Data processing method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a data processing method and device, electronic equipment and storage medium. The method comprises: obtaining movement trajectory data of a plurality of movable devices, the movement trajectory data comprising trajectory segments of a plurality of road segments and road segment identifiers corresponding to the road segments; performing clustering processing on the trajectory segments according to the road segment identifiers to obtain a plurality of trajectory segment sets, wherein each trajectory segment set comprises a plurality of trajectory segments corresponding to a same target road segment; and performing fusion processing on the plurality of trajectory segments in the trajectory segment set corresponding to a target road segment to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updating. The present disclosure collects movement trajectory data of a plurality of movable devices, and performs clustering and fusion on trajectory segments of a same road segment based on road segment identifiers to generate a reference trajectory for navigation or map updating, thereby improving the output quality of a movement path.

Description

Technical Field

[0001] This disclosure relates to data processing technology applied in the field of vehicles, and more particularly to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of intelligent assisted driving technology, the automotive industry is shifting from relying on high-precision maps to lightweight or map-free solutions. However, the road information provided by lightweight map solutions is insufficient to support lane-level navigation path guidance; and map-free solutions struggle to guarantee the stability and safety of driving trajectories in complex scenarios.

[0003] Therefore, improving the output quality of navigation paths is an urgent problem to be solved. Summary of the Invention

[0004] This disclosure provides a data processing method, apparatus, electronic device, and storage medium.

[0005] This disclosure provides a data processing method in its first aspect, comprising: acquiring movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; clustering the trajectory segments according to the road segment identifiers to obtain multiple sets of trajectory segments; wherein each set of trajectory segments includes multiple trajectory segments corresponding to the same target road segment; and for any target road segment, fusing the multiple trajectory segments in the set of trajectory segments corresponding to the target road segment to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updates. This disclosure improves the output quality of navigation paths by aggregating movement trajectory data of multiple mobile devices and clustering and fusing trajectory segments of the same road segment based on road segment identifiers to generate a reference trajectory for navigation or map updates.

[0006] In some embodiments of this disclosure, the step of fusing multiple trajectory segments in the trajectory segment set corresponding to any target road segment to generate a reference trajectory corresponding to the target road segment includes: for any target road segment, determining the weight corresponding to each trajectory segment based on the positioning accuracy of each trajectory segment; and performing a weighted average processing on multiple trajectory segments in the trajectory segment set corresponding to the target road segment based on the weights of each trajectory segment to obtain a reference trajectory corresponding to the target road segment. This disclosure effectively suppresses interference from low-precision data and improves the accuracy and stability of the generated reference trajectory by determining the corresponding weights based on the positioning accuracy of each trajectory segment and performing a weighted average fusion of multiple trajectory segments from the same road segment, thereby providing a high-quality data foundation for highly reliable navigation or map updates.

[0007] In some embodiments of this disclosure, the method further includes: for any set of trajectory segments, filtering multiple trajectory segments in the set using preset filtering conditions; in response to a trajectory segment in the set satisfying the filtering conditions, retaining the trajectory segment satisfying the filtering conditions in the set of trajectory segments; in response to a trajectory segment in the set not satisfying the filtering conditions, reconstructing the trajectory segment that does not satisfy the filtering conditions, and replacing the trajectory segment that does not satisfy the preset filtering conditions with the reconstructed trajectory segment before saving it in the set of trajectory segments. This disclosure, by filtering and reconstructing the set of trajectory segments, can eliminate low-quality or abnormal trajectory data, retain high-precision trajectory segments, and smoothly repair trajectory segments that do not satisfy the filtering conditions based on adjacent valid trajectories, thereby improving the overall consistency and reliability of trajectory data and providing a more accurate data foundation for subsequent reference trajectory generation, navigation, and map updates.

[0008] In some embodiments of this disclosure, the trajectory reconstruction of trajectory segments that do not meet the filtering conditions includes: for any trajectory segment that does not meet the filtering conditions, obtaining a first trajectory segment corresponding to the preceding road segment and a second trajectory segment corresponding to the following road segment adjacent to the trajectory segment that does not meet the filtering conditions; and smoothly reconstructing the trajectory segment that does not meet the filtering conditions based on the first trajectory segment and the second trajectory segment to obtain the reconstructed trajectory segment. This disclosure effectively repairs the trajectory breakage problem by smoothly reconstructing the trajectory segments that do not meet the filtering conditions using the effective trajectory segments of their preceding and following road segments and replacing the original abnormal data, significantly improving the integrity, continuity, and usability of trajectory data, and providing high-quality input for high-precision reference trajectory generation and reliable navigation.

[0009] In some embodiments of this disclosure, the first trajectory segment and the second trajectory segment overlap. This disclosure improves the continuity and spatial consistency of trajectory stitching by fusing trajectory data from the overlapping areas, thereby enhancing the accuracy and robustness of the reference trajectory.

[0010] In some embodiments of this disclosure, the preset filtering conditions include at least one of the following:

[0011] The length of the trajectory segment is greater than the preset minimum trajectory length; Within a preset continuous trajectory length, the positioning accuracy of the trajectory segment meets the preset positioning accuracy. The angle between the extension direction of the trajectory segment and the driving direction of the corresponding lane is less than a set angle.

[0012] This disclosure effectively filters out invalid trajectory data that is short, low-precision, or has abnormal direction by setting multi-dimensional filtering conditions, including trajectory length, positioning accuracy, and consistency of driving direction. This significantly improves the quality and reliability of trajectory segment sets and lays a reliable foundation for generating high-precision reference trajectories.

[0013] In some embodiments of this disclosure, acquiring the movement trajectory data of multiple mobile devices includes: for any mobile device, segmenting the historical movement trajectory of the mobile device according to the road segment division boundaries to obtain trajectory segments corresponding to each road segment; and associating each trajectory segment with its corresponding road segment identifier to obtain the movement trajectory data. This disclosure, by accurately segmenting the historical movement trajectory of each mobile device according to road segment division boundaries and associating trajectory segments with corresponding road segment identifiers, clearly identifies the specific road segment to which each trajectory segment belongs, thereby constructing complete movement trajectory data for the mobile device.

[0014] In some embodiments of this disclosure, the step of segmenting the historical movement trajectory of the mobile device according to the road segment division boundaries to obtain trajectory segments corresponding to each road segment includes: for any target road segment, extending a first predetermined length towards the adjacent preceding road segment and a second predetermined length towards the adjacent following road segment, based on the target road segment division boundary, to obtain a target trajectory segment; and determining the target trajectory segment as the trajectory segment corresponding to the target road segment. Through this extension operation, this disclosure can obtain trajectory segments that overlap with the preceding and following road segments. These overlapping trajectory segments can be seamlessly spliced ​​together in subsequent use, thereby ensuring the continuity and integrity of the entire trajectory.

[0015] In some embodiments of this disclosure, the method further includes: in response to a navigation request, determining a first reference trajectory of the road segment where the target device initiating the navigation request is located and at least one second reference trajectory of a subsequent road segment; in response to the existence of a second reference trajectory with a confidence level greater than a set confidence level among the at least one second reference trajectory of the subsequent road segment, concatenating the first reference trajectory with the second reference trajectory with a confidence level greater than the set confidence level to obtain a navigation path; wherein the navigation path is used for navigation of the target device. This disclosure, by selecting a concatenation strategy based on the confidence level of the second reference trajectory, in scenarios where a high-confidence reference trajectory exists, directly merges it with the first reference trajectory of the current road segment to generate a navigation path, effectively ensuring the accuracy of the path and environmental consistency, and significantly improving the reliability, adaptability, and user experience of the navigation system.

[0016] In some embodiments of this disclosure, the method further includes: responding to the fact that the confidence levels of at least one second reference trajectory in the subsequent road segment are all less than or equal to the set confidence level, based on the end state information of the first reference trajectory, performing correction and smoothing processing on the second reference trajectory with the highest confidence level among the at least one second reference trajectory in the subsequent road segment, and concatenating the corrected and smoothed second reference trajectory with the first reference trajectory to obtain the navigation path. This disclosure, when the confidence levels of all second reference trajectories in the subsequent road segment are insufficient, selects the trajectory with the highest confidence level and performs correction and smoothing processing in conjunction with the end state information of the first reference trajectory, effectively fusing local reliable information and motion continuity constraints, thereby generating a reasonable and stable navigation path and improving navigation robustness and safety in low-confidence scenarios.

[0017] A second aspect of this disclosure provides a data processing method applied to a mobile device, comprising: sending mobile trajectory data of the mobile device to a server, wherein the mobile trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; wherein the mobile trajectory data is used by the server to perform clustering processing on the trajectory segments according to the road segment identifiers to obtain multiple trajectory segment sets, and to perform fusion processing on multiple trajectory segments of the same target road segment in the same trajectory segment set to generate a reference trajectory corresponding to the same target road segment, wherein the reference trajectory is used for navigation or map updates.

[0018] A third aspect of this disclosure provides a data processing apparatus, comprising: an acquisition module for acquiring movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; a first processing module for clustering the trajectory segments according to the road segment identifiers to obtain multiple trajectory segment sets; wherein each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment; and a second processing module for fusing multiple trajectory segments in the trajectory segment sets corresponding to any target road segment to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updates.

[0019] A fourth aspect of this disclosure provides a data processing apparatus for a mobile device, comprising: a sending module for sending mobile trajectory data of the mobile device to a server, wherein the mobile trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; wherein the mobile trajectory data is used by the server to perform clustering processing on the trajectory segments according to the road segment identifiers to obtain multiple trajectory segment sets, and to perform fusion processing on multiple trajectory segments of the same target road segment in the same trajectory segment set to generate a reference trajectory corresponding to the same target road segment, wherein the reference trajectory is used for navigation or map updates.

[0020] A fifth aspect of this disclosure provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the data processing method of the first aspect embodiment or the steps of the data processing method of the second aspect embodiment.

[0021] A sixth aspect of this disclosure provides a non-transitory computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the data processing method of the first aspect embodiment or the steps of the data processing method of the second aspect embodiment.

[0022] The data processing method, apparatus, electronic device, and storage medium disclosed herein acquire movement trajectory data from multiple mobile devices. This movement trajectory data includes trajectory segments from multiple road segments and road segment identifiers corresponding to each road segment. Based on the road segment identifiers, the trajectory segments are clustered to obtain multiple sets of trajectory segments, where each set includes multiple trajectory segments corresponding to the same target road segment. For any target road segment, the multiple trajectory segments in the corresponding set are fused to generate a reference trajectory corresponding to the target road segment. This reference trajectory is used for navigation or map updates. This disclosure improves the output quality of navigation paths by aggregating movement trajectory data from multiple mobile devices and clustering and fusing trajectory segments from the same road segment based on road segment identifiers to generate a reference trajectory for navigation or map updates.

[0023] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, in part in itself, and in part in practice, as may be revealed through practice of this disclosure. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1This is a flowchart of a data processing method according to an embodiment of the present disclosure; Figure 2 This is a flowchart of a data processing method according to another embodiment of the present disclosure; Figure 3 This is a flowchart illustrating a navigation trajectory generation method according to an embodiment of the present disclosure; Figure 4 This is a schematic diagram illustrating the trajectory processing and navigation process of a vehicle according to an embodiment of this disclosure; Figure 5 This is a flowchart of a data processing method according to yet another embodiment of the present disclosure; Figure 6 This is a block diagram of a data processing apparatus according to an embodiment of the present disclosure; Figure 7 This is a block diagram of a data processing apparatus according to another embodiment of the present disclosure; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0025] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0026] It should be noted that the data involved in the embodiments disclosed herein is anonymized data, does not involve the disclosure of user privacy, and can be uploaded only after obtaining user authorization.

[0027] The data processing method, apparatus, electronic device, and storage medium of this disclosure are described below with reference to the accompanying drawings.

[0028] Figure 1 This is a schematic flowchart of a data processing method according to an embodiment of the present disclosure.

[0029] It should be noted that the data processing method of this disclosure can be applied to a data processing device, which can be configured in an electronic device to enable the electronic device to perform vehicle data processing functions. The electronic device includes a vehicle and a server, where the server is also referred to as a server-side application or cloud computing platform. The data processing method of this disclosure is described using an example of execution on a server.

[0030] like Figure 1 As shown, the data processing method of this disclosure includes the following steps: S101, acquire the movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments, and road segment identifiers corresponding to each road segment.

[0031] The process of acquiring movement trajectory data for any mobile device is as follows: Based on the road segment boundaries, the historical movement trajectories uploaded by mobile devices are segmented to obtain trajectory fragments corresponding to each road segment. These trajectory fragments accurately reflect the travel trajectory information of the mobile device within different road segments. After segmentation, each trajectory fragment needs to be associated with its corresponding road segment identifier. Through this association operation, the specific road segment to which each trajectory fragment belongs can be clearly identified, thereby constructing complete movement trajectory data for the mobile device.

[0032] During the acquisition of motion trajectory data, to ensure the continuity and accuracy of the stitching, for any target road segment, using the target road segment boundary as a reference, a first predetermined length is extended towards the adjacent preceding road segment, and a second predetermined length is extended towards the adjacent following road segment, resulting in a target trajectory segment. This target trajectory segment is then identified as the trajectory segment corresponding to the target road segment. Through this extension operation, trajectory segments that overlap with the preceding and following road segments can be obtained. These overlapping trajectory segments allow for seamless stitching between two adjacent trajectory segments in subsequent use, thereby ensuring the continuity and integrity of the entire trajectory.

[0033] To further improve the integrity and quality of mobile trajectory data, the following processing methods can be adopted during the acquisition process: trajectory segments corresponding to the starting and ending road segments in historical mobile trajectories can be removed, or these segments can be omitted from the data collection phase. The trajectory segments at the starting and ending road segments may have relatively low data quality due to limitations in data collection, abnormal conditions during device startup and shutdown, etc., which may interfere with subsequent trajectory analysis and navigation applications. Deleting or omitting these segments can effectively reduce noise and uncertainty in the data, making the trajectory data for the entire navigation segment more reliable and processing more efficient.

[0034] S102, based on the road segment identification, the trajectory segments are clustered to obtain multiple trajectory segment sets; each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment.

[0035] In other words, trajectory segments with the same road segment identifier are automatically grouped into the same trajectory segment set R. For example, all trajectory segments labeled "Road Segment A" are aggregated into set R_A; trajectory segments labeled "Road Segment B" are grouped into set R_B.

[0036] S103: For any target road segment, multiple trajectory segments in the corresponding trajectory segment set of the target road segment are fused to generate a reference trajectory corresponding to the target road segment. The reference trajectory is used for navigation or map updates.

[0037] For example, for any target road segment, based on the positioning accuracy of multiple trajectory segments corresponding to the target road segment, the weight of each trajectory segment is determined, wherein the higher the positioning accuracy, the greater the weight of the corresponding trajectory segment. Then, based on the weights of each trajectory segment, a weighted average is performed on the multiple trajectory segments in the set of trajectory segments corresponding to the target road segment to obtain a reference trajectory corresponding to the target road segment. This disclosure effectively suppresses interference from low-precision data and improves the accuracy and stability of the generated reference trajectory by determining the corresponding weights based on the positioning accuracy of each trajectory segment and performing a weighted average fusion of multiple trajectory segments from the same road segment, thereby providing a high-quality data foundation for highly reliable navigation or map updates.

[0038] Therefore, this disclosure generates a reference trajectory by aggregating the movement trajectory data of multiple mobile devices and clustering and fusing trajectory segments of the same road segment based on road segment identifiers, which can be used for navigation or map updates, thereby improving the output quality of navigation paths.

[0039] Figure 2 This is a schematic flowchart illustrating a data processing method according to another embodiment of this disclosure.

[0040] like Figure 2 As shown, the data processing method of this disclosure includes: S201, acquire the movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments, and road segment identifiers corresponding to each road segment.

[0041] S202, based on the road segment identification, the trajectory segments are clustered to obtain multiple trajectory segment sets; each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment.

[0042] It should be noted that the process of executing steps S201 and S202 is the same as that described above for steps S101 and S102, and will not be repeated here.

[0043] S203: For any set of trajectory segments, use preset filtering conditions to filter multiple trajectory segments in the set of trajectory segments.

[0044] For example, the filtering results include: trajectory segments in the trajectory segment set that meet the filtering conditions and trajectory segments in the trajectory segment set that do not meet the filtering conditions. Specifically, if a trajectory segment in the trajectory segment set meets the filtering conditions, the process proceeds to step S204 for further processing; if a trajectory segment in the trajectory segment set does not meet the filtering conditions, the process proceeds to step S205 to perform data correction.

[0045] The preset filtering criteria include at least one of the following: The length of the trajectory segment is greater than the preset minimum trajectory length to ensure that the length of the trajectory segment meets the usage requirements; Within the preset continuous trajectory length, the positioning accuracy of the trajectory segment continuously meets the preset positioning accuracy to ensure the spatial accuracy of the data; The angle between the extension direction of the trajectory segment and the driving direction of the corresponding lane is less than a set angle to exclude data of reverse driving or eccentricity.

[0046] This disclosure effectively filters out invalid trajectory data that is short, low-precision, or has abnormal direction by setting multi-dimensional filtering conditions, including trajectory length, positioning accuracy, and consistency of driving direction. This significantly improves the quality and reliability of trajectory segment sets and lays a reliable foundation for generating high-precision reference trajectories.

[0047] S204, in response to the trajectory segments in the trajectory segment set satisfying the filtering conditions, retain the trajectory segments that satisfy the filtering conditions in the trajectory segment set.

[0048] S205, in response to the trajectory segments in the trajectory segment set not meeting the filtering conditions, reconstruct the trajectory segments that do not meet the filtering conditions, and replace the trajectory segments that do not meet the preset filtering conditions with the reconstructed trajectory segments and save them in the trajectory segment set.

[0049] For any trajectory segment that does not meet the filtering criteria, a first trajectory segment corresponding to the preceding road segment and a second trajectory segment corresponding to the following road segment are obtained. Then, based on the geometric features (such as direction and curvature) and positioning information of the first and second trajectory segments, the trajectory segment that does not meet the filtering criteria is smoothly reconstructed to obtain the reconstructed trajectory segment. This disclosure effectively repairs the trajectory breakage problem by using the effective trajectory segments of the adjacent road segments to smoothly reconstruct the trajectory segment that does not meet the filtering criteria and replacing the original abnormal data. This significantly improves the integrity, continuity, and usability of trajectory data, providing high-quality input for high-precision reference trajectory generation and reliable navigation.

[0050] Furthermore, the first and second trajectory segments overlap. The overlap range can be flexibly set according to actual conditions. This disclosure fuses trajectory data from the overlapping areas, improving the continuity and spatial consistency of trajectory stitching, and enhancing the accuracy and robustness of the reference trajectory.

[0051] It should be noted that step S206 is executed after step S204 or step S205 is completed.

[0052] S206, for any target road segment in the trajectory segment set, perform fusion processing on multiple trajectory segments in the trajectory segment set corresponding to the target road segment to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updates.

[0053] It should be noted that the process of executing step S206 is the same as that described in step S103 above, and will not be repeated here.

[0054] Therefore, by filtering and reconstructing the set of trajectory segments, this disclosure can eliminate low-quality or abnormal trajectory data, retain high-precision trajectory segments, and perform smooth repair on trajectory segments that do not meet the filtering conditions based on adjacent valid trajectories, thereby improving the overall consistency and reliability of trajectory data and providing a more accurate data foundation for subsequent reference trajectory generation, navigation and map updates.

[0055] Figure 3 This is a flowchart illustrating a navigation trajectory generation method according to an embodiment of the present disclosure.

[0056] It should be noted that after executing step S103 or S206, the following can be executed: Figure 3 The navigation path trajectory generation process is shown.

[0057] like Figure 3 The navigation trajectory generation method of this disclosure includes: S301, in response to a navigation request, determines a first reference trajectory for the road segment where the target device initiating the navigation request is located and at least one second reference trajectory for subsequent road segments.

[0058] In other words, the navigation request initiated by the target device (such as a vehicle) determines its current road segment based on the target device's real-time location information and extracts it from a high-precision map: The first reference trajectory is the smoothed reference trajectory of the road segment where the target device is currently located; At least one second reference trajectory for subsequent road segments, i.e., the reference trajectory for subsequent adjacent road segments (which can be pre-selected based on navigation planning algorithms; the number depends on the path complexity and usually includes 1-3 alternative road segments).

[0059] S302, in response to the existence of a second reference trajectory with a confidence level greater than a set confidence level in at least one second reference trajectory of a subsequent road segment, the first reference trajectory is spliced ​​with the second reference trajectory with a confidence level greater than the set confidence level to obtain a navigation path; wherein, the navigation path is used for navigation of the target device.

[0060] For example, after obtaining the first reference trajectory and at least one second reference trajectory for subsequent road segments, the confidence level of each second reference trajectory for the subsequent road segments is compared with a set confidence level. If at least one second reference trajectory for a subsequent road segment has a confidence level greater than the set confidence level, the first reference trajectory is directly spliced ​​with the second reference trajectory whose confidence level is greater than the set confidence level to generate a navigation path. Subsequently, the generated navigation path can be sent to the target device for navigation.

[0061] For example, when the target device is a vehicle, after receiving the navigation path, the vehicle can be controlled to perform assisted driving according to the navigation path.

[0062] This disclosure selects a stitching strategy based on the confidence level of the second reference trajectory. In scenarios where a high-confidence reference trajectory exists, it directly merges the second reference trajectory with the first reference trajectory of the current road segment to generate a navigation path. This effectively ensures the accuracy of the path and the consistency with the environment, and significantly improves the reliability, adaptability, and user experience of the navigation system.

[0063] S303, in response to the fact that the confidence of at least one second reference trajectory in the subsequent road segment is less than or equal to the set confidence, based on the end state information of the first reference trajectory, the second reference trajectory with the highest confidence among at least one second reference trajectory in the subsequent road segment is corrected and smoothed, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

[0064] For example, if the confidence of all second reference trajectories in the subsequent road segment is greater than the set confidence, then the position, speed, heading angle and other state information are extracted from the end of the first reference trajectory. Using this as a constraint, the second reference trajectory with the highest confidence among at least one second reference trajectory in the subsequent road segment is corrected and smoothed. Then, the second reference trajectory obtained after correction and smoothing is spliced ​​with the first reference trajectory to obtain the navigation path.

[0065] In cases where the confidence levels of all second reference trajectories in subsequent road segments are insufficient, this disclosure selects the trajectory with the highest confidence level and combines it with the terminal state information of the first reference trajectory for correction and smoothing. This effectively integrates local reliable information and motion continuity constraints, thereby generating a reasonable and stable navigation path and improving the robustness and safety of navigation in low-confidence scenarios.

[0066] Therefore, this disclosure breaks through the strong dependence of crowdsourced mapping on vehicle-side perception results in related technologies. By performing cloud-based fusion processing on the raw trajectory data of each road segment identifier and selecting the optimal trajectory segment, a high-precision reference trajectory library covering the entire scenario is formed. When a mobile device (such as a vehicle) initiates a navigation request, the corresponding high-precision trajectory data is extracted from the reference trajectory library based on the current road segment identifier and subsequent related road segment identifiers specified in the request, then stitched together, and finally sent to the mobile device for its use.

[0067] Figure 4 This is a schematic diagram illustrating the trajectory processing and navigation of a vehicle according to an embodiment of the present disclosure.

[0068] It should be noted that, in this embodiment, the mobile device is illustrated using a vehicle as an example.

[0069] like Figure 4 As shown, the vehicle trajectory processing and navigation process disclosed herein includes: uploading historical navigation trajectories on the vehicle side, server-side processing, and vehicle-side application.

[0070] The process of uploading historical navigation routes from the vehicle includes: Figure 4 The left side shows the triggering mechanism for the vehicle (including the first vehicle, the second vehicle, ..., the nth vehicle). During operation, each vehicle uploads its historical navigation trajectory information to the cloud when the triggering conditions are met. This historical navigation trajectory information may include vehicle information that does not contain privacy data, such as the vehicle's location, speed, and direction.

[0071] The cloud processing process includes: After receiving the historical navigation trajectories uploaded by each vehicle, the cloud platform segments the historical movement trajectories to obtain multiple trajectory fragments corresponding to each road segment, such as trajectory fragment 1, trajectory fragment 2, ... Each trajectory fragment is then associated with a road segment identifier and stored in database 1.

[0072] Based on the road segment identification, the trajectory segments are clustered to obtain multiple sets of trajectory segments, namely set 1, set 2, ..., set m. For example, set 1 (ID1) includes multiple trajectory segments related to the road segment identification, which can be denoted as trajectory 1, trajectory 2, ..., trajectory p.

[0073] Multiple trajectory fragment sets are processed in parallel to obtain the fused trajectory of each road segment identifier, i.e., the reference trajectory, and stored in database 2.

[0074] The process of vehicle-side application includes: Figure 4The right side again shows the vehicle-side interface. The cloud will distribute the merged trajectory according to the vehicle-side's needs. For example, when a vehicle (such as the first vehicle, the second vehicle, ..., the nth vehicle) initiates a navigation request, the cloud extracts the reference trajectory of the current road segment and the reference trajectory of the subsequent road segments from database 2 based on the current road segment specified in the request, and distributes them to the vehicle-side interface.

[0075] Once the vehicle receives this trajectory data, it can be directly used by the vehicle's planning and control module to provide accurate navigation guidance and ensure that the vehicle can travel along the optimal path.

[0076] This disclosure generates a reference trajectory by aggregating historical navigation trajectories of multiple vehicles and clustering and fusing trajectory segments of the same road segment based on road segment identifiers. This reference trajectory originates from the aggregation of numerous real-world driving behaviors, conforms to road geometry constraints and driver traffic habits, and effectively constrains the path output of the intelligent driving system, preventing the generation of abnormal or dangerous trajectories and significantly improving vehicle safety and behavioral rationality in complex scenarios. Furthermore, this disclosure relies on lightweight road segment identifiers for trajectory organization and mapping, eliminating the need for real-time high-precision maps. This reduces communication and computational costs and enhances adaptability in environments with weak networks or missing signals. It is particularly suitable for security enhancement mechanisms in lightweight map or mapless architectures, improving the security of end-to-end models.

[0077] In summary, the data processing method proposed in this disclosure acquires movement trajectory data from multiple mobile devices, including trajectory segments of multiple road segments and corresponding road segment identifiers. Based on the road segment identifiers, the trajectory segments are clustered to obtain multiple sets of trajectory segments. Each set of trajectory segments includes multiple trajectory segments corresponding to the same target road segment. For any target road segment, the multiple trajectory segments in the corresponding set are fused to generate a reference trajectory corresponding to the target road segment. This reference trajectory is used for navigation or map updates. By aggregating movement trajectory data from multiple mobile devices and clustering and fusing trajectory segments of the same road segment based on road segment identifiers to generate a reference trajectory for navigation or map updates, this disclosure improves the output quality of navigation paths.

[0078] Figure 5 This is a flowchart of a data processing method according to yet another embodiment of the present disclosure.

[0079] It should be noted that the data processing method of this disclosure can be applied to a data processing device, which can be configured in an electronic device to enable the electronic device to perform vehicle data processing functions. The electronic device includes a mobile device, such as a vehicle. The data processing method of this disclosure is executed by the mobile device.

[0080] like Figure 5 As shown, the data processing method of this disclosure includes: S501, send the movement trajectory data of the mobile device to the server, wherein the movement trajectory data includes trajectory segments of multiple road segments, and road segment identifiers corresponding to each road segment; The mobile trajectory data is used by the server to cluster trajectory segments according to road segment identifiers to obtain multiple trajectory segment sets. Multiple trajectory segments of the same target road segment in the same trajectory segment set are then merged to generate a reference trajectory corresponding to the same target road segment. The reference trajectory is used for navigation or map updates.

[0081] In one embodiment of this disclosure, the method further includes: Send a navigation request to the server; the navigation request is used by the server to determine the road segment where the mobile device initiating the navigation request is located. The system receives a navigation path sent by the server. The generation of the navigation path includes the server using a first reference trajectory based on the road segment where the mobile device is located and at least one second reference trajectory for subsequent road segments. In response to the existence of a second reference trajectory with a confidence level greater than a set confidence level in at least one second reference trajectory for subsequent road segments, the system concatenates the first reference trajectory with the second reference trajectory with a confidence level greater than the set confidence level to obtain the navigation path. Navigation is based on the navigation path.

[0082] In one embodiment of this disclosure, the generation of the navigation path further includes: If the confidence level of at least one second reference trajectory in the subsequent road segment is less than or equal to the set confidence level, then based on the end state information of the first reference trajectory, the second reference trajectory with the highest confidence level among the at least one second reference trajectory in the subsequent road segment is corrected and smoothed, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

[0083] It should be noted that for details not disclosed in the data processing method applied to mobile devices in this disclosure embodiment, please refer to the details disclosed in the data processing method applied to servers described above, which will not be repeated here.

[0084] According to the data processing method for mobile devices according to embodiments of this disclosure, mobile trajectory data of the mobile device is sent to a server. The mobile trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment. The mobile trajectory data is used by the server to cluster the trajectory segments according to the road segment identifiers, obtaining multiple sets of trajectory segments. Multiple trajectory segments from the same target road segment within the same set are then fused to generate a reference trajectory corresponding to the same target road segment. This reference trajectory is used for navigation or map updates. By uploading trajectory segments carrying road segment identifiers to the server, the mobile device of this disclosure enables the server to cluster and fuse multiple trajectory segments of the same target road segment to generate a high-quality reference trajectory. This allows the mobile device to obtain accurate navigation paths without relying on high-precision maps, effectively improving navigation output quality and system robustness.

[0085] Figure 6 This is a block diagram of a data processing apparatus according to an embodiment of the present disclosure.

[0086] like Figure 6 As shown, the data processing apparatus 600 of this embodiment is applied on the server side and includes: an acquisition module 610, a first processing module 620 and a second processing module 630.

[0087] The acquisition module 610 is used to acquire the movement trajectory data of multiple mobile devices. The movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment. The first processing module 620 is used to cluster the trajectory segments according to the road segment identifier to obtain multiple trajectory segment sets; wherein, each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment; The second processing module 630 is used to perform fusion processing on multiple trajectory segments in the trajectory segment set corresponding to any target road segment to generate a reference trajectory corresponding to the target road segment. The reference trajectory is used for navigation or map updates.

[0088] In one embodiment of this disclosure, the second processing module 630 includes: The determination unit is used to determine the weight of each trajectory segment based on the positioning accuracy of each trajectory segment for any target road segment. The processing unit is used to perform weighted averaging on multiple trajectory segments in the set of trajectory segments corresponding to the target road segment based on the weights corresponding to each trajectory segment, so as to obtain the reference trajectory corresponding to the target road segment.

[0089] In one embodiment of this disclosure, the apparatus further includes: The filtering module is used to filter multiple trajectory segments in any given set of trajectory segments using preset filtering criteria. In response to the trajectory segments in the trajectory segment set meeting the filtering criteria, the trajectory segments that meet the filtering criteria are retained in the trajectory segment set; In response to a trajectory segment in the trajectory segment set not meeting the filtering criteria, the trajectory segment that does not meet the filtering criteria is reconstructed, and the reconstructed trajectory segment replaces the trajectory segment that does not meet the preset filtering criteria and is then saved in the trajectory segment set.

[0090] In one embodiment of this disclosure, the filtering module, when reconstructing the trajectory of trajectory segments that do not meet the filtering criteria, includes: For any trajectory segment that does not meet the filtering criteria, obtain the first trajectory segment corresponding to the preceding segment and the second trajectory segment corresponding to the following segment that is adjacent to the trajectory segment that does not meet the filtering criteria. Based on the first trajectory segment and the second trajectory segment, the trajectory segments that do not meet the filtering conditions are smoothly reconstructed to obtain the reconstructed trajectory segments.

[0091] In one embodiment of this disclosure, the first trajectory segment and the second trajectory segment have overlapping trajectories.

[0092] In one embodiment of this disclosure, the preset filtering conditions include at least one of the following: The length of the trajectory segment is greater than the preset minimum trajectory length; Within the preset continuous trajectory length, the positioning accuracy of the trajectory segment meets the preset positioning accuracy. The angle between the extension direction of the trajectory segment and the driving direction of the corresponding lane is less than the set angle.

[0093] In one embodiment of this disclosure, when the acquisition module 610 acquires movement trajectory data of multiple mobile devices, it includes: For any mobile device, the historical movement trajectory of the mobile device is segmented according to the road segment division boundaries to obtain the trajectory segments corresponding to each road segment. Each trajectory segment is then associated with the corresponding road segment identifier to obtain the movement trajectory data.

[0094] In one embodiment of this disclosure, the acquisition module 610 is used to segment the historical movement trajectory of the mobile device according to the road segment division boundaries of each road segment, and to obtain the trajectory segment corresponding to each road segment, including: For any target road segment, based on the boundary of the target road segment, extend a first predetermined length in the direction of the adjacent preceding road segment and a second predetermined length in the direction of the adjacent following road segment to obtain a target trajectory segment, and determine the target trajectory segment as the trajectory segment corresponding to the target road segment.

[0095] In one embodiment of this disclosure, the apparatus further includes: The navigation module is used to respond to a navigation request by determining a first reference trajectory of the road segment where the target device initiating the navigation request is located and at least one second reference trajectory of the subsequent road segment. In response to the existence of a second reference trajectory with a confidence level greater than a set confidence level among the at least one second reference trajectory of the subsequent road segment, the first reference trajectory is concatenated with the second reference trajectory with a confidence level greater than the set confidence level to obtain a navigation path. The navigation path is used for navigation of the target device.

[0096] In one embodiment of this disclosure, the navigation module is further configured to: In response to the fact that the confidence of at least one second reference trajectory in the subsequent road segment is less than or equal to the set confidence, based on the end state information of the first reference trajectory, the second reference trajectory with the highest confidence among at least one second reference trajectory in the subsequent road segment is corrected and smoothed, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

[0097] It should be noted that for details not disclosed in the data processing apparatus of this disclosure, please refer to the details disclosed in the data processing method of this disclosure, which will not be repeated here.

[0098] The data processing apparatus of this disclosure includes an acquisition module for acquiring movement trajectory data of multiple mobile devices. This movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment. A first processing module clusters the trajectory segments according to the road segment identifiers to obtain multiple trajectory segment sets. Each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment. A second processing module fuses the multiple trajectory segments in the trajectory segment set corresponding to any target road segment to generate a reference trajectory corresponding to the target road segment. This reference trajectory is used for navigation or map updates. This disclosure improves the output quality of navigation paths by aggregating movement trajectory data of multiple mobile devices and clustering and fusing trajectory segments of the same road segment based on road segment identifiers to generate a reference trajectory for navigation or map updates.

[0099] Figure 7 This is a block diagram of a data processing apparatus according to another embodiment of the present disclosure.

[0100] like Figure 7The data processing apparatus described in this embodiment of the present disclosure, applied on a vehicle, includes: a sending module 710.

[0101] The sending module 710 is used to send the movement trajectory data of the mobile device to the server. The movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment. The mobile trajectory data is used by the server to cluster trajectory segments according to road segment identifiers to obtain multiple trajectory segment sets. Multiple trajectory segments of the same target road segment in the same trajectory segment set are then merged to generate a reference trajectory corresponding to the same target road segment. The reference trajectory is used for navigation or map updates.

[0102] In one embodiment of this disclosure, the sending module 710 is further configured to send a navigation request to the server; wherein the navigation request is used by the server to determine the road segment where the mobile device initiating the navigation request is located. The above-mentioned device also includes: The receiving module is used to receive the navigation path sent by the server; wherein, the generation of the navigation path includes the server using a first reference trajectory based on the road segment where the mobile device is located and at least one second reference trajectory for subsequent road segments, and in response to the existence of a second reference trajectory with a confidence level greater than a set confidence level in at least one second reference trajectory for subsequent road segments, the first reference trajectory is concatenated with the second reference trajectory with a confidence level greater than the set confidence level to obtain the navigation path; The navigation module is used for navigation based on a navigation path.

[0103] In one embodiment of this disclosure, the generation of the navigation path further includes: If the confidence level of at least one second reference trajectory in the subsequent road segment is less than or equal to the set confidence level, then based on the end state information of the first reference trajectory, the second reference trajectory with the highest confidence level among the at least one second reference trajectory in the subsequent road segment is corrected and smoothed, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

[0104] It should be noted that for details not disclosed in the data processing apparatus applied to a mobile device according to the embodiments of this disclosure, please refer to the details disclosed in the data processing method applied to a server according to the embodiments of this disclosure, which will not be repeated here.

[0105] According to the data processing apparatus for a mobile device according to embodiments of this disclosure, a sending module sends mobile trajectory data of the mobile device to a server. The mobile trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment. The server uses the mobile trajectory data to cluster the trajectory segments based on the road segment identifiers, obtaining multiple sets of trajectory segments. It then merges multiple trajectory segments from the same set of trajectory segments targeting the same road segment to generate a reference trajectory corresponding to the same target road segment. This reference trajectory is used for navigation or map updates. By sending trajectory segments carrying road segment identifiers to the server, the mobile device of this disclosure enables the server to cluster and merge multiple trajectory segments targeting the same road segment, generating a high-quality reference trajectory. This allows the mobile device to obtain accurate navigation paths without relying on high-precision maps, effectively improving navigation output quality and system robustness.

[0106] To implement the above embodiments, this disclosure also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the data processing method executed by the server or the steps of the data processing method executed by the mobile device.

[0107] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. For example, the electronic device 800 may be a vehicle, a server (cloud), etc.

[0108] Reference Figure 8 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0109] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0110] The memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of this data include instructions, messages, etc., for any application or method used to operate on the electronic device 800.

[0111] Power component 806 provides power to the various components of electronic device 800. Power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0112] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the multimedia component 808 further includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data.

[0113] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0114] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0115] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0116] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi (Wireless Fidelity), 4G (Fourth Generation), or 5G (Fifth Generation), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), Bluetooth, and other technologies.

[0117] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0118] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the data processing method applied to a server, or implements the steps of the data processing method applied to a mobile device.

[0119] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0120] To implement the above embodiments, this disclosure also proposes a computer program product having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the data processing method applied to a server, or the steps of the data processing method applied to a mobile device.

[0121] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0123] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0125] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0126] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A data processing method, characterized in that, include: Acquire movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; Based on the road segment identifier, the trajectory segments are clustered to obtain multiple trajectory segment sets; wherein, each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment; For any target road segment, multiple trajectory segments in the trajectory segment set corresponding to the target road segment are fused to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updates.

2. The method according to claim 1, characterized in that, For any target road segment, the process of fusing multiple trajectory segments from the trajectory segment set corresponding to the target road segment to generate a reference trajectory corresponding to the target road segment includes: For any target road segment, the weight corresponding to each trajectory segment is determined based on the positioning accuracy of each trajectory segment; Based on the weights corresponding to each trajectory segment, a weighted average is performed on multiple trajectory segments in the set of trajectory segments corresponding to the target road segment to obtain a reference trajectory corresponding to the target road segment.

3. The method according to claim 1, characterized in that, The method further includes: For any set of trajectory segments, multiple trajectory segments in the set of trajectory segments are filtered using preset filtering conditions; In response to the trajectory segments in the trajectory segment set satisfying the filtering conditions, the trajectory segments that satisfy the filtering conditions are retained in the trajectory segment set; In response to a trajectory segment in the trajectory segment set not meeting the filtering conditions, the trajectory segment that does not meet the filtering conditions is reconstructed, and the reconstructed trajectory segment replaces the trajectory segment that does not meet the preset filtering conditions and is then saved in the trajectory segment set.

4. The method according to claim 3, characterized in that, The process of reconstructing the trajectory of trajectory segments that do not meet the filtering conditions includes: For any trajectory segment that does not meet the filtering conditions, obtain the first trajectory segment corresponding to the preceding segment and the second trajectory segment corresponding to the following segment that is adjacent to the trajectory segment that does not meet the filtering conditions. Based on the first trajectory segment and the second trajectory segment, trajectory segments that do not meet the filtering conditions are smoothly reconstructed to obtain reconstructed trajectory segments.

5. The method according to claim 4, characterized in that, The first trajectory segment and the second trajectory segment have overlapping trajectories.

6. The method according to claim 3, characterized in that, The preset filtering conditions include at least one of the following: The length of the trajectory segment is greater than the preset minimum trajectory length; Within a preset continuous trajectory length, the positioning accuracy of the trajectory segment meets the preset positioning accuracy. The angle between the extension direction of the trajectory segment and the driving direction of the corresponding lane is less than a set angle.

7. The method according to claim 1, characterized in that, The acquisition of movement trajectory data from multiple mobile devices includes: For any of the aforementioned mobile devices, the historical movement trajectory of the mobile device is segmented according to the road segment division boundaries of each road segment to obtain the trajectory segments corresponding to each road segment. The movement trajectory data is obtained by associating each trajectory segment with its corresponding road segment identifier.

8. The method according to claim 7, characterized in that, The process of segmenting the historical movement trajectory of the mobile device according to the road segment boundaries to obtain trajectory segments corresponding to each road segment includes: For any target road segment, based on the boundary of the target road segment, extend a first predetermined length in the direction of the adjacent preceding road segment and extend a second predetermined length in the direction of the adjacent following road segment to obtain the target trajectory segment. The target trajectory segment is determined as the trajectory segment corresponding to the target road segment.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: In response to a navigation request, a first reference trajectory for the road segment where the target device initiating the navigation request is located and at least one second reference trajectory for subsequent road segments are determined; In response to the existence of a second reference trajectory with a confidence level greater than a set confidence level in at least one of the subsequent road segments, the first reference trajectory is concatenated with the second reference trajectory with a confidence level greater than the set confidence level to obtain a navigation path; The navigation path is used for navigation of the target device.

10. The method according to claim 9, characterized in that, The method further includes: In response to the fact that the confidence of at least one second reference trajectory of the subsequent road segment is less than or equal to the set confidence, based on the end state information of the first reference trajectory, the second reference trajectory with the highest confidence among the at least one second reference trajectory of the subsequent road segment is corrected and smoothed, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

11. A data processing method, characterized in that, Applied to mobile devices, including: Send the mobile device's movement trajectory data to the server, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; The mobile trajectory data is used by the server to cluster the trajectory segments according to the road segment identifier to obtain multiple trajectory segment sets, and to fuse multiple trajectory segments of the same target road segment in the same trajectory segment set to generate a reference trajectory corresponding to the same target road segment. The reference trajectory is used for navigation or map updates.

12. The method according to claim 11, characterized in that, The method further includes: Send a navigation request to the server; wherein, the navigation request is used by the server to determine the road segment where the mobile device initiating the navigation request is located; The system receives a navigation path sent by the server; wherein the generation of the navigation path includes the server using a first reference trajectory based on the road segment where the mobile device is located and at least one second reference trajectory for subsequent road segments, and in response to the existence of a second reference trajectory with a confidence level greater than a set confidence level in the at least one second reference trajectory for subsequent road segments, the system concatenates the first reference trajectory with the second reference trajectory with a confidence level greater than the set confidence level to obtain the navigation path; Navigation is performed based on the navigation path.

13. The method according to claim 12, characterized in that, The generation of the navigation path also includes: In response to the fact that the confidence level of at least one second reference trajectory of the subsequent road segment is less than or equal to the set confidence level, the second reference trajectory with the highest confidence level among the at least one second reference trajectory of the subsequent road segment is corrected and smoothed based on the end state information of the first reference trajectory, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

14. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire the movement trajectory data of multiple mobile devices, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; The first processing module is used to perform clustering processing on the trajectory segments according to the road segment identifier to obtain multiple trajectory segment sets; wherein, each trajectory segment set includes multiple trajectory segments corresponding to the same target road segment; The second processing module is used to perform fusion processing on multiple trajectory segments in the trajectory segment set corresponding to any target road segment to generate a reference trajectory corresponding to the target road segment, wherein the reference trajectory is used for navigation or map updates.

15. The apparatus according to claim 14, characterized in that, The second processing module includes: The determining unit is used to determine the weight of each trajectory segment based on the positioning accuracy of each trajectory segment for any target road segment; The processing unit is used to perform weighted average processing on multiple trajectory segments in the set of trajectory segments corresponding to the target road segment based on the weights corresponding to each trajectory segment, so as to obtain a reference trajectory corresponding to the target road segment.

16. The apparatus according to claim 14, characterized in that, The device further includes: The filtering module is used to filter multiple trajectory segments in any given set of trajectory segments using preset filtering conditions, wherein... In response to the trajectory segments in the trajectory segment set satisfying the filtering conditions, the trajectory segments that satisfy the filtering conditions are retained in the trajectory segment set; In response to a trajectory segment in the trajectory segment set not meeting the filtering conditions, the trajectory segment that does not meet the filtering conditions is reconstructed, and the reconstructed trajectory segment replaces the trajectory segment that does not meet the preset filtering conditions and is then saved in the trajectory segment set.

17. A data processing apparatus, characterized in that, Applied to mobile devices, including: The sending module is used to send the movement trajectory data of the mobile device to the server, wherein the movement trajectory data includes trajectory segments of multiple road segments and road segment identifiers corresponding to each road segment; The mobile trajectory data is used by the server to cluster the trajectory segments according to the road segment identifier to obtain multiple trajectory segment sets, and to fuse multiple trajectory segments of the same target road segment in the same trajectory segment set to generate a reference trajectory corresponding to the same target road segment. The reference trajectory is used for navigation or map updates.

18. The apparatus according to claim 17, characterized in that, The sending module is further configured to send a navigation request to the server; wherein the navigation request is used by the server to determine the road segment where the mobile device initiating the navigation request is located; The device further includes: A receiving module is configured to receive the navigation path sent by the server; wherein, the generation of the navigation path includes the server concatenating the first reference trajectory with the second reference trajectory with the confidence level greater than a set confidence level among the at least one second reference trajectory of the subsequent road segment to obtain the navigation path; A navigation module is used to navigate based on the navigation path.

19. The apparatus according to claim 18, characterized in that, The generation of the navigation path also includes: In response to the fact that the confidence level of at least one second reference trajectory of the subsequent road segment is less than or equal to the set confidence level, the second reference trajectory with the highest confidence level among the at least one second reference trajectory of the subsequent road segment is corrected and smoothed based on the end state information of the first reference trajectory, and the corrected and smoothed second reference trajectory is spliced ​​with the first reference trajectory to obtain the navigation path.

20. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the method as claimed in any one of claims 1-10, or implements the steps of the method as claimed in any one of claims 11-13.

21. A non-transitory computer-readable storage medium storing computer program instructions thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method according to any one of claims 1-10, or the steps of the method according to any one of claims 11-13.

Images