log model to calculate the distance parameter m D .

[0086]

[0087] Here, G represents a preset communication distance limit. For example, in V2X, G can be 10.

[0088] Since the sizes of the M actual distances may vary greatly, for example, some sections to be matched are 10 meters away from the traffic participants, while some sections to be matched are 1000 meters away from the traffic participants, which is an order of magnitude difference. Therefore, using the log model, the distance parameter can be constrained between 0 and 1 to reduce the impact on the accuracy of the matching probability caused by the order of magnitude difference in the actual distances of the sections to be matched. In addition, the size of the actual diagonal will affect the size of the actual distance. The use of the log model can average the impact of the actual diagonal on the parameters of the distance dimension. At the same time, the smaller the actual distance D, the smaller the corresponding distance parameter m. D The larger the distance parameter, the better the rule. Therefore, m is used. D To calculate the matching probability, the reliability of the matching probability can be improved.

[0089] Regarding the angle parameter, the smaller the actual angle of the traffic participant relative to the to-be-matched road section, the higher the similarity between the driving trajectory of the traffic participant and the to-be-matched road section. Therefore, in order to meet the rule that the larger the angle parameter, the better, the supplementary angle of the actual angle is used to calculate the angle parameter in this application.

[0090] The V2X terminal first calculates the complementary angles of the angles between the driving directions of the traffic participants and each road section to be matched.

[0091] For example, taking the road section i to be matched as an example, the V2X terminal calculates the complementary angle of the angle between the driving direction of the traffic participant and the road section i to be matched based on the road section information of the road section i and the motion state information of the traffic participant.

[0092] For example, firstly based on the following formula (5), the coordinates of endpoint A, the coordinates of endpoint B and the preset reference section in the north direction are Determine the angle between section AB and true north Then based on the following formula (6), according to and the heading angle ∠Veh of the traffic participant (i.e. the angle between the driving direction of the traffic participant and the true north direction), determine the complementary angle H of the angle H' between the driving direction of the traffic participant and the road section i to be matched.

[0093]

[0094]

[0095] H=180-H'

[0096] Since the values ​​of the M supplementary angles vary greatly, they will have a great impact on the calculation results of the matching probability. Therefore, after the V2X terminal calculates the supplementary angles of the angles between the driving directions of the traffic participants and each road section to be matched, it can determine the maximum supplementary angle H from the M supplementary angles. max . Using H max Normalize the M supplementary angles, that is, based on the following formula (7), the supplementary angles of each to-be-matched road section are respectively compared with H max The ratio of the traffic participants to each section to be matched is determined by the angle parameter m. H .

[0097]

[0098] It can be understood that, through normalization, the angle parameter m H The limit is between 0 and 1, which reduces the influence of the large difference in the values ​​of the M supplementary angles on the calculation result of the matching probability. It can be understood that the smaller the angle H' is, the larger the supplementary angle H is, and the angle parameter m is H The larger the angle, the better the rule is met. Therefore, the angle parameter m is used. H To calculate the matching probability, the reliability of the matching probability can be improved.

[0099] For the diagonal information, the V2X terminal first calculates the actual diagonal of the traffic participant relative to each road section to be matched.

[0100] For example, taking the to-be-matched road section i as an example, the V2X terminal calculates the actual diagonal of the traffic participant relative to the to-be-matched road section i based on the road section information of the to-be-matched road section i and the motion state information of the traffic participant.

[0101] For example, the actual diagonal of the traffic participant relative to the to-be-matched road segment i can be calculated based on the coordinates of the endpoint A, the endpoint B, and the point P based on the following formulas (8) and (9).

[0102]

[0103]

[0104] Since the values ​​of the M diagonals vary greatly, the calculation result of the matching probability will be greatly affected when calculating the matching probability. Therefore, after the V2X terminal calculates the diagonals of the traffic participants relative to each road section to be matched, it can determine the maximum diagonal E from the M diagonals. max . Using E max Normalize the M diagonals, that is, based on the following formula (10), the diagonal E of each to-be-matched road segment is respectively equal to H max The ratio of the traffic participants to each road section to be matched is determined by E .

[0105]

[0106] It can be understood that by normalization, the diagonal parameter m E The limit is between 0 and 1, which reduces the impact of the large numerical differences of the M diagonals on the calculation results of the matching probability. It can be understood that the larger the diagonal E, the smaller the diagonal parameter m. E The larger the value, the better the rule is met. Therefore, the diagonal parameter m is used. E To calculate the matching probability, the reliability of the matching probability can be improved.

[0107] S103: Determine a position parameter weight according to the position parameters of the traffic participant object relative to the M to-be-matched road sections.

[0108] Corresponding to the above position parameters, the position parameter weights may specifically include distance parameter weights, angle parameter weights and diagonal parameter weights.

[0109] Exemplarily, in the calculation of the position parameters described in S102 above, since each parameter is limited to between 0 and 1, in one example, the standard deviation of the position parameters of the M sections to be matched in each dimension can be calculated to determine whether the distribution of the parameters of each dimension is consistent. If the distribution is consistent, it means that the parameters of each dimension have little effect on distinguishing the matching probability of each section to be matched. If the distribution is very scattered, it means that the parameters of some dimensions are more unique and have a greater effect on distinguishing the matching probability of each section to be matched. In this example, weights can be assigned to the parameters of each dimension based on the standard deviation of the position parameters of each dimension.

[0110] For example, the standard deviation of the distance parameter cov can be calculated based on the following formula (11) according to the position parameters of the traffic participant object relative to the M to-be-matched road sections: D , angle parameter standard deviation cov H and the diagonal parameter standard deviation cov E .

[0111]

[0112] in, is the mean of the distance parameters, is the mean of the diagonal parameters, is the mean value of the angle parameter.

[0113] After obtaining the standard deviation of the location parameters of each dimension, the V2X terminal can assign weights based on the standard deviation of the location parameters. It can be understood that the smaller the standard deviation of the location parameters of a dimension, the more similar the sections to be matched are in this dimension, and the smaller the role of this dimension in selecting the target section.

[0114] Therefore, if the standard deviations of the position parameters in each dimension are similar (i.e., the difference is less than the preset value) or the same, it means that the road segments to be matched are similar in each dimension, and the weights preset for each dimension can be used for allocation. For example, the parameter weights corresponding to the distance, angle, and diagonal dimensions are 0.5, 0.3, and 0.2, respectively.

[0115] If the size of the location parameter standard deviation of each dimension is different, for example, the distance parameter standard deviation is greater than the location parameter standard deviation of the other two dimensions, and the difference exceeds the preset value. This means that the sections to be matched are not similar in the distance dimension, which has a greater effect on the selection of the target section. Then, according to the size of the location parameter standard deviation of the three dimensions, the preset parameter weights are assigned accordingly. For example, the three preset weights are 0.5, 0.3, and 0.2. The dimension with the largest parameter standard deviation can be assigned a parameter weight of 0.5, the dimension with the smallest location parameter standard deviation can be assigned a parameter weight of 0.2, and the remaining dimension can be assigned a parameter weight of 0.3.

[0116] Optionally, the present application also provides a parameter weight calculation method, that is, after obtaining the distance parameter standard deviation cov D , angle parameter standard deviation cov H and the diagonal parameter standard deviation cov E After that, the distance parameter standard deviation cov D , angle parameter standard deviation cov H and the diagonal parameter standard deviation cov E Scale proportionally to the preset maximum constraint value cov max and the minimum constraint value cov min between.

[0117] For example, cov max =0.9, cov min =0.1, then the distance parameter standard deviation cov D The scaled value cov' D , the angle parameter standard deviation scaled value cov' H, and the standard deviation of the diagonal parameters cov E The scaled value cov' E , satisfying cov' D =αcov' E =βcov' H . Where α and β are scaling factors.

[0118] Afterwards, according to cov' D 、cov' E 、cov' H , calculate the distance parameter weight C1, the diagonal parameter weight C2 and the foot clamping parameter weight C3.

[0119] Exemplarily, it can be calculated based on the following formula (12):

[0120]

[0121] It can be understood that in the present application, the position parameter weights are adaptively determined according to the actual position parameters of the traffic participant object relative to the M road sections to be matched, so that the weights used are more consistent with the actual position relationship between the traffic participant object and the multiple road sections to be matched.

[0122] S104, determining a target road section from the M road sections to be matched according to the position parameter weights and the position parameters of the M road sections to be matched.

[0123] After obtaining the position parameter weights and the position parameters of each to-be-matched section, for each to-be-matched section, the V2X terminal can perform weighted summation of the position parameters of the traffic participants relative to the to-be-matched section according to the position parameter weights to obtain the matching probability of the to-be-matched section.

[0124] For example, the matching probability for the to-be-matched road segment i is

[0125] Afterwards, according to the matching probabilities of the M to-be-matched road sections and the preset matching conditions, the target road section is determined from the M to-be-matched road sections.

[0126] In the embodiment of the present application, if the target road segment is selected based on the matching rule that the greater the matching probability, the more likely it is to be the target road segment, the preset condition may include at least one of the following three conditions:

[0127] Condition 1: Maximum matching probability.

[0128] Condition 2: the to-be-matched road section with the second largest matching probability and the to-be-matched road section with the maximum matching probability are different road sections, and the ratio of the second largest matching probability to the maximum matching probability is less than a preset threshold.

[0129] Condition three: the to-be-matched road segment with the maximum matching probability meets the preset regional restriction condition.

[0130] Exemplarily, after obtaining the matching probability of each to-be-matched road section, the V2X terminal may directly select the to-be-matched road section with the highest matching probability as the target road section.

[0131] Optionally, in order to ensure the validity of the selected target section, the target section can be tested. For example, test whether the ratio of the second largest matching probability to the maximum matching probability is less than a preset threshold (e.g., 80%, 85%, etc.). If the ratio of the second largest matching probability to the maximum matching probability is less than the preset threshold, it means that the to-be-matched section with the maximum matching probability is obviously more likely to be the target section, and the to-be-matched section with the maximum matching probability can be selected as the target section. If the ratio of the second largest matching probability to the maximum matching probability is greater than the preset threshold, it means that the two to-be-matched sections corresponding to the second largest matching probability and the maximum matching probability are relatively similar, and the final target section cannot be determined, and the matching result can be ignored.

[0132] Alternatively, it is also possible to further detect whether the to-be-matched road section with the maximum matching probability meets the preset regional restriction conditions. For example, the domain restriction conditions include the minimum thresholds of the distance parameter, the diagonal parameter, and the angle parameter. When the various position parameters of the to-be-matched road section with the maximum matching probability meet the regional restriction conditions respectively, the to-be-matched road section with the maximum matching probability is determined as the target road section. This can avoid the situation where the matching probability of the to-be-matched road section is the highest due to the excessively large parameters of a certain dimension, but in fact the to-be-matched road section is not within the matching range of the traffic participant object, for example, the distance is very close, but the driving trajectory is completely different from the road section trajectory.

[0133] In summary, the map matching method provided in the embodiment of the present application can adaptively determine the position parameter weights according to the actual position parameters of the traffic participant relative to the M to-be-matched road sections, so that the used weights are more consistent with the actual position relationship between the traffic participant and the multiple to-be-matched road sections. Thus, the used weights are universal for a variety of different maps, thereby improving the accuracy of map matching.

[0134] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0135] Corresponding to a map matching method described in the above embodiment, Figure 5 FIG. 1 shows a structural diagram of an embodiment of a map matching device provided in an embodiment of the present application. Figure 5 , the map matching device may include:

[0136] The acquisition unit 401 is used to acquire the motion state information of the traffic participant and the section information of the M sections to be matched.

[0137] The calculation unit 402 is used to determine the position parameters of the traffic participant object relative to each of the to-be-matched sections according to the motion state information and the section information of the M to-be-matched sections, wherein the position parameters are used to indicate the relative position relationship between the to-be-matched sections and the traffic participant object.

[0138] The calculation unit 402 is further configured to determine a position parameter weight according to the position parameter of the traffic participant object relative to the M road sections to be matched.

[0139] The matching unit 403 is used to determine a target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched.

[0140] Optionally, the acquiring unit 401 acquires the road section information of the M road sections to be matched, specifically including:

[0141] Acquire map information, the map information including section information of a plurality of road sections; determine the path length and width radius of the longest section among the plurality of road sections based on the section information of the plurality of road sections; determine an adaptive matching distance based on the path length and width radius of the longest section; and filter the section information of M sections to be matched from the section information of the plurality of road sections based on the adaptive matching distance.

[0142] Optionally, the location parameter includes a distance parameter, and the calculation unit 402 determines the distance parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information, including:

[0143] For each of the to-be-matched road sections, determining the actual distance from the traffic participant to the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information;

[0144] Substituting the actual distance and the minimum distance into the formula , calculating and obtaining the distance parameter of the traffic participant object relative to the road section to be matched;

[0145] Among them, m D represents the distance parameter of the traffic participant relative to the to-be-matched road section, G represents the preset communication distance limit, D represents the actual distance, and D min represents the minimum distance, and the minimum distance is the minimum value of the actual distances from the traffic participant to the M road sections to be matched.

[0146] Optionally, the position parameter includes an angle parameter, and the calculation unit 402 determines the angle parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information, including:

[0147] For each of the to-be-matched road sections, determining the supplementary angle of the angle between the driving direction of the traffic participant and the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information;

[0148] The angle parameter of the traffic participant object relative to the to-be-matched road section is determined according to the ratio of the supplementary angle to the maximum supplementary angle, and the maximum supplementary angle is the maximum value of the supplementary angles between the driving direction and the M to-be-matched road sections.

[0149] Optionally, the position parameter includes a diagonal parameter, and the calculation unit 402 determines the diagonal parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information, including:

[0150] For each of the to-be-matched road sections, determining the actual diagonal angle of the traffic participant object relative to the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information;

[0151] The diagonal parameter of the traffic participant object relative to the to-be-matched road section is determined according to the ratio of the actual diagonal and the maximum diagonal, wherein the maximum diagonal is the maximum value of the actual diagonals of the traffic participant object relative to the M to-be-matched road sections.

[0152] Optionally, the calculating unit 402 determines the position parameter weight according to the position parameter of the traffic participant object relative to the M road sections to be matched, including:

[0153] Calculating a position parameter standard deviation according to the position parameters of the traffic participant object relative to the M road sections to be matched;

[0154] Scale the location parameter standard deviation proportionally to between the preset maximum and minimum constraint values;

[0155] The location parameter weight is calculated according to the scaled location parameter standard deviation.

[0156] Optionally, the matching unit 403 determines the target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched, including:

[0157] For each of the to-be-matched road sections, weighted summing is performed on the position parameters of the traffic participant objects relative to the to-be-matched road section according to the position parameter weights to obtain a matching probability of the to-be-matched road section;

[0158] Determine a target road section from the M road sections to be matched according to the matching probabilities of the M road sections to be matched and a preset matching condition;

[0159] The preset condition includes at least one of the following three conditions:

[0160] Condition 1: Maximum matching probability;

[0161] Condition 2: the to-be-matched road section with the second largest matching probability and the to-be-matched road section with the largest matching probability are different road sections, and the ratio of the second largest matching probability to the largest matching probability is less than a preset threshold;

[0162] Condition three: the to-be-matched road segment with the maximum matching probability meets the preset regional restriction condition.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] Figure 5 A schematic block diagram of a V2X terminal provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0166] like Figure 5 As shown, the V2X terminal 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned map matching method embodiments are implemented, for example Figure 1 Alternatively, when the processor 50 executes the computer program 52, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 4 The functions of modules 401 to 403 are shown.

[0167] Exemplarily, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 52 in the V2X terminal 5.

[0168] Those skilled in the art will understand that Figure 5 It is only an example of the V2X terminal 5 and does not constitute a limitation of the V2X terminal 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the V2X terminal 5 may also include input and output devices, network access devices, buses, etc.

[0169] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0170] The memory 51 may be an internal storage unit of the V2X terminal 5, such as a hard disk or memory of the V2X terminal 5. The memory 51 may also be an external storage device of the V2X terminal 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the V2X terminal 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the V2X terminal 5. The memory 51 is used to store the computer program and other programs and data required by the V2X terminal 5. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0171] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0172] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0173] An embodiment of the present application provides a computer program product. When the computer program product runs on a V2X terminal, the V2X terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0174] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0176] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0179] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0180] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A map matching method, characterized in that: The method comprises: Obtaining the motion state information of the traffic participant and the section information of M sections to be matched, where M is an integer greater than or equal to 1; Determine, according to the motion state information and the section information of the M sections to be matched, the position parameters of the traffic participant object in multiple dimensions relative to each of the sections to be matched, wherein the position parameters are used to indicate the relative position relationship between the sections to be matched and the traffic participant object; Calculate the standard deviation of the position parameters of each dimension according to the position parameters of multiple dimensions of the traffic participant object relative to the M road sections to be matched; Allocate position parameter weights of position parameters of each dimension according to the position parameter standard deviations of each dimension; wherein the smaller the position parameter standard deviation, the smaller the allocated position parameter weight; According to the position parameter weight and the position parameters of the M to-be-matched road sections, a target road section is determined from the M to-be-matched road sections.

2. The method according to claim 1, characterized in that Get the segment information of M segments to be matched, including: Acquiring map information, wherein the map information includes road section information of a plurality of road sections; Determine the path length and width radius of the longest road section among the multiple road sections according to the road section information of the multiple road sections; Determining an adaptive matching distance according to the path length and width radius of the longest road section; According to the adaptive matching distance, the road section information of the M road sections to be matched is screened from the road section information of the plurality of road sections.

3. The method according to claim 1 or 2, characterized in that: The position parameter includes a distance parameter, and determining the distance parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes: For each of the to-be-matched road sections, determining the actual distance from the traffic participant to the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information; Substituting the actual distance and the minimum distance into the formula , calculating and obtaining the distance parameter of the traffic participant object relative to the road section to be matched; in, represents the distance parameter of the traffic participant object relative to the to-be-matched road section, G represents the preset communication distance limit, D represents the actual distance, represents the minimum distance, and the minimum distance is the minimum value of the actual distances from the traffic participant to the M road sections to be matched.

4. The method according to any one of claims 1 to 3, characterized in that: The position parameter includes an angle parameter, and determining the angle parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes: For each of the to-be-matched road sections, determining the supplementary angle of the angle between the driving direction of the traffic participant and the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information; The angle parameter of the traffic participant object relative to the to-be-matched road section is determined according to the ratio of the supplementary angle to the maximum supplementary angle, and the maximum supplementary angle is the maximum value of the supplementary angles between the driving direction and the M to-be-matched road sections.

5. The method according to any one of claims 1 to 4, characterized in that: The position parameter includes a diagonal parameter, and determining the diagonal parameter of the traffic participant object relative to each of the to-be-matched road sections according to the road section information of the M to-be-matched road sections and the motion state information includes: For each of the to-be-matched road sections, determining the actual diagonal angle of the traffic participant object relative to the to-be-matched road section according to the road section information of the to-be-matched road section and the motion state information; The diagonal parameter of the traffic participant object relative to the to-be-matched road section is determined according to the ratio of the actual diagonal and the maximum diagonal, wherein the maximum diagonal is the maximum value of the actual diagonals of the traffic participant object relative to the M to-be-matched road sections.

6. The method according to any one of claims 1 to 5, characterized in that: The position parameter weights of the position parameters of each dimension are allocated according to the position parameter standard deviations of each dimension, including: Scale the location parameter standard deviation proportionally to between the preset maximum and minimum constraint values; The location parameter weight is calculated according to the scaled location parameter standard deviation.

7. The method according to any one of claims 1 to 6, characterized in that: Determining a target road section from the M road sections to be matched according to the position parameter weight and the position parameters of the M road sections to be matched includes: For each of the to-be-matched road sections, weighted summing is performed on the position parameters of the traffic participant objects relative to the to-be-matched road section according to the position parameter weights, so as to obtain a matching probability of the to-be-matched road section; Determine a target road section from the M road sections to be matched according to the matching probabilities of the M road sections to be matched and a preset matching condition; The preset matching condition includes at least one of the following three conditions: Condition 1: Maximum matching probability; Condition 2: the to-be-matched road section with the second largest matching probability and the to-be-matched road section with the largest matching probability are different road sections, and the ratio of the second largest matching probability to the largest matching probability is less than a preset threshold; Condition three: the to-be-matched road segment with the maximum matching probability meets the preset regional restriction condition.

8. A map matching device, characterized in that: include: An acquisition unit, used to acquire the motion state information of the traffic participant and the section information of the M sections to be matched; A calculation unit, used to determine, based on the motion state information and the section information of the M sections to be matched, a position parameter of the traffic participant object relative to each of the sections to be matched, wherein the position parameter is used to indicate a relative position relationship between the sections to be matched and the traffic participant object; The calculation unit is further used to determine the position parameter weight according to the position parameter of the traffic participant object relative to the M road sections to be matched; A matching unit is used to determine a target section from the M sections to be matched according to the position parameter weight and the position parameters of the M sections to be matched.

9. A V2X terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable 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 7 are implemented.

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