Vehicle positioning method and device, storage medium and electronic equipment

Abstract

The invention discloses a vehicle positioning method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining first vehicle-mounted data of a target vehicle at a target moment; under the condition that the first vehicle-mounted data are invalid, second vehicle-mounted data of the target vehicle at a first moment are obtained, the first moment is a moment before the target moment, and the second vehicle-mounted data are latest valid vehicle-mounted data of the target vehicle before the target moment; and determining the position of the target vehicle at the target moment according to the second vehicle-mounted data. Through the vehicle positioning method and device, the problem of inaccurate vehicle positioning in related technologies is solved, and the effect of improving the vehicle positioning accuracy is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the computer field, in particular, to a positioning method and device of a vehicle, a storage medium and an electronic device. BACKGROUND

[0002] In the field of intelligent traffic management, task execution by a vehicle fleet is an effective way to improve traffic efficiency, reduce costs and reduce environmental impact. During the execution of the task by the vehicle, accurate positioning and tracking of the vehicle fleet are crucial to ensure the efficiency and safety of the task execution.

[0003] In related technologies, the position tracking of vehicles in a vehicle fleet mainly relies on the data provided by the vehicle-mounted GPS (Global Positioning System) device. However, GPS positioning has limitations, especially in urban environments, where obstacles such as high-rise buildings, bridges and trees can block signals, resulting in loss of positioning data or reduced accuracy. In addition, when the vehicle passes through complex road conditions, such as intersections, overpasses or tunnels, the positioning error of GPS can be significantly increased, thereby affecting the accuracy and continuity of the vehicle trajectory.

[0004] To address the above problems, there is currently no effective solution. SUMMARY

[0005] Embodiments of the present application provide a positioning method and device of a vehicle, a storage medium and an electronic device to at least solve the technical problem of inaccurate vehicle positioning in related technologies.

[0006] According to an aspect of an embodiment of the present application, a positioning method of a vehicle is provided, comprising: obtaining first vehicle-mounted data of a target vehicle at a target time; in the case that the first vehicle-mounted data is invalid, obtaining second vehicle-mounted data of the target vehicle at a first time, wherein the first time is a time before the target time, and the second vehicle-mounted data is the latest valid vehicle-mounted data of the target vehicle before the target time; and determining the position of the target vehicle at the target time according to the second vehicle-mounted data.

[0007] In one exemplary embodiment, the above method further comprises: in the case that the first position parameter is not present in the first vehicle-mounted data, determining that the first vehicle-mounted data is invalid; or in the case that the first position parameter is present in the first vehicle-mounted data, determining a first distance between the second position parameter in the second vehicle-mounted data and the first position parameter; and in the case that the first distance is greater than or equal to a preset distance threshold, determining that the first vehicle-mounted data is invalid.

[0008] In an example embodiment, determining the position of the target vehicle at the target time according to the second vehicle data comprises: in the case that the first position parameter is not present in the first vehicle data, determining a plurality of target parameter values according to the first speed in the second vehicle data; determining a target speed of the target vehicle at the target time according to the plurality of target parameter values; and determining the position of the target vehicle at the target time according to the second position parameter and the target speed in the second vehicle data.

[0009] In an example embodiment, determining the plurality of target parameter values according to the first speed in the second vehicle data comprises: determining a target parameter fitting curve according to the speed, the headway, the headway time, and the expected speed of the target vehicle in the historical time; and determining the plurality of target parameter values corresponding to the first speed in the target parameter fitting curve, wherein the target parameter values comprise the headway, the headway time, and the expected speed corresponding to the first speed.

[0010] In an example embodiment, determining the target speed of the target vehicle at the target time according to the plurality of target parameter values comprises: updating a car following model by using the plurality of target parameter values to obtain a target car following model; inputting the first speed into the target car following model to obtain an acceleration of the target vehicle; and determining the target speed according to the acceleration of the target vehicle and the first speed.

[0011] In an example embodiment, determining the target speed according to the acceleration of the target vehicle and the first speed comprises: determining a first time difference between the target time and the first time; determining a speed deviation by multiplying the acceleration of the target vehicle and the first time difference; and determining the target speed by summing the speed deviation and the first speed.

[0012] In an example embodiment, determining the position of the target vehicle at the target time according to the second vehicle data comprises: in the case that the first position parameter is present in the first vehicle data, smoothing the first distance to obtain a first smoothed parameter; determining a target speed of the target vehicle at the target time according to the first smoothed parameter; and determining the position of the target vehicle at the target time according to the second position parameter and the target speed in the second vehicle data.

[0013] In an example embodiment, determining the target speed of the target vehicle at the target time according to the first smoothed parameter comprises: updating the first speed in the second vehicle data by using the first smoothed parameter to obtain an updated speed; and optimizing the updated speed to obtain the target speed.

[0014] In an example embodiment, the first speed is updated by the first smoothing parameter to obtain an updated speed, including: determining a first product of the first smoothing parameter and the first coefficient as the first product; determining a first difference of the first product and the first value as the first difference; determining a first time difference between the target time and the first time as the first time difference; determining a second product of the first difference, the control gain, and the first time difference as the second product; and determining a sum of the second speed in the first vehicle data and the second product as the updated speed.

[0015] In an example embodiment, the updated speed is optimized to obtain a target speed, including: determining a minimum value between the updated speed and a preset maximum speed as a third speed; and determining a maximum value between the third speed and a preset minimum speed as the target speed.

[0016] According to another aspect of the embodiments of the present application, there is also provided a positioning device of a vehicle, including: a first obtaining module configured to obtain first vehicle data of a target vehicle at a target time; a second obtaining module configured to obtain second vehicle data of the target vehicle at a first time in a case where the first vehicle data is invalid, the first time being a time before the target time, and the second vehicle data being the latest valid vehicle data of the target vehicle before the target time; and a determining module configured to determine a position of the target vehicle at the target time according to the second vehicle data.

[0017] According to still another aspect of the embodiments of the present application, there is also provided a computer readable storage medium having stored therein a computer program, wherein the computer program is configured to perform the steps of any one of the method embodiments described above when executed by a processor.

[0018] According to still another aspect of the embodiments of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps of any one of the method embodiments described above.

[0019] According to still another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, the memory having stored therein a computer program, and the processor being configured to perform the steps of any one of the method embodiments described above by means of the computer program.

[0020] By the present application, when positioning the target vehicle, first, the first vehicle-mounted data of the target vehicle at the target time is acquired, and in the case that the first vehicle-mounted data is invalid, the valid vehicle-mounted data of the target vehicle at the first time before the target time is acquired to obtain the second vehicle-mounted data, and then the target vehicle is positioned according to the second vehicle-mounted data to determine the position of the target vehicle at the target time.

[0021] Since before positioning the vehicle, the acquired vehicle-mounted data is first judged, and in the case that the vehicle-mounted data is determined to be invalid, the latest valid vehicle-mounted data of the target vehicle is acquired, and then the target vehicle is predicted on the basis of the latest valid vehicle-mounted data to realize the positioning of the target vehicle, so that in the case that the vehicle-mounted data is lost or abnormal, the continuous tracking and positioning of the vehicle position can also be maintained, the vehicle positioning delay and precision decline caused by the abnormal vehicle-mounted data are overcome, the positioning error caused by the abnormal data is reduced, the high consistency of the positioning result and the actual vehicle position is ensured, and the stability and accuracy of the vehicle positioning are significantly improved. Therefore, the problem of inaccurate vehicle positioning in the related art can be solved, and the effect of improving the accuracy of vehicle positioning is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is an application scenario of a vehicle positioning method according to an embodiment of the present application;

[0023] Figure 2 is a flowchart of a vehicle positioning method according to an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of a lane line in a high-precision map according to an embodiment of the present application;

[0025] Figure 4 is a schematic diagram of a road grid according to an embodiment of the present application;

[0026] Figure 5 is a schematic diagram of vehicle positioning according to an embodiment of the present application;

[0027] Figure 6 is a schematic diagram of a deceleration scenario according to an embodiment of the present application;

[0028] Figure 7 is a schematic diagram of an acceleration scenario according to an embodiment of the present application;

[0029] Figure 8 is a schematic diagram of an intersection range according to an embodiment of the present application;

[0030] Figure 9 is a schematic diagram of candidate lane filtering according to an embodiment of the present application;

[0031] Figure 10is a flowchart of a process of restoring a vehicle trajectory based on vehicle data according to an embodiment of the present application;

[0032] Figure 11 is a structural block diagram of a positioning device of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to enable persons skilled in the art to better understand the scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should belong to the scope of protection of the present application.

[0034] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] According to an aspect of an embodiment of the present application, a positioning method of a vehicle is provided. Optionally, in the present embodiment, the above-mentioned positioning method of a vehicle can be applied in, but is not limited to, a hardware environment as shown in Figure 1 The server 104 can be connected with the terminal device 102 through a network, can be used to provide services (for example, application services, etc.) for the terminal device 102 or a client installed on the terminal device 102, and a database can be set on the server 104 or independently of the server 104, used to provide data storage services for the server 104.

[0036] The network can include, but is not limited to, at least one of the following: a wired network, a wireless network. The wired network can include, but is not limited to, at least one of the following: a wide area network, a metropolitan area network, a local area network. The wireless network can include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI), Bluetooth. The terminal device 102 can be, but is not limited to, a personal computer (PC), a mobile phone, a tablet computer, etc. The server 104 can be, but is not limited to, a cloud server, a server cluster, or other server types.

[0037] The positioning method of the vehicle in the embodiments of the present application can be executed by the server 104, or by the terminal device 102, or by both the server 104 and the terminal device 102. The terminal device 102 can execute the positioning method of the vehicle in the embodiments of the present application by a client installed thereon.

[0038] Taking the terminal device 102 as an example, Figure 2 The flowchart of the positioning method of the vehicle in the embodiments of the present application is shown in FIG. 2, which can include the following steps: Figure 2

[0039] In step S202, first vehicle-mounted data of a target vehicle at a target time is acquired.

[0040] The target vehicle can be a vehicle that needs to be positioned, which can be any vehicle in any vehicle group. Optionally, the target vehicle is usually equipped with a GPS positioning device to provide real-time position data of the vehicle.

[0041] The target time can be a time point at which the data of the target vehicle is processed and the target vehicle is positioned, which can be any time point during the driving of the target vehicle.

[0042] The first vehicle-mounted data can be a series of driving data of the vehicle at the target time, such as a device number of the vehicle, a position parameter of the vehicle, a heading angle of the vehicle, a speed of the vehicle, etc. The driving data can be collected and reported by a vehicle-mounted device (such as a vehicle-mounted GPS) on the target vehicle. Through the first vehicle-mounted data, the system can perform real-time positioning and trajectory restoration of the vehicle.

[0043] In the embodiments of the present application, during the formation of a vehicle group, the vehicle-mounted devices on each vehicle in the vehicle group will report the vehicle-mounted data of the corresponding vehicle to the system at regular time points. At this time, the system receives the vehicle-mounted data sent by the vehicle-mounted devices on the vehicles in the vehicle group to acquire the first vehicle-mounted data of the target vehicle at the target time.

[0044] ​In the step S204, the second vehicle-mounted data of the target vehicle at the first time point is obtained in the case that the first vehicle-mounted data is invalid, wherein the first time point is a time point before the target time point, and the second vehicle-mounted data is the latest valid vehicle-mounted data of the target vehicle before the target time point.

[0045] The first time point can be a time point before the target time point, for example, a time point one second before the target time point. For example, in the case that the vehicle-mounted device reports the vehicle-mounted data every one second, the first time point can be a time point one second before the target time point.

[0046] The second vehicle-mounted data can be valid driving data of the target vehicle at the first time point, such as a device number of the vehicle, a position parameter of the vehicle, a heading angle of the vehicle, a speed of the vehicle, etc. The driving data can be obtained from the vehicle-mounted data reported by the vehicle-mounted device of the target vehicle at the first time point, or can be generated by the system based on the related data of the target vehicle at the first time point, etc. The specific obtaining manner can be selected according to actual conditions.

[0047] In the embodiment of the present application, after obtaining the first vehicle-mounted data, the system judges the validity of the first vehicle-mounted data. In the case that the first vehicle-mounted data is invalid, the valid vehicle-mounted data (i.e., the second vehicle-mounted data) of the target vehicle at the previous time point (i.e., the first time point) is obtained.

[0048] In the step S206, the position of the target vehicle at the target time point is determined according to the second vehicle-mounted data.

[0049] Through the above steps, when the target vehicle is positioned, the first vehicle-mounted data of the target vehicle at the target time point is obtained first, and in the case that the first vehicle-mounted data is invalid, the valid vehicle-mounted data of the target vehicle at the first time point before the target time point is obtained, so as to obtain the second vehicle-mounted data. Then, the target vehicle is positioned according to the second vehicle-mounted data, so as to determine the position of the target vehicle at the target time point.

[0050] Since the obtained vehicle-mounted data is judged before the vehicle is positioned, and in the case that the vehicle-mounted data is invalid, the latest valid vehicle-mounted data of the target vehicle is obtained, and then the target vehicle is predicted based on the latest valid vehicle-mounted data, so as to position the target vehicle. In the case that the vehicle-mounted data is lost or abnormal, the continuous tracking and positioning of the vehicle position can also be maintained. The vehicle positioning delay and the precision decline caused by the abnormal vehicle-mounted data are overcome. The positioning error caused by the abnormal data is reduced. The consistency between the positioning result and the actual vehicle position is ensured. The stability and accuracy of the vehicle positioning are significantly improved. The problem of inaccurate vehicle positioning in the related art is solved. The accuracy of the vehicle positioning is improved.

[0051] As an optional implementation, the method further comprises: determining that the first vehicle data is invalid if the first position parameter is not present in the first vehicle data; or determining a first distance between the second position parameter in the second vehicle data and the first position parameter if the first position parameter is present in the first vehicle data; and determining that the first vehicle data is invalid if the first distance is greater than or equal to a preset distance threshold.

[0052] The first position parameter can be a parameter for describing a position of the target vehicle at a target time, which can be a latitude and longitude, address information, or a coordinate point in a geographic coordinate system, etc. Through the first position parameter, the system can determine the accurate position of the target vehicle at the target time, so as to realize the management and service support of the target vehicle. In the embodiments of the present application, the first position parameter can be data reported by the vehicle-mounted device.

[0053] The second position parameter can be a parameter for describing a position of the target vehicle at a first time, which can be a latitude and longitude, address information, or a coordinate point in a geographic coordinate system, etc. Through the second position parameter, the system can determine the accurate position of the target vehicle at the first time, so as to realize the management and service support of the target vehicle. In the embodiments of the present application, the second position parameter can be data reported by the vehicle-mounted device, or data generated by the system based on related data of the target vehicle, etc.

[0054] The first distance can be a distance between two positions represented by the first position parameter and the second position parameter. The distance can be an Euclidean distance, a Manhattan distance, or a distance calculated by a Haversine formula, which can be selected according to actual conditions.

[0055] The preset distance threshold can be a fixed distance value for comparison, which is used to determine whether the distance between two position parameters is within an acceptable range, so as to ensure the smoothness of vehicle driving, realize the abnormal detection of the position parameter, and ensure that the system can automatically filter out data that does not meet the conditions when analyzing and processing vehicle data, so as to improve the reliability and accuracy of the overall system.

[0056] In the embodiment of the present application, after the system obtains the first vehicle-mounted data, the validity of the first vehicle-mounted data needs to be judged. At this time, firstly, it is judged whether the first position parameter of the target vehicle at the target time is included in the first vehicle-mounted data reported by the vehicle-mounted device. If the first position parameter does not exist in the first vehicle-mounted data, it is considered that the first vehicle-mounted data (or the first position parameter) is incomplete and missing, and the system determines that the first vehicle-mounted data is invalid. Or, in the case that the first position parameter exists in the first vehicle-mounted data, it is further judged whether the first position parameter is available, the first distance between the position of the target vehicle at the target time (i.e. the first position parameter) and the position of the target vehicle at the first time (i.e. the second position parameter) is determined, it is judged whether the first distance exceeds the preset distance threshold, and when the first distance is greater than or equal to the preset distance threshold, it is considered that the first position parameter is not available, and the system determines that the first vehicle-mounted data is invalid. For example, if the first distance between the position of the target vehicle at the target time and the position of the target vehicle at the first time (i.e. 1 second before the target time) is 50m, which exceeds the normal driving distance of the target vehicle within 1 second (i.e. the preset distance threshold), it is considered that the position parameter of the target vehicle at the target time (i.e. the first position parameter) is not available, and the system determines that the first vehicle-mounted data is invalid.

[0057] Through the above steps, through strict screening and validity verification of the vehicle-mounted data, the system can eliminate data points that do not meet the standard, thereby providing more stable and reliable trajectory information for vehicle positioning, reducing error data caused by positioning errors or other factors, avoiding trajectory matching errors caused by incomplete data or abnormal data, and ensuring the accuracy of vehicle trajectory prediction and positioning.

[0058] Optionally, in the embodiment of the present application, during the driving of the vehicle, the vehicle-mounted device often causes abnormal loss or large accuracy deviation of the collected vehicle-mounted data due to shielding or other external factors. At this time, if the system directly uses the vehicle-mounted data reported by the vehicle-mounted device for vehicle positioning and trajectory restoration, the trajectory of the vehicle may often be lost or drift. Therefore, before receiving or obtaining the first vehicle-mounted data, the system will preliminarily screen the first vehicle-mounted data reported by the vehicle-mounted device, and when it is determined that the first vehicle-mounted data reported by the vehicle-mounted device is abnormal data, all data (except the device number) in the first vehicle-mounted data reported by the vehicle-mounted device will be deleted (after deletion, all data (except the device number) in the first vehicle-mounted data in the system does not exist), otherwise, the first vehicle-mounted data reported by the vehicle-mounted device is retained. The specific judgment method can include at least one of the following contents:

[0059] (1) If the first vehicle-mounted data reported by the vehicle-mounted device is empty data, it is determined that the first vehicle-mounted data is abnormal;

[0060] (2) If the first position parameter in the first vehicle-mounted data shows that the vehicle is located in a grid and there is no lane line in the surrounding grid, it is determined that the first vehicle-mounted data is abnormal;

[0061] In the embodiment of the present application, since the number of road elements in the entire traffic network system is large, if matching is performed by traversing each road in the traffic network system, a large amount of time is required to locate the position of the vehicle. Therefore, before locating the vehicle, the system prepares relevant data, pre-processes the map data, converts the high-precision map into a road grid, and then performs matching and positioning of the vehicle based on the road grid, thereby reducing the amount of calculation when positioning the vehicle and significantly improving the efficiency of vehicle positioning.

[0062] In an example, the pre-processing of the map data and the conversion of the high-precision map into a road grid include marking lane lines in the road in the high-precision map, as shown in Figure 3 , wherein Figure 3 The black thin solid line in indicates the road in the map, the black thick solid line indicates the lane line in the road, the origin indicates the positioning of the vehicle, and the black dashed line indicates the trajectory restoration of the trajectory of the vehicle. Then, the high-precision map marked with the lane line is divided into a road grid, the entire map is divided into M*N grids according to a fixed degree, and the marked lane line is marked in the grid to determine the label of the grid where each lane line is located, as shown in Figure 4 , wherein Figure 4 Each grid in indicates that a grid is divided, each grid is assigned a grid label, the black circle indicates the vehicle, and the black thick solid line indicates the lane line in the map. Based on the grid label, it can be determined that the grid to which the lane line belongs is [(2, 2), (2, 3), (3, 3)], and then in the subsequent real-time road matching, only the grid to which the vehicle belongs is selected according to the position of the vehicle, and the lane line existing in the grid is found, so that the road matching can be quickly performed to realize the positioning of the vehicle.

[0063] In the embodiment of the present application, when the system receives the vehicle-mounted data reported by the vehicle-mounted device, the grid label (m, n) of the grid where the vehicle is located is determined according to the position parameter of the vehicle included in the vehicle-mounted data. The grid label (m, n) can be determined by the following formula:

[0064]

[0065] In the formula, m is the number of columns where the grid is located, x is the longitude of the vehicle shown in the vehicle-mounted data (i.e., the position parameter of the vehicle), xmin is the longitude of the position represented by the starting point of the grid (generally, the lower-left corner of the grid), gridx is the fixed length when the map is divided into M columns, n is the number of rows where the grid is located, y is the latitude of the vehicle shown in the vehicle-mounted data (i.e., the position parameter of the vehicle), umin is the latitude of the position represented by the starting point of the grid (generally, the lower-left corner of the grid), and gridy is the fixed length when the map is divided into N rows.

[0066] Subsequently, the surrounding grids of the grid where the vehicle is located are located according to the grid, and the surrounding grids include the grids above, below, left, right, upper left, upper right, lower left, and lower right of the grid. For example, when the grid where the vehicle is located is (2, 2), the surrounding grids are [(1, 1), (2, 1), (3, 1), (1, 2), (3, 2), (1, 3), (2, 3), (3, 3)]. If there is no lane line in the grid where the vehicle is located and the surrounding grids, it is determined that the vehicle-mounted data currently reported by the vehicle-mounted device is abnormal.

[0067] (3) If the shortest distance between the candidate lane in the grid where the vehicle is located and the vehicle is greater than the first threshold value, as shown in the first position parameter in the first vehicle-mounted data, it is determined that the first vehicle-mounted data is abnormal.

[0068] In the embodiments of the present application, after the system determines that there is a lane line in the grid where the vehicle is located or the surrounding grid, the lane lines in the grid where the vehicle is located and the surrounding grid are taken as candidate lanes to obtain a candidate lane set R = {r1, r2, r3, …, r n}. At this time, the system calculates the distance d between the vehicle and each candidate lane in the candidate set, as shown in detail in Figure 4 , and if the distance d between the vehicle and each candidate lane in the candidate set is greater than the first threshold value, it is determined that the vehicle-mounted data currently reported by the vehicle-mounted device is abnormal.

[0069] (4) If the heading angle of the vehicle in the first vehicle-mounted data is greater than the preset heading angle threshold value, it is determined that the first vehicle-mounted data is abnormal.

[0070] (5) If the fourth speed of the vehicle in the first vehicle-mounted data is abnormal, it is determined that the vehicle-mounted data is abnormal.

[0071] In the embodiments of the present application, because the speed of the vehicle is generally in a relatively smooth change process during the driving process of the vehicle, and will not appear instantaneous large mutation, therefore, the change between the speed of the vehicle at the current moment and the speed of the vehicle at the previous moment needs to be within a certain range.

[0072] For example, if the speed between the two time instants is greater than 1.5 times the average of the speeds of the two time instants, or if the speed between the two time instants is less than 0.5 times the minimum speed of the two time instants, i.e., the fourth speed of the target vehicle at the target time instant and the first speed of the target vehicle at the first time instant collected by the vehicle-mounted device satisfy any of the following formulas, it is determined that the first vehicle-mounted data is abnormal:

[0073]

[0074] where v4 is the fourth speed of the target vehicle at the target time instant collected by the vehicle-mounted device, v1 is the first speed, t is the target time instant, t1 is the first time instant, is the average of the first speed and the fourth speed, and vmin = min{v4, v1} is the minimum speed of the first speed and the fourth speed.

[0075] In the embodiments of the present application, after judging the validity of the obtained first vehicle-mounted data, the target vehicle can be positioned by the following several ways.

[0076] Embodiment one:

[0077] As an optional implementation, determining the position of the target vehicle at the target time instant according to the second vehicle-mounted data includes: in the case that the first position parameter does not exist in the first vehicle-mounted data, determining a plurality of target parameter values according to the first speed in the second vehicle-mounted data; determining a target speed of the target vehicle at the target time instant according to the plurality of target parameter values; and determining the position of the target vehicle at the target time instant according to the second position parameter in the second vehicle-mounted data and the target speed.

[0078] The above target parameter values can be the values of related parameters in the model. By determining the values of the parameters in the model, the model can be more personalized and scene-adaptive, which not only improves the accuracy and reliability of the model, but also adapts to various complex driving environments and vehicle characteristics, providing strong technical support for the processing of vehicle-mounted data and the monitoring of vehicle state.

[0079] The above target speed can be an updated value of the speed of the vehicle predicted by the system based on the valid data of the vehicle in the trajectory correction process, aiming to ensure the smoothness of the vehicle driving and reduce the speed mutation, so as to improve the reliability of the vehicle positioning.

[0080] In the embodiment of the present application, in the case that the first position parameter does not exist in the first vehicle data, the system determines the effective speed (i.e., the first speed) of the target vehicle at the first time according to the second vehicle data, and then determines a plurality of target parameter values of the model according to the first speed, determines the target speed of the target vehicle at the target time based on the plurality of target parameter values, and thus predicts the position of the target vehicle at the target time according to the target speed and the effective position parameter (i.e., the second position parameter) of the target vehicle at the first time, so as to realize the positioning of the target vehicle.

[0081] Through the above operation, the incomplete position information caused by the missing vehicle data is effectively made up, and it is ensured that the high-precision positioning of the target vehicle can be realized even in complex environment or sudden situation. Meanwhile, the correlation and continuity between data are also considered, and the accuracy and reliability of the positioning result are further improved by comprehensively analyzing the speed and position change trend of the vehicle at the historical time, so that the vehicle positioning and trajectory restoration are more coherent and credible.

[0082] As an optional implementation, determining the plurality of target parameter values according to the first speed in the second vehicle data comprises: determining a target parameter fitting curve according to the speed, the headway, the headway time interval and the expected speed of the target vehicle at the historical time; and determining the plurality of target parameter values corresponding to the first speed in the target parameter fitting curve, wherein the target parameter values include the headway, the headway time interval and the expected speed corresponding to the first speed.

[0083] The target parameter fitting curve described above can be a curve for determining the target parameter values of the model, and the fitting curve can be constructed by statistical analysis of the historical data of the vehicle (including the speed, the headway, the headway time interval and the expected speed of the target vehicle), aiming to capture and express the dynamic characteristics between the vehicle and the parameter values (including the values of the headway, the headway time interval and the expected speed) of the model under different driving conditions and environments, wherein different vehicles correspond to different parameter fitting curves. Through the target parameter fitting curve, the system can determine the parameter values of the model corresponding to the vehicle under different conditions (such as different speeds) based on the actual driving situation of the vehicle, so that the system can more personalized and accurately predict the behavior of the vehicle through the model. The specific form of the target parameter fitting curve can be as follows:

[0084] F(s0, T, vmax) = f(vt)

[0085] In the formula, s0 is the headway, which represents the distance between the front of the current vehicle in the convoy and the rear of the vehicle in front; T is the headway, which represents the time interval between two consecutive vehicles in the convoy reaching the same location (such as a road sign or checkpoint); vmax is the expected speed, which represents the expected speed of the vehicle under the current road conditions; and vt is the vehicle speed, which represents the average speed of the vehicle under the current road conditions.

[0086] In the application embodiment, relevant data of the target vehicle within a historical time range is used to capture the dynamic relationship between the target vehicle's speed and headway, headway spacing, and desired speed. The least squares method is used to fit and construct polynomial fitting equations for headway, headway spacing, and desired speed under different road conditions to obtain the target parameter fitting curve of the target vehicle. Then, based on the target parameter fitting curve, the target parameter value (i.e., the value of headway, headway, and desired speed) corresponding to the first speed of the target vehicle at the first moment is determined.

[0087] Through the above operations, the relationship between vehicle speed and model parameters under different road conditions can be accurately captured, thereby dynamically adjusting the parameter values ​​of the model. This allows the model to predict vehicle speed in different road environments, improving the accuracy of vehicle speed prediction and enhancing the precision and reliability of vehicle positioning.

[0088] As an optional implementation, determining the target speed of the target vehicle at the target time based on multiple target parameter values ​​includes: updating the car-following model using multiple target parameter values ​​to obtain a target car-following model; inputting a first speed into the target car-following model to obtain the acceleration of the target vehicle; and determining the target speed based on the acceleration of the target vehicle and the first speed.

[0089] The aforementioned target car-following model can be a dynamic model used to predict the speed change of a target vehicle in a car-following state. It is used to predict the expected speed of the target vehicle at a target time based on the vehicle's state at the previous moment, in scenarios involving missing or abnormal onboard data. The specific form of the target car-following model is shown below:

[0090]

[0091] In the formula, is an acceleration of the target vehicle, a is a maximum acceleration acceptable to the target vehicle in a current driving process, v1 is a first speed of the target vehicle at a first time, vmax is the expected speed determined based on the target parameter fitting curve, Δsn is a distance between the target vehicle and a neighboring vehicle in the vehicle platoon, the distance between the target vehicle and a front vehicle if vehicle data of the front vehicle is valid, or the distance between the target vehicle and a rear vehicle if vehicle data of the rear vehicle is valid, s*(v1, Δvn) is a braking term for controlling a safety distance between vehicles and ensuring that a distance between two neighboring vehicles meets a requirement for safe driving, and the braking term can be obtained by the following formula:

[0092]

[0093] wherein s*(v1, Δvn) is the braking term, s0 is a headway distance determined based on the target parameter fitting curve, v1 is the first speed of the target vehicle at the first time, T is a headway time determined based on the target parameter fitting curve, Δvn is a speed difference between the target vehicle and the neighboring vehicle in the vehicle platoon, the speed difference between the target vehicle and the front vehicle if vehicle data of the front vehicle is valid, or the speed difference between the target vehicle and the rear vehicle if vehicle data of the rear vehicle is valid, a is the maximum acceleration acceptable to the target vehicle in the current driving process, and b is a maximum deceleration acceptable to the target vehicle in the current driving process.

[0094] In the embodiments of the present application, the car following model is updated by the plurality of target parameter values to obtain a target car following model suitable for the target vehicle in the current scenario, and the first speed is input into the target car following model to calculate the acceleration of the target vehicle, and then the acceleration of the target vehicle and the first speed of the target vehicle at the first time are combined to determine the target speed of the target vehicle at the target time by using the dynamics principle.

[0095] An example of the vehicle positioning according to the embodiments of the present application is shown in FIG. 1. Figure 5 is a schematic diagram of vehicle positioning according to the embodiments of the present application, as shown in Figure 5As shown, for a vehicle platoon, when part of the trajectory of the vehicle platoon is lost (i.e., the on-board data of the vehicle acquired by the system is invalid), the system can perform forward operation and inverse operation based on the car-following model respectively, so as to calculate the predicted trajectory of the front vehicle or the rear vehicle, and realize positioning of the vehicle with lost trajectory in the vehicle platoon. For example, when the on-board data of the front vehicle is valid but the on-board data of the rear vehicle is abnormal (i.e., invalid), the acceleration of the rear vehicle can be calculated based on the on-board data of the front vehicle by using the car-following model, and then the positioning (or trajectory prediction) of the rear vehicle is realized. Or, when the on-board data of the rear vehicle is valid but the on-board data of the front vehicle is abnormal (i.e., invalid), the acceleration of the front vehicle can be calculated based on the on-board data of the rear vehicle by using the car-following model, and then the positioning (or trajectory prediction) of the front vehicle is realized. Generally, the rear vehicle is preferentially positioned based on the on-board data of the front vehicle.

[0096] Through the above operation, through real-time updating of the car-following model and accurate calculation of the acceleration, dynamic prediction of the vehicle speed is realized, the safety and fluency of the vehicle in the complex traffic environment are ensured, the system can quickly respond according to the state and road condition at the previous moment, and the accuracy and reliability of the vehicle positioning are improved.

[0097] As an optional implementation, the target speed is determined according to the acceleration of the target vehicle and the first speed, including: determining a first time difference between the target moment and the first moment; determining a product of the acceleration of the target vehicle and the first time difference as a speed deviation; and determining a sum of the speed deviation and the first speed as the target speed.

[0098] In the embodiments of the present application, after the acceleration of the target vehicle is determined, a time difference (i.e., the first time difference) between the target moment and the first moment is determined, and based on this time, the acceleration of the target vehicle is multiplied by the calculated time difference to obtain a speed deviation, and then the speed deviation is added to the first speed to determine the target speed of the target vehicle at the target moment, so that the position of the target vehicle at the target moment is accurately calculated through the target speed, the first time difference between the first moment and the target moment, and the second position parameter of the target vehicle at the first moment, and the positioning of the target vehicle is realized.

[0099] Through the above operation, the accuracy and stability of the vehicle speed prediction can be effectively improved, especially when the vehicle platoon is driving, even in the case of signal shielding or data anomaly, the continuity and real-time performance of the vehicle trajectory can be ensured, and then the accuracy and adaptability of the vehicle positioning are improved.

[0100] Embodiment two:

[0101] As an optional implementation, the position of the target vehicle at the target moment is determined according to the second vehicle-mounted data, comprising: in the case that the first position parameter exists in the first vehicle-mounted data, smoothing the first distance to obtain a first smoothed parameter; determining the target speed of the target vehicle at the target moment according to the first smoothed parameter; and determining the position of the target vehicle at the target moment according to the second position parameter in the second vehicle-mounted data and the target speed.

[0102] In the embodiments of the present application, the smoothness of vehicle driving is considered, and the speed of vehicle driving cannot have a large mutation from the previous state. If the first position parameter in the obtained first vehicle-mounted data shows that the first distance between the position of the target vehicle at the target moment and the position of the target vehicle at the first moment exceeds the normal driving distance of the vehicle within the time range (between the first moment and the target moment), such as showing that the target vehicle drives 50 m in 1 s, it is indicated that the speed of the target vehicle at the target moment has a large mutation from the previous moment, and the speed of the target vehicle at the target moment needs to be updated at this time to limit the speed of the target vehicle. First, the first distance is smoothed to obtain a first smoothed parameter, and then the target speed of the target vehicle at the target moment is determined according to the parameter, and the position of the target vehicle at the target moment is accurately determined in combination with the second position parameter in the second vehicle-mounted data and the target speed.

[0103] Through the above operation, the driving characteristics of the vehicle in different states are fully considered, and the dynamic correction and prediction of the vehicle driving track are realized through the fusion of real-time data and high-precision map data. Especially in the case of inaccurate positioning, the continuity and accuracy of the vehicle track can also be ensured through the correction algorithm, thereby providing reliable technical support for efficient and accurate positioning of the vehicle.

[0104] As an optional implementation, the target speed of the target vehicle at the target moment is determined according to the first smoothed parameter, comprising: updating the first speed in the second vehicle-mounted data through the first smoothed parameter to obtain an updated speed; and optimizing the updated speed to obtain the target speed.

[0105] The updated speed can be calculated according to the current state of the vehicle (such as the distance between the previous moment, the speed of the previous moment, etc.) in the track correction process, and is used to update the speed value of the vehicle driving speed.

[0106] In the embodiments of the present application, after predicting the speed at the current moment at the target moment based on the first speed of the target vehicle at the first moment, in order to ensure that the predicted updated speed is more in line with the actual situation and safety regulations, and to ensure that the vehicle is in a safe state and avoid unreasonable prediction results, such as deceleration scenarios (such as Figure 6As shown in FIG. 6, because of model errors, external disturbances or data abnormalities, an unreasonable speed increase is wrongly predicted, resulting in a sudden increase of the update speed to an unsafe level, or in an acceleration scenario (such as a traffic light turning green), the update speed is suddenly reduced to a level that is too low to ensure safety. Figure 7 As shown in FIG. 6, because of model errors, external disturbances or data abnormalities, an unreasonable speed increase is wrongly predicted, resulting in a sudden increase of the update speed to an unsafe level, or in an acceleration scenario (such as a traffic light turning green), the update speed is suddenly reduced to a level that is too low to ensure safety.

[0107] As an optional implementation, the first speed is updated by the first smoothing parameter to obtain an update speed, including: determining a product of the first smoothing parameter and the first coefficient as a first product; determining a difference between the first product and the first value as a first difference; determining a time difference between the target time and the first time as a first time difference; determining a product of the first difference, the control gain, and the first time difference as a second product; and determining a sum of the second speed in the first vehicle data and the second product as the update speed.

[0108] In the embodiments of the present application, the update speed can be determined by the following formula:

[0109] v' = v1 + (sigmod(serror) * 2 - 1) * kp * (t - t1)

[0110] In the formula, v' is the update speed, v1 is the first speed of the target vehicle at the first time, sigmod(serror) is the first smoothing parameter, serror is the first distance, kp is the control gain, t is the target time, and t1 is the first time.

[0111] As an optional implementation, the update speed is optimized to obtain a target speed, including: determining a minimum value between the update speed and a preset maximum speed as a third speed; and determining a maximum value between the third speed and a preset minimum speed as the target speed.

[0112] In the embodiments of the present application, the target speed can be determined by the following formula:

[0113] v = max(0, min(v', 120))

[0114] In the formula, v is the target speed, v' is the update speed, 0 is the preset minimum speed, and 120 is the preset maximum speed.

[0115] Through the above operation, the continuity and rationality of the speed update are ensured by adjusting the speed information through the smoothing parameter, the vehicle speed estimation error caused by abnormal fluctuations of the vehicle-mounted data is avoided, the optimized target speed more accurately reflects the actual driving state of the vehicle, especially in the acceleration and deceleration scenarios, the trajectory prediction error caused by the sudden change of the speed is effectively reduced, and the accuracy and stability of the trajectory prediction and correction are enhanced.

[0116] Embodiment three:

[0117] In the embodiments of the present application, in the case where the system determines that the first vehicle-mounted data is valid, the first grid in which the target vehicle is located is positioned according to the first position parameter in the first vehicle-mounted data, and the surrounding grids of the first grid are determined to obtain a plurality of second grids; then the system determines a candidate lane set of the target vehicle in the first grid and the plurality of second grids, and performs preliminary screening on the candidate lane set. For example, if the target vehicle is within the range of an intersection (such as within the dashed box shown in Figure 8 , the lane in which the target vehicle is currently located and the downstream lane of the lane are retained in the candidate lane set to obtain a first candidate lane set; if the target vehicle is within the range of a non-intersection, all lanes in the candidate lane set are retained to obtain a second candidate lane set.

[0118] After the preliminary screening of the candidate lanes, further judgment and screening are performed on the first candidate lane set or the second candidate lane set obtained through the preliminary screening based on the first heading angle of the target vehicle in the first vehicle-mounted data to obtain a final target candidate lane set. Figure 9 is a schematic diagram of candidate lane filtering according to the embodiments of the present application, as shown in Figure 9 , the candidate lane in the first candidate lane or the second candidate lane that meets the following conditions can be determined as the target candidate lane to obtain the target candidate lane set:

[0119] | θc- θr| < 60°

[0120] In the formula, θc is the first heading angle of the target vehicle at the target time, and θr is the angle of the fitting line of the candidate lane.

[0121] Further, after the screening of the candidate lane set to obtain the target candidate lane set, the distance between the target vehicle and the fitting line of each target candidate lane is calculated according to the shortest distance algorithm, and the target candidate lane corresponding to the minimum distance is selected as the lane matching line of the target vehicle. At the same time, a perpendicular line is drawn from the position of the target vehicle to the lane matching line, and the intersection point of the perpendicular line and the lane matching line is the position of the target vehicle.

[0122] It should be noted that if the target vehicle is in the range of the intersection, and the target vehicle is in the parking state, the first position parameter of the target vehicle may be affected by the positioning accuracy and other factors, so that the first position parameter shows that the target vehicle will move back and forth in the parking position. At this time, the position data of the target vehicle calculated by the above method cannot be directly used, and the vehicle needs to be parked to make the vehicle stable and wait for the red light. Or, if the target vehicle is in the non-intersection range, but the first position parameter of the target vehicle shows that the target vehicle has a trajectory rollback. At this time, the position data of the target vehicle calculated by the above method cannot be directly used, and the position data of the target vehicle calculated by the above method needs to be discarded, and the first vehicle-mounted data is determined to be invalid. At the same time, the first vehicle-mounted data is invalid. The way is predicted to realize the positioning of the target vehicle.

[0123] As an optional implementation, Figure 10 is a flowchart for restoring the vehicle trajectory based on the vehicle-mounted data according to the embodiments of the present application, as Figure 10 shown, the specific process is as follows:

[0124] Step S1001, the system receives the vehicle-mounted data (i.e. the first vehicle-mounted data) reported by the target vehicle at the target time;

[0125] Step S1002, the system judges the validity of the first vehicle-mounted data, which specifically includes:

[0126] Firstly, the system preliminarily judges the first vehicle-mounted data based on the road grid converted from the high-precision map, determines whether the first vehicle-mounted data is abnormal, if the first vehicle-mounted data is abnormal, deletes all data in the first vehicle-mounted data except the device number, at this time, only the device number of the target vehicle is retained in the first vehicle-mounted data in the system, otherwise, all data in the first vehicle-mounted data is retained;

[0127] Then, the system further judges the first vehicle-mounted data to determine whether the first vehicle-mounted data is valid. If there is no first position parameter in the first vehicle-mounted data, or if there is a first position parameter in the first vehicle-mounted data, but the first position parameter shows that the first distance between the target vehicle at the previous time (i.e. the first time) and the position (determined by the second position parameter) is greater than or equal to the preset distance threshold, it is determined that the first vehicle-mounted data is invalid, otherwise, it is determined that the first vehicle-mounted data is valid;

[0128] Step S1003, if the first vehicle-mounted data is valid, the system directly determines the candidate lane in the road grid based on the first vehicle-mounted data and performs lane matching, and then determines the position of the target vehicle at the target time based on the lane matching line obtained by matching;

[0129] Step S1004, if the first vehicle-mounted data is invalid, the system predicts the speed of the target vehicle at the target time based on the latest valid data (i.e., the second vehicle-mounted data) of the target vehicle at the previous time through the car following model or the correction algorithm, and then determines the position of the target vehicle at the target time based on the predicted speed and the position of the target vehicle at the previous time (determined through the second position parameter);

[0130] Step S1005, after the system determines the position of the target vehicle at the target time, in order to avoid the situation that the system continues to predict the trajectory (i.e., the position) of the target vehicle due to the invalidity of the first vehicle-mounted data, the system determines whether the target vehicle has completed the task, i.e., whether the position of the target vehicle is within the preset spatial range, or the distance between the position shown by the position parameter in the vehicle-mounted data reported by the target vehicle and the predicted position of the target vehicle exceeds the preset deviation threshold.

[0131] Step S1006, if it is determined that the target vehicle has not completed the task, the system continues to receive the vehicle-mounted data reported by the target vehicle and performs subsequent operations until it is determined that the target vehicle has completed the task.

[0132] Step S1007, if it is determined that the target vehicle has completed the task, the system directly ends the trajectory restoration (i.e., the positioning) of the target vehicle.

[0133] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0134] Those skilled in the art can clearly understand that the method according to the above-mentioned embodiments can be realized by means of software and necessary general hardware platforms, and of course, can also be realized by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a Read-Only Memory (ROM) / Random Access Memory (RAM), a magnetic disk, or an optical disc) and includes a number of instructions for causing an end device (which can be a mobile phone, a computer, a server, or a network device) to perform the methods described in the various embodiments of the present application.

[0135] According to another aspect of the embodiments of the present application, a positioning device of a vehicle is also provided, which can be used to implement the positioning method of the vehicle provided in the above-mentioned embodiments, which has been described above and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and is contemplated.

[0136] Figure 11 is a structural block diagram of the positioning device of the vehicle according to the embodiments of the present application, as shown in Figure 11 The device includes: a first acquisition module 1102, configured to acquire first vehicle-mounted data of a target vehicle at a target time; a second acquisition module 1104, configured to acquire second vehicle-mounted data of the target vehicle at a first time in a case where the first vehicle-mounted data is invalid, wherein the first time is a time before the target time, and the second vehicle-mounted data is the latest valid vehicle-mounted data of the target vehicle before the target time; and a determination module 1106, configured to determine a position of the target vehicle at the target time according to the second vehicle-mounted data.

[0137] In one exemplary embodiment, the device is further configured to: in a case where the first position parameter is absent from the first vehicle-mounted data, determine that the first vehicle-mounted data is invalid; or in a case where the first position parameter is present in the first vehicle-mounted data, determine a first distance between the second position parameter in the second vehicle-mounted data and the first position parameter; and in a case where the first distance is greater than or equal to a preset distance threshold, determine that the first vehicle-mounted data is invalid.

[0138] In an example embodiment, the apparatus is further configured to determine a plurality of target parameter values corresponding to the first speed according to the second vehicle data in a case that the first position parameter is absent in the first vehicle data; determine a target speed of the target vehicle at the target time according to the plurality of target parameter values; and determine the position of the target vehicle at the target time according to the second position parameter and the target speed in the second vehicle data.

[0139] In an example embodiment, the apparatus is further configured to determine a target parameter fitting curve according to the speed, the headway, the headway time, and the desired speed of the target vehicle at the historical time; and determine the plurality of target parameter values corresponding to the first speed in the target parameter fitting curve, wherein the target parameter values include the headway, the headway time, and the desired speed corresponding to the first speed.

[0140] In an example embodiment, the apparatus is further configured to update the car following model by the plurality of target parameter values to obtain a target car following model; input the first speed into the target car following model to obtain an acceleration of the target vehicle; and determine the target speed according to the acceleration of the target vehicle and the first speed.

[0141] In an example embodiment, the apparatus is further configured to determine a first time difference between the target time and the first time; determine a speed deviation as a product of the acceleration of the target vehicle and the first time difference; and determine the target speed as a sum of the speed deviation and the first speed.

[0142] In an example embodiment, the apparatus is further configured to smooth the first distance to obtain a first smoothed parameter in a case that the first position parameter is present in the first vehicle data; determine the target speed of the target vehicle at the target time according to the first smoothed parameter; and determine the position of the target vehicle at the target time according to the second position parameter and the target speed in the second vehicle data.

[0143] In an example embodiment, the apparatus is further configured to update the first speed in the second vehicle data by the first smoothed parameter to obtain an updated speed; and optimize the updated speed to obtain the target speed.

[0144] In an example embodiment, the apparatus is further configured to determine a first product as a product of the first smoothed parameter and a first coefficient; determine a first difference value as a difference between the first product and a first value; determine a first time difference as a time difference between the target time and the first time; determine a second product as a product of the first difference value, a control gain, and the first time difference; and determine the updated speed as a sum of the second speed in the first vehicle data and the second product.

[0145] In one example embodiment, the apparatus is further configured to determine a minimum value between the update speed and a preset maximum speed as a third speed; and determine a maximum value between the third speed and a preset minimum speed as the target speed.

[0146] It should be noted that the above modules can be implemented by software or hardware, and for the latter, the implementation is not limited to the following: all the modules are located in the same processor; or the modules are located in different processors in any combination.

[0147] According to another aspect of the embodiments of the present application, a computer readable storage medium is provided, which includes a stored program, wherein the program performs the steps in any of the above method embodiments when executed.

[0148] In one example embodiment, the computer readable storage medium can include, but is not limited to, a U disk, a ROM, a RAM, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0149] According to another aspect of the embodiments of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor is configured to execute the steps in any of the above method embodiments through the computer program. In one example embodiment, the electronic device can further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0150] The specific examples in the embodiments can refer to the examples described in the above embodiments and exemplary implementation manners, which will not be described herein again.

[0151] According to another aspect of the embodiments of the present application, a computer program product is also provided, which includes computer programs / instructions containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed. The above serial numbers of the embodiments of the present application only represent the description, and do not represent the advantages or disadvantages of the embodiments.

[0152] It should be apparent to those skilled in the art that the modules or steps of the application described above can be implemented with general computing devices, which can be centralized on a single computing device or distributed on a network of multiple computing devices, and which can be implemented with program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in an order different from that shown here, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0153] The preferred embodiments of the present application are only used to illustrate the present application, but not to limit the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for locating a vehicle, characterized in that, include: Acquire the first onboard data of the target vehicle at the target time; If the first vehicle data is invalid, the second vehicle data of the target vehicle at a first moment is obtained, wherein the first moment is a moment before the target moment, and the second vehicle data is the latest valid vehicle data of the target vehicle before the target moment; The position of the target vehicle at the target time is determined based on the second vehicle data.

2. The method according to claim 1, characterized in that, The method further includes: If the first position parameter is not present in the first vehicle data, the first vehicle data is determined to be invalid; or... If the first position parameter exists in the first vehicle data, determine the first distance between the second position parameter in the second vehicle data and the first position parameter; if the first distance is greater than or equal to a preset distance threshold, determine that the first vehicle data is invalid.

3. The method according to claim 2, characterized in that, Determining the position of the target vehicle at the target time based on the second vehicle-mounted data includes: If the first position parameter is not present in the first vehicle data, multiple target parameter values ​​are determined based on the first speed in the second vehicle data. The target speed of the target vehicle at the target time is determined based on multiple target parameter values; The position of the target vehicle at the target time is determined based on the second position parameter in the second vehicle data and the target speed.

4. The method according to claim 3, characterized in that, Based on the first speed in the second vehicle data, multiple target parameter values ​​are determined, including: The target parameter fitting curve is determined based on the target vehicle's speed, headway, headway, and desired speed over a historical period. In the target parameter fitting curve, a plurality of target parameter values ​​corresponding to the first speed are determined, wherein the target parameter values ​​include the vehicle headway, vehicle headway, and desired speed corresponding to the first speed.

5. The method according to claim 3, characterized in that, Determining the target speed of the target vehicle at the target time based on multiple target parameter values ​​includes: The target car-following model is obtained by updating the car-following model using multiple target parameter values. The first speed is input into the target car-following model to obtain the acceleration of the target vehicle; The target speed is determined based on the acceleration of the target vehicle and the first speed.

6. The method according to claim 5, characterized in that, Determining the target speed based on the target vehicle's acceleration and the first speed includes: Determine the first time difference between the target time and the first time. The product of the target vehicle's acceleration and the first time difference is determined as the speed deviation; The sum of the speed deviation and the first speed is determined as the target speed.

7. The method according to claim 2, characterized in that, Determining the position of the target vehicle at the target time based on the second vehicle-mounted data includes: If the first position parameter exists in the first vehicle data, the first distance is smoothed to obtain the first smoothing parameter. The target speed of the target vehicle at the target time is determined based on the first smoothing parameter; The position of the target vehicle at the target time is determined based on the second position parameter in the second vehicle data and the target speed.

8. The method according to claim 7, characterized in that, Determining the target speed of the target vehicle at the target time based on the first smoothing parameter includes: The first speed in the second vehicle data is updated using the first smoothing parameter to obtain the updated speed; The update speed is optimized to obtain the target speed.

9. The method according to claim 8, characterized in that, The first speed is updated using the first smoothing parameter to obtain the updated speed, including: The product of the first smoothing parameter and the first coefficient is determined as the first product; The difference between the first product and the first value is defined as the first difference; The time difference between the target time and the first time is defined as the first time difference; The product of the first difference, the control gain, and the first time difference is determined as the second product; The sum of the second speed in the first vehicle data and the second product is determined as the update speed.

10. The method according to claim 8, characterized in that, Optimizing the update speed to obtain the target speed includes: The minimum value between the update speed and the preset maximum speed is determined as the third speed; The maximum value between the third speed and the preset minimum speed is determined as the target speed.

11. A vehicle positioning device, characterized in that, include: The first acquisition module is used to acquire the first on-board data of the target vehicle at the target time. The second acquisition module is used to acquire the second vehicle data of the target vehicle at a first moment when the first vehicle data is invalid, wherein the first moment is a moment before the target moment, and the second vehicle data is the latest valid vehicle data of the target vehicle before the target moment. The determination module is used to determine the position of the target vehicle at the target time based on the second vehicle data.

12. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 10.

14. An electronic device 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, it implements the steps of the method according to any one of claims 1 to 10.

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