Vehicle trajectory determination method and device, computer device, and storage medium

Abstract

The application relates to a vehicle driving track determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring longitude data, latitude data and vehicle speed corresponding to each collection time within a preset time length of a target vehicle, composing an initial data group by the longitude data, latitude data, vehicle speed and collection time corresponding to each collection time, correcting all initial data groups to obtain remaining initial data groups after correction, performing ascending sequence sorting on the collection time corresponding to each remaining initial data group to obtain an initial data set, determining a low-speed data set, determining a to-be-deleted data set and a target key set based on the low-speed data set, deleting the to-be-deleted data set from the initial data set, performing ascending sequence sorting on the collection time corresponding to each initial data group in the remaining initial data set to obtain a track data set, and acquiring a vehicle driving track by using the track data set and the target key set. The method can improve the vehicle driving track determination efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining vehicle driving trajectory. Background Technology

[0002] Vehicle trajectory tracking is crucial for enterprise vehicle monitoring. For example, in the event of an accident, administrators can access vehicle operation data, location, trajectory playback, and current location information up to the previous time, aiding in accident analysis. Furthermore, determining vehicle trajectories is extremely valuable for traffic system planning and management, as well as for early warning and tracking by public security systems.

[0003] Determining vehicle trajectories using traditional techniques requires combining road network data, traffic element information, and regional POI information, which is quite complex and therefore inefficient. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for determining vehicle trajectory that can improve the efficiency of determining vehicle trajectory, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for determining a vehicle's driving trajectory. The method includes:

[0006] The system acquires the longitude, latitude, and speed of the target vehicle at each collection moment within a preset time period. An initial data set is formed by the longitude, latitude, speed, and collection moment at each collection moment. All initial data sets are corrected to obtain the corrected remaining initial data set.

[0007] Sort the remaining initial data sets in ascending order according to the acquisition time corresponding to each set of initial data to obtain the initial dataset;

[0008] In the initial dataset, identify the low-speed dataset, and based on the low-speed dataset, determine the dataset to be deleted and the target key set;

[0009] The dataset to be deleted is removed from the initial dataset, and the remaining initial dataset is sorted in ascending order according to the acquisition time corresponding to each initial data group to obtain the trajectory dataset;

[0010] Using the trajectory dataset and the target key set, the vehicle trajectory of the target vehicle within the preset time period is obtained.

[0011] In one embodiment, determining the low-speed dataset in the initial dataset includes:

[0012] Obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next collection time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0013] In one embodiment, the dataset to be deleted includes a first dataset to be deleted and a second dataset to be deleted, and the target key set includes a first target key set and a second target key set; the step of determining the dataset to be deleted and the target key set based on the low-speed dataset includes:

[0014] The second to the last initial data group in the low-speed dataset are designated as the first dataset to be deleted.

[0015] The first initial data group in the low-speed dataset is used as the candidate key data group. The longitude and latitude data corresponding to each candidate key data group are used as candidate key points. The candidate key point combination vector is determined based on the candidate key points corresponding to two adjacent low-speed datasets.

[0016] Calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, take the candidate key point corresponding to the angle as the first target key point, and combine the candidate key data groups corresponding to the first target key point into the first target key set.

[0017] Based on the candidate key data set and the first target key set, a second target key set and a second dataset to be deleted are determined.

[0018] In one embodiment, determining the second target key set and the second dataset to be deleted based on the candidate key data set and the first target key set includes:

[0019] The candidate key data groups are combined to form a candidate dataset. The first target key set is deleted from the candidate dataset to obtain the remaining dataset.

[0020] Using the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points. If the distance is not greater than the preset distance threshold, then the remaining key points are used as the second target key points, and the candidate key data groups corresponding to the second target key points are combined into the second target key set.

[0021] If the distance is greater than a preset distance threshold, then the candidate key data group corresponding to the remaining key points is taken as the second data group to be deleted, and the second dataset to be deleted is determined based on the second data group to be deleted.

[0022] In one embodiment, obtaining the vehicle trajectory of the target vehicle within the preset time period using the trajectory dataset and the target key set includes:

[0023] The trajectory dataset is divided according to the target key set to obtain the divided trajectory dataset;

[0024] The Douglas-Puk algorithm is used to simplify the segmented trajectory dataset to obtain the vehicle's driving trajectory within the preset time period.

[0025] In one embodiment, the step of correcting all initial data sets to obtain corrected remaining initial data sets includes:

[0026] Based on the longitude, latitude, or acquisition time in each initial data set, identify the drift data sets among all initial data sets, and delete the drift data sets from all initial data sets to obtain the remaining initial data sets.

[0027] Secondly, this application also provides a vehicle trajectory determination device. The device includes:

[0028] The initial data set acquisition module is used to acquire the longitude data, latitude data, and vehicle speed of the target vehicle at each collection time within a preset time period. The initial data set is composed of the longitude data, latitude data, vehicle speed, and collection time corresponding to each collection time. All initial data sets are corrected to obtain the corrected remaining initial data set.

[0029] The initial dataset acquisition module is used to sort the remaining initial data groups in ascending order according to the collection time corresponding to each group, and obtain the initial dataset.

[0030] A low-speed dataset determination module is used to determine a low-speed dataset in the initial dataset, and to determine a dataset to be deleted and a target key set based on the low-speed dataset;

[0031] The trajectory dataset determination module is used to delete the dataset to be deleted from the initial dataset and sort it in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset.

[0032] The trajectory acquisition module is used to acquire the vehicle driving trajectory of the target vehicle within the preset time period using the trajectory dataset and the target key set.

[0033] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the methods in any of the above embodiments.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0035] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0036] The aforementioned vehicle trajectory determination method, device, computer equipment, and storage medium acquire longitude, latitude, and vehicle speed data corresponding to each acquisition moment of the target vehicle within a preset time period. An initial data set is formed from the longitude, latitude, vehicle speed, and acquisition moment data corresponding to each acquisition moment. All initial data sets are corrected to obtain the remaining corrected initial data sets. These remaining initial data sets are then sorted in ascending order according to the acquisition moment corresponding to each acquisition moment to obtain an initial dataset. A low-speed dataset is identified within the initial dataset. Based on the low-speed dataset, a dataset to be deleted and a target key set are determined. The dataset to be deleted is deleted from the initial dataset. The remaining initial dataset is then sorted in ascending order according to the acquisition moment corresponding to each initial data set to obtain a trajectory dataset. Using the trajectory dataset and the target key set, the vehicle trajectory of the target vehicle within the preset time period is obtained. Compared to the low efficiency of traditional technologies for determining vehicle trajectories, this application determines an initial dataset based on the collection time within a preset time period, as well as the longitude, latitude, and speed of the target vehicle. From this dataset, a low-speed dataset is identified, and further, a dataset to be deleted and a target key set are determined. Based on the initial dataset and the dataset to be deleted, a trajectory dataset is determined, and based on the trajectory dataset and the target key set, the vehicle's trajectory is determined, thus reducing data and improving the efficiency of determining the vehicle's trajectory. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the vehicle trajectory determination method provided in the embodiments of this application;

[0038] Figure 2 This is a flowchart illustrating the process of determining the dataset to be deleted and the target key set based on a low-speed dataset in one embodiment.

[0039] Figure 3 This is a flowchart illustrating the process of determining the second target key set and the second dataset to be deleted in one embodiment;

[0040] Figure 4 This is a flowchart illustrating the process of obtaining the vehicle trajectory of a target vehicle within a preset time period in one embodiment.

[0041] Figure 5 This is a structural block diagram of a vehicle trajectory determination device provided in the embodiments of this application;

[0042] Figure 6 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In this embodiment, a method for determining a vehicle's driving trajectory is provided. This embodiment uses the application of this method to a computer device as an example for illustration. It can be understood that this method can also be applied to a server, and can also be applied to a system including a computer device and a server, and can be implemented through the interaction between the computer device and the server.

[0045] Figure 1 This is a flowchart illustrating the vehicle trajectory determination method provided in this application embodiment. The method is applied in a computer device. In one embodiment, such as... Figure 1 As shown, it includes the following steps:

[0046] S101: Obtain the longitude, latitude, and vehicle speed of the target vehicle at each collection time within a preset time period. The longitude, latitude, vehicle speed, and collection time at each collection time form an initial data set. Correct all initial data sets to obtain the corrected remaining initial data set.

[0047] The preset duration can be set manually. The interval between every two data collection moments can also be set manually. Longitude and latitude data are used to reflect the location of the target vehicle. An initial data set is composed of the longitude data, latitude data, vehicle speed, and collection moment corresponding to each collection moment. For example, an initial data set is (ti, loni, lati, vi), where ti is the collection moment, loni is the longitude data, lati is the latitude data, and vi is the vehicle speed. Within the preset duration, each collection moment corresponds to one initial data set.

[0048] S102, sort the remaining initial data sets in ascending order according to the acquisition time corresponding to each set of initial data to obtain the initial dataset.

[0049] Sort the remaining initial data sets in ascending order according to the collection time corresponding to each set. For example, if the remaining initial data sets are A1, A2, and A3, and the corresponding collection times are 1 o'clock, 2 o'clock, and 3 o'clock respectively, then the initial dataset is {A1, A2, A3}.

[0050] S103, identify the low-speed dataset in the initial dataset, and determine the dataset to be deleted and the target key set based on the low-speed dataset.

[0051] The low-speed dataset consists of the initial data set whose average vehicle speed is less than a preset vehicle speed threshold, as mentioned in the following embodiments. The dataset to be deleted is the dataset to be removed from the initial dataset. The target key set contains target key points and is used to divide the trajectory dataset.

[0052] S104, delete the dataset to be deleted from the initial dataset, and sort it in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset.

[0053] The trajectory dataset is a dataset obtained by sorting the acquisition times corresponding to each initial data group in the remaining initial dataset after deleting the dataset to be deleted from the initial dataset in ascending order.

[0054] S105: Using the trajectory dataset and target key set, obtain the vehicle's driving trajectory within a preset time period.

[0055] The vehicle's trajectory is obtained by connecting the points determined by longitude and latitude data.

[0056] The vehicle trajectory determination method provided in this embodiment obtains the longitude, latitude, and speed of the target vehicle at each collection time within a preset time period. An initial data set is formed from the longitude, latitude, speed, and collection time at each collection time. All initial data sets are corrected to obtain the remaining corrected initial data sets. The remaining initial data sets are then sorted in ascending order according to the collection time corresponding to each collection time to obtain an initial dataset. A low-speed dataset is identified within the initial dataset. Based on the low-speed dataset, a dataset to be deleted and a target key set are determined. The dataset to be deleted is deleted from the initial dataset. The remaining initial dataset is then sorted in ascending order according to the collection time corresponding to each collection time to obtain a trajectory dataset. Using the trajectory dataset and the target key set, the vehicle trajectory of the target vehicle within the preset time period is obtained. Compared to the low efficiency of traditional technologies for determining vehicle trajectories, this embodiment determines an initial dataset based on the collection time within a preset time period, as well as the longitude, latitude, and speed of the target vehicle. From this dataset, a low-speed dataset is identified, and further, a dataset to be deleted and a target key set are determined. Based on the initial dataset and the dataset to be deleted, a trajectory dataset is determined, and based on the trajectory dataset and the target key set, the vehicle's trajectory is determined, thus reducing data and improving the efficiency of determining the vehicle's trajectory.

[0057] In one embodiment, identifying the low-speed dataset from the initial dataset includes:

[0058] Obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0059] The preset vehicle speed threshold is set manually, for example, a preset vehicle speed threshold of 2 km / h. The continuous average vehicle speed refers to the average vehicle speed corresponding to continuous data collection time.

[0060] For example, the average vehicle speed is calculated as follows: the vehicle speed at the current collection time is v1, the vehicle speed at the next collection time is v2, and the average vehicle speed at the current collection time is v_avg1 = (v1 + v2) / 2.

[0061] There can be more than one low-speed dataset determined from the initial dataset, and each low-speed dataset contains at least two initial data groups.

[0062] In this embodiment, the data in the low-speed dataset changes less, thus improving accuracy.

[0063] In one embodiment, the dataset to be deleted includes a first dataset to be deleted and a second dataset to be deleted, and the target key set includes a first target key set and a second target key set; a flowchart illustrating the process of determining the dataset to be deleted and the target key set based on the low-speed dataset is shown below. Figure 2 As shown, it includes the following:

[0064] S201, take the second to the last initial data group in the low-speed dataset as the first dataset to be deleted.

[0065] S202, take the first initial data group in the low-speed dataset as the candidate key data group, take the longitude and latitude data corresponding to each candidate key data group as the candidate key points, and determine the candidate key point combination vector based on the candidate key points corresponding to two adjacent low-speed datasets.

[0066] S203, calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, take the candidate key point corresponding to the angle as the first target key point, and combine the candidate key data groups corresponding to the first target key point into the first target key set.

[0067] The preset angle threshold can usually be set to 90°, but there is no specific limitation.

[0068] In some embodiments, the angle between the combined vectors of two adjacent candidate key points is calculated using the law of cosines.

[0069] S204, based on the candidate key data set and the first target key set, determine the second target key set and the second dataset to be deleted.

[0070] In this embodiment, the dataset to be deleted includes a first dataset to be deleted and a second dataset to be deleted, and the target key set includes a first target key set and a second target key set. The acquisition methods of the first dataset to be deleted and the first target key set are clearly defined so that they can be used in subsequent determination of vehicle driving trajectory, thereby improving the efficiency of vehicle driving trajectory determination.

[0071] In one embodiment, a flowchart illustrating the process of determining a second target key set and a second dataset to be deleted based on candidate key data sets and a first target key set is shown below. Figure 3 As shown, it includes the following:

[0072] S301, construct a candidate dataset from the candidate key data groups, delete the first target key set from the candidate dataset, and obtain the remaining dataset.

[0073] S302, take the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points, if the distance is not greater than the preset distance threshold, then take the remaining key points as the second target key points, and combine the candidate key data groups corresponding to the second target key points into the second target key set.

[0074] The preset location points are manually set based on the locations frequently visited by the target vehicle. The preset distance threshold is also manually set, typically 200 meters, but the specific value is not limited.

[0075] S303, if the distance is greater than the preset distance threshold, the candidate key data group corresponding to the remaining key points is taken as the second data group to be deleted, and the second dataset to be deleted is determined based on the second data group to be deleted.

[0076] In this embodiment, the dataset to be deleted includes a first dataset to be deleted and a second dataset to be deleted, and the target key set includes a first target key set and a second target key set. The acquisition method of the second dataset to be deleted and the second target key set is further clarified so that they can be used in subsequent determination of vehicle driving trajectory, thereby improving the efficiency of vehicle driving trajectory determination.

[0077] In one embodiment, a flowchart illustrating the process of obtaining the vehicle trajectory of a target vehicle within a preset time period using a trajectory dataset and a target key set is shown below. Figure 4 As shown, it includes the following steps:

[0078] S401, the trajectory dataset is divided according to the target key set to obtain the divided trajectory dataset.

[0079] In some embodiments, the trajectory dataset is divided according to the target key set by dividing the trajectory dataset according to the target key points in the target key set. The first target key set includes first target key points, and the second target key set includes second target key points.

[0080] S402 uses the Douglas-Puk algorithm to simplify the segmented trajectory dataset and obtain the vehicle's driving trajectory within a preset time period.

[0081] In this embodiment, the Ramer-Douglas-Peucker algorithm (RDP) is used to simplify the segmented trajectory dataset, which can avoid generating overly complex vehicle driving trajectories for easier viewing by the administrator.

[0082] In one embodiment, all initial data sets are corrected to obtain corrected remaining initial data sets, including:

[0083] Based on the longitude, latitude, or acquisition time in each initial data set, identify the drift data sets among all initial data sets, and delete the drift data sets from all initial data sets to obtain the remaining initial data sets.

[0084] In some embodiments, the drift data set is determined by determining whether the acquisition time is within a preset time period. If not, the initial data set corresponding to the acquisition time is used as the offset data set; or the location is determined based on longitude and latitude data. If the location is not in China, the initial data set corresponding to the longitude and latitude data is used as the offset data set.

[0085] In this embodiment, all initial data sets are corrected to obtain the remaining corrected initial data sets, which can improve the accuracy of the determined vehicle trajectory.

[0086] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0087] Based on the same inventive concept, this application also provides a vehicle trajectory determination device for implementing the vehicle trajectory determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more vehicle trajectory determination device embodiments provided below can be found in the limitations of the vehicle trajectory determination method described above, and will not be repeated here.

[0088] See Figure 5 , Figure 5 This is a structural block diagram of a vehicle trajectory determination device provided in an embodiment of this application. The device 500 includes: an initial data set acquisition module 501, an initial dataset acquisition module 502, a low-speed dataset determination module 503, a trajectory dataset determination module 504, and a trajectory acquisition module 505, wherein:

[0089] The initial data set acquisition module 501 is used to acquire the longitude data, latitude data and vehicle speed of the target vehicle at each collection time within a preset time period. The initial data set is composed of the longitude data, latitude data, vehicle speed and collection time corresponding to each collection time. All initial data sets are corrected to obtain the corrected remaining initial data set.

[0090] The initial dataset acquisition module 502 is used to sort the data in ascending order according to the acquisition time corresponding to each remaining initial data group to obtain the initial dataset.

[0091] The low-speed dataset determination module 503 is used to determine the low-speed dataset in the initial dataset, and to determine the dataset to be deleted and the target key set based on the low-speed dataset;

[0092] The trajectory dataset determination module 504 is used to delete the datasets to be deleted from the initial dataset and sort them in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset.

[0093] The trajectory acquisition module 505 is used to acquire the vehicle driving trajectory of the target vehicle within a preset time period using the trajectory dataset and the target key set.

[0094] The vehicle trajectory determination device provided in this embodiment acquires the longitude, latitude, and speed of the target vehicle at each acquisition time within a preset time period. An initial data set is formed from the longitude, latitude, speed, and acquisition time at each acquisition time. All initial data sets are corrected to obtain the remaining corrected initial data sets. The remaining initial data sets are then sorted in ascending order according to the acquisition time corresponding to each acquisition time to obtain an initial dataset. A low-speed dataset is identified within the initial dataset. Based on the low-speed dataset, a dataset to be deleted and a target key set are determined. The dataset to be deleted is deleted from the initial dataset. The remaining initial dataset is then sorted in ascending order according to the acquisition time corresponding to each initial data set to obtain a trajectory dataset. Using the trajectory dataset and the target key set, the vehicle trajectory of the target vehicle within the preset time period is obtained. Compared to the low efficiency of traditional technologies for determining vehicle trajectories, this embodiment determines an initial dataset based on the collection time within a preset time period, as well as the longitude, latitude, and speed of the target vehicle. From this dataset, a low-speed dataset is identified, and further, a dataset to be deleted and a target key set are determined. Based on the initial dataset and the dataset to be deleted, a trajectory dataset is determined, and based on the trajectory dataset and the target key set, the vehicle's trajectory is determined, thus reducing data and improving the efficiency of determining the vehicle's trajectory.

[0095] Optionally, the low-speed dataset determination module 503 includes:

[0096] The low-speed dataset determination unit is used to obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0097] Optionally, the dataset to be deleted includes a first dataset to be deleted and a second dataset to be deleted, and the target key set includes a first target key set and a second target key set; the low-speed dataset determination module 503 includes:

[0098] The first data set to be deleted determination unit is used to take the second to the last initial data set in the low-speed dataset as the first data set to be deleted.

[0099] The candidate key point determination unit is used to take the first initial data group in the low-speed dataset as the candidate key data group, take the longitude and latitude data corresponding to each candidate key data group as the candidate key points, and determine the candidate key point combination vector based on the candidate key points corresponding to two adjacent low-speed datasets.

[0100] The first target key set determination unit is used to calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, the candidate key point corresponding to the angle is taken as the first target key point, and the candidate key data group corresponding to the first target key point is combined into the first target key set.

[0101] The determination unit is used to determine the second target key set and the second dataset to be deleted based on the candidate key data set and the first target key set.

[0102] Optionally, the determining unit includes:

[0103] The remaining dataset determination unit is used to construct a candidate dataset from the candidate key data groups, and to delete the first target key set from the candidate dataset to obtain the remaining dataset;

[0104] The second target key set determination unit is used to take the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points, and if the distance is not greater than the preset distance threshold, then the remaining key points are used as the second target key points, and the candidate key data groups corresponding to the second target key points are combined into the second target key set.

[0105] The second data set to be deleted determination unit is used to determine the second data set to be deleted based on the candidate key data set corresponding to the remaining key points if the distance is greater than a preset distance threshold.

[0106] Optionally, the trajectory acquisition module 505 includes:

[0107] The partitioning unit is used to divide the trajectory dataset according to the target key set, resulting in a partitioned trajectory dataset;

[0108] The trajectory acquisition unit is used to simplify the divided trajectory dataset using the Douglas-Puk algorithm to obtain the vehicle's driving trajectory within a preset time period.

[0109] Optionally, the initial data set acquisition module 501 includes:

[0110] The correction unit is used to identify the drift data group in all initial data groups based on the longitude data, latitude data or acquisition time in each initial data group, and delete the drift data group from all initial data groups to obtain the remaining initial data groups.

[0111] The various modules in the aforementioned vehicle trajectory determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0112] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for determining a vehicle's driving trajectory. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0113] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the vehicle trajectory determination method provided in the above embodiment.

[0115] The system acquires the longitude, latitude, and speed of the target vehicle at each collection moment within a preset time period. An initial data set is formed by the longitude, latitude, speed, and collection moment at each collection moment. All initial data sets are corrected to obtain the remaining corrected initial data sets.

[0116] Sort the remaining initial data sets in ascending order according to the acquisition time corresponding to each set of initial data to obtain the initial dataset;

[0117] In the initial dataset, identify the low-speed dataset, and based on the low-speed dataset, determine the dataset to be deleted and the target key set;

[0118] Remove the datasets to be deleted from the initial dataset, and sort them in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset;

[0119] Using trajectory datasets and target key sets, the vehicle's driving trajectory within a preset time period is obtained.

[0120] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0121] Obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0122] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0123] The dataset to be deleted includes the first dataset to be deleted and the second dataset to be deleted; the target key set includes the first target key set and the second target key set; the second to the last initial data set in the low-speed dataset are taken as the first dataset to be deleted.

[0124] The first initial data group in the low-speed dataset is used as the candidate key data group. The longitude and latitude data corresponding to each candidate key data group are used as candidate key points. Based on the candidate key points corresponding to two adjacent low-speed datasets, the candidate key point combination vector is determined.

[0125] Calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, take the candidate key point corresponding to the angle as the first target key point, and combine the candidate key data groups corresponding to the first target key point into the first target key set.

[0126] Based on the candidate key data sets and the first target key set, the second target key set and the second dataset to be deleted are determined.

[0127] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0128] The candidate key data groups are combined to form a candidate dataset. The first target key set is deleted from the candidate dataset to obtain the remaining dataset.

[0129] Using the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points. If the distance is not greater than the preset distance threshold, then the remaining key points are used as the second target key points, and the candidate key data groups corresponding to the second target key points are combined into the second target key set.

[0130] If the distance is greater than the preset distance threshold, the candidate key data group corresponding to the remaining key points will be used as the second data group to be deleted, and the second dataset to be deleted will be determined based on the second data group to be deleted.

[0131] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0132] The trajectory dataset is divided according to the target key set to obtain the divided trajectory dataset;

[0133] The Douglas-Puk algorithm is used to simplify the segmented trajectory dataset to obtain the vehicle's driving trajectory within a preset time period.

[0134] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0135] Based on the longitude, latitude, or acquisition time in each initial data set, identify the drift data sets among all initial data sets, and delete the drift data sets from all initial data sets to obtain the remaining initial data sets.

[0136] The implementation principle and technical effects of the above embodiments are similar to those of the above method embodiments, and will not be repeated here.

[0137] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the steps of the vehicle trajectory determination method provided in the above embodiment:

[0138] The system acquires the longitude, latitude, and speed of the target vehicle at each collection moment within a preset time period. An initial data set is formed by the longitude, latitude, speed, and collection moment at each collection moment. All initial data sets are corrected to obtain the remaining corrected initial data sets.

[0139] Sort the remaining initial data sets in ascending order according to the acquisition time corresponding to each set of initial data to obtain the initial dataset;

[0140] In the initial dataset, identify the low-speed dataset, and based on the low-speed dataset, determine the dataset to be deleted and the target key set;

[0141] Remove the datasets to be deleted from the initial dataset, and sort them in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset;

[0142] Using trajectory datasets and target key sets, the vehicle's driving trajectory within a preset time period is obtained.

[0143] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0144] Obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0145] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0146] The dataset to be deleted includes the first dataset to be deleted and the second dataset to be deleted; the target key set includes the first target key set and the second target key set; the second to the last initial data set in the low-speed dataset are taken as the first dataset to be deleted.

[0147] The first initial data group in the low-speed dataset is used as the candidate key data group. The longitude and latitude data corresponding to each candidate key data group are used as candidate key points. Based on the candidate key points corresponding to two adjacent low-speed datasets, the candidate key point combination vector is determined.

[0148] Calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, take the candidate key point corresponding to the angle as the first target key point, and combine the candidate key data groups corresponding to the first target key point into the first target key set.

[0149] Based on the candidate key data sets and the first target key set, the second target key set and the second dataset to be deleted are determined.

[0150] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0151] The candidate key data groups are combined to form a candidate dataset. The first target key set is deleted from the candidate dataset to obtain the remaining dataset.

[0152] Using the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points. If the distance is not greater than the preset distance threshold, then the remaining key points are used as the second target key points, and the candidate key data groups corresponding to the second target key points are combined into the second target key set.

[0153] If the distance is greater than the preset distance threshold, the candidate key data group corresponding to the remaining key points will be used as the second data group to be deleted, and the second dataset to be deleted will be determined based on the second data group to be deleted.

[0154] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0155] The trajectory dataset is divided according to the target key set to obtain the divided trajectory dataset;

[0156] The Douglas-Puk algorithm is used to simplify the segmented trajectory dataset to obtain the vehicle's driving trajectory within a preset time period.

[0157] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0158] Based on the longitude, latitude, or acquisition time in each initial data set, identify the drift data sets among all initial data sets, and delete the drift data sets from all initial data sets to obtain the remaining initial data sets.

[0159] The implementation principle and technical effects of the above embodiments are similar to those of the above method embodiments, and will not be repeated here.

[0160] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the vehicle trajectory determination method provided in the above embodiment:

[0161] The system acquires the longitude, latitude, and speed of the target vehicle at each collection moment within a preset time period. An initial data set is formed by the longitude, latitude, speed, and collection moment at each collection moment. All initial data sets are corrected to obtain the remaining corrected initial data sets.

[0162] Sort the remaining initial data sets in ascending order according to the acquisition time corresponding to each set of initial data to obtain the initial dataset;

[0163] In the initial dataset, identify the low-speed dataset, and based on the low-speed dataset, determine the dataset to be deleted and the target key set;

[0164] Remove the datasets to be deleted from the initial dataset, and sort them in ascending order according to the acquisition time corresponding to each initial data group in the remaining initial dataset to obtain the trajectory dataset;

[0165] Using trajectory datasets and target key sets, the vehicle's driving trajectory within a preset time period is obtained.

[0166] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0167] Obtain the collection time and vehicle speed corresponding to each initial data group in the initial dataset, determine the vehicle speed at the next collection time corresponding to the next collection time, average the vehicle speed with the vehicle speed at the next time to obtain the average vehicle speed, and form a low-speed dataset by combining the initial data groups with consecutive average vehicle speeds less than a preset vehicle speed threshold.

[0168] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0169] The dataset to be deleted includes the first dataset to be deleted and the second dataset to be deleted; the target key set includes the first target key set and the second target key set; the second to the last initial data set in the low-speed dataset are taken as the first dataset to be deleted.

[0170] The first initial data group in the low-speed dataset is used as the candidate key data group. The longitude and latitude data corresponding to each candidate key data group are used as candidate key points. Based on the candidate key points corresponding to two adjacent low-speed datasets, the candidate key point combination vector is determined.

[0171] Calculate the angle between the combined vectors of two adjacent candidate key points. If the angle is not less than a preset angle threshold, take the candidate key point corresponding to the angle as the first target key point, and combine the candidate key data groups corresponding to the first target key point into the first target key set.

[0172] Based on the candidate key data sets and the first target key set, the second target key set and the second dataset to be deleted are determined.

[0173] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0174] The candidate key data groups are combined to form a candidate dataset. The first target key set is deleted from the candidate dataset to obtain the remaining dataset.

[0175] Using the longitude and latitude data corresponding to each candidate key data group in the remaining dataset as the remaining key points, calculate the distance between the remaining key points and the preset location points. If the distance is not greater than the preset distance threshold, then the remaining key points are used as the second target key points, and the candidate key data groups corresponding to the second target key points are combined into the second target key set.

[0176] If the distance is greater than the preset distance threshold, the candidate key data group corresponding to the remaining key points will be used as the second data group to be deleted, and the second dataset to be deleted will be determined based on the second data group to be deleted.

[0177] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0178] The trajectory dataset is divided according to the target key set to obtain the divided trajectory dataset;

[0179] The Douglas-Puk algorithm is used to simplify the segmented trajectory dataset to obtain the vehicle's driving trajectory within a preset time period.

[0180] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0181] Based on the longitude, latitude, or acquisition time in each initial data set, identify the drift data sets among all initial data sets, and delete the drift data sets from all initial data sets to obtain the remaining initial data sets.

[0182] The implementation principle and technical effects of the above embodiments are similar to those of the above method embodiments, and will not be repeated here.

[0183] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0184] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle travel trajectory determination method characterized by comprising: The method comprises: acquiring longitude data, latitude data and vehicle speed corresponding to each collection time of a target vehicle within a preset time length, forming an initial data group from the longitude data, latitude data, vehicle speed and collection time corresponding to each collection time, correcting all initial data groups to obtain a remaining initial data group after correction; sorting the initial data group in ascending order according to the collection time corresponding to each remaining initial data group to obtain an initial data set; determining a low-speed data set in the initial data set, determining a to-be-deleted data set and a target key set based on the low-speed data set; deleting the to-be-deleted data set from the initial data set and sorting the remaining initial data set in ascending order according to the collection time corresponding to each initial data group to obtain a trajectory data set; acquiring a vehicle driving trajectory of the target vehicle within the preset time length by using the trajectory data set and the target key set; the to-be-deleted data set comprises a first to-be-deleted data set and a second to-be-deleted data set, and the target key set comprises a first target key set and a second target key set; the target key set is determined based on the low-speed data set, comprising: the second initial data group to the last initial data group in the low-speed data set is taken as the first to-be-deleted data set; the first initial data group in the low-speed data set is taken as a candidate key data group, the longitude data and latitude data corresponding to each candidate key data group are taken as candidate key points, and a candidate key point combination vector is determined according to the candidate key points corresponding to adjacent two low-speed data sets; an included angle of adjacent two candidate key point combination vectors is calculated, in a case where the included angle is not less than a preset angle threshold, the candidate key points corresponding to the included angle are taken as first target key points, and the candidate key data groups corresponding to the first target key points are combined into a first target key set; a second target key set and a second to-be-deleted data set are determined based on the candidate key data group and the first target key set.

2. The method of claim 1, wherein, the low-speed data set is determined in the initial data set, comprising: acquiring the collection time and vehicle speed corresponding to each initial data group in the initial data set, determining a next time vehicle speed corresponding to a next collection time of the collection time, averaging the vehicle speed and the next time vehicle speed to obtain an average vehicle speed, and constructing a low-speed data set from initial data groups with continuous average vehicle speeds less than a preset vehicle speed threshold.

3. The method of claim 1, wherein, the second target key set and the second to-be-deleted data set are determined based on the candidate key data group and the first target key set, comprising: candidate key data groups are constructed into a candidate data set, the first target key set is deleted from the candidate data set to obtain a remaining data set; longitude data and latitude data corresponding to each candidate key data group in the remaining data set are taken as remaining key points, a distance between the remaining key points and a preset position point is calculated, if the distance is not greater than a preset distance threshold, the remaining key points are taken as second target key points, and the candidate key data groups corresponding to the second target key points are combined into a second target key set; If the distance is greater than a preset distance threshold, the candidate key data group corresponding to the remaining key points is taken as a second to-be-deleted data group, and a second to-be-deleted data set is determined based on the second to-be-deleted data group.

4. The method of claim 1, wherein, The vehicle driving track of the target vehicle in the preset time length is obtained by using the track data set and the target key set, including: The track data set is divided according to the target key set to obtain a divided track data set; The track data set is simplified by using a Douglas-Peucker algorithm to obtain the vehicle driving track of the target vehicle in the preset time length.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Based on the longitude data, the latitude data or the collection time in each initial data group, a drift data group is determined in all initial data groups, and the drift data group is deleted from all initial data groups to obtain the remaining initial data groups.

6. A vehicle travel trajectory determination device characterized by comprising: The device includes: An initial data group acquisition module is configured to acquire longitude data, latitude data and vehicle speed corresponding to each collection time of a target vehicle in a preset time length, and to form an initial data group from the longitude data, the latitude data, the vehicle speed and the collection time corresponding to each collection time, and to correct all initial data groups to obtain remaining initial data groups after correction; An initial data set acquisition module is configured to sort the collection times corresponding to each remaining initial data group in ascending order to obtain an initial data set; A low-speed data set determination module is configured to determine a low-speed data set in the initial data set, to determine a to-be-deleted data set and a target key set based on the low-speed data set, and to include a first to-be-deleted data set and a second to-be-deleted data set in the to-be-deleted data set and to include a first target key set and a second target key set in the target key set; The low-speed data set determination module includes: A first to-be-deleted data set determination unit is configured to take the second initial data group to the last initial data group in the low-speed data set as the first to-be-deleted data set; A candidate key point determination unit is configured to take the first initial data group in the low-speed data set as a candidate key data group, to take the longitude data and the latitude data corresponding to each candidate key data group as a candidate key point, and to determine a candidate key point combination vector according to the candidate key points corresponding to adjacent two low-speed data sets; A first target key set determination unit is configured to calculate the included angle of adjacent two candidate key point combination vectors, to take the candidate key points corresponding to the included angle as first target key points in the case where the included angle is not less than a preset angle threshold, and to combine the candidate key data groups corresponding to the first target key points into a first target key set; A determination unit is configured to determine a second target key set and a second to-be-deleted data set based on the candidate key data group and the first target key set; A track data set determination module is configured to delete the to-be-deleted data set from the initial data set, to sort the collection times corresponding to each initial data group in the remaining initial data set in ascending order, and to obtain a track data set. An orbit acquiring module is configured to acquire a vehicle orbit of the target vehicle within the preset time length by using the orbit data set and the target key set. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

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