Track processing method and device, equipment, medium and product

Abstract

The invention provides a track processing method, device and equipment, a medium and a product, and relates to the technical field of computers, in particular to the technical fields of intelligent transportation, map navigation and the like. According to the specific implementation scheme, the method comprises the steps of determining an initial confidence coefficient of a to-be-processed track according to first projection information of the to-be-processed track on each candidate road in a candidate road group; and according to the initial confidence coefficient, the road confidence coefficient of the candidate road group and the equipment confidence coefficient of the source equipment of the to-be-processed track, determining the target confidence coefficient of the to-be-processed track. According to the scheme, the target confidence degree of the to-be-processed track is comprehensively determined from the three aspects of the track, the road and the equipment, the evaluation comprehensiveness and integrity of the target confidence degree are effectively guaranteed, and therefore the average accuracy and reliability of the track confidence degree are guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the technical fields of intelligent transportation, map navigation, etc., and specifically to trajectory processing methods, devices, equipment, media and products. Background Art

[0002] The core idea of ​​map matching technology is to match the vehicle positioning trajectory obtained by the positioning device with the road information in the electronic map database to determine the vehicle's relative position to the map. In practical applications, map matching technology provides important data support for intelligent transportation, navigation, data engine intelligence and other fields. The reliability of vehicle positioning trajectory is an important foundation of map matching technology. Summary of the invention

[0003] The present disclosure provides a trajectory processing method, device, equipment, medium and product, which can accurately evaluate the target confidence of the trajectory.

[0004] According to a first aspect of the present disclosure, a trajectory processing method is provided, comprising:

[0005] Determining an initial confidence level of the trajectory to be processed according to first projection information of the trajectory to be processed on each candidate road in the candidate road group;

[0006] The target confidence of the trajectory to be processed is determined according to the initial confidence, the road confidence of the candidate road group, and the device confidence of the source device of the trajectory to be processed.

[0007] According to a second aspect of the present disclosure, a trajectory processing device is provided, comprising an accident confidence determination module and a target confidence determination module, wherein:

[0008] The initial confidence determination module is configured to determine the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group;

[0009] The target confidence determination module is configured to determine the target confidence of the trajectory to be processed according to the initial confidence, the road confidence of the candidate road group and the device confidence of the source device of the trajectory to be processed.

[0010] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0011] at least one processor; and

[0012] a memory communicatively connected to at least one processor; wherein,

[0013] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable the at least one processor to execute the method provided by the first aspect.

[0014] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method provided in the first aspect.

[0015] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method provided according to the first aspect when executed by a processor.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0018] Figure 1 is an exemplary system architecture diagram to which the trajectory processing method of the present disclosure may be applied;

[0019] Figure 2 is a schematic diagram of a first embodiment of a trajectory processing method according to the present disclosure;

[0020] Figure 3 is a schematic diagram of the projection relationship between the trajectory points and the road according to the present disclosure;

[0021] Figure 4 is a schematic diagram of a scenario in which one trajectory point corresponds to two candidate roads according to the present disclosure;

[0022] Figure 5 is a schematic diagram of distribution scenarios of historical trajectory points in multiple candidate roads according to the present disclosure;

[0023] Figure 6 is a schematic diagram of a second embodiment of a trajectory processing method according to the present disclosure;

[0024] Figure 7 is a schematic diagram of an embodiment of a trajectory processing device according to the present disclosure;

[0025] Figure 8 It is a block diagram of an electronic device used to implement the trajectory processing method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0027] In the field of intelligent transportation, map matching data can be used to monitor the operation status of road traffic, publish traffic information in real time, and help vehicles achieve intelligent scheduling. In navigation applications, map matching technology can assist route mining and road binding functions to improve navigation accuracy and reliability. At the same time, data engine intelligence can also use map matching data to discover new roads, monitor traffic restrictions and other information, and provide strong support for traffic management and planning.

[0028] However, in practical applications, map matching technology also faces some challenges and problems. Among them, poor trajectory quality and complex road networks are relatively common problems. Especially in parallel road areas, due to the close spacing between multiple roads, positioning trajectory points are easily confused, resulting in a decrease in the quality of map matching data. This kind of low-quality data will interfere with the application and affect the use effect.

[0029] The present disclosure provides a trajectory processing method, which evaluates the confidence of each candidate road and source device of the trajectory in the road network, and combines the initial confidence of the trajectory on the candidate road to comprehensively determine the target confidence of the trajectory, which can evaluate the trajectory confidence in real time, effectively and accurately, thereby accurately evaluating the map matching quality and providing quality information to the downstream. After the method evaluates the confidence of the road and the device respectively, it matches and compares the positioning trajectory point and at least one candidate road, thereby evaluating the quality of the map matching data. For low-quality data, the method can filter and eliminate it in real time to avoid interference with the application of trajectory data.

[0030] It should be pointed out that in the technical solution of the present disclosure, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the trajectory processing method or apparatus of the present disclosure may be applied.

[0032] like Figure 1As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The network 102 is used to provide a communication link between the terminal device 101 and the server 103, and may include various connection types, such as a wired communication link, a wireless communication link or an optical fiber cable.

[0033] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send information, etc. Various client applications, such as map navigation applications, can be installed on the terminal device 101.

[0034] The terminal device 101 can be hardware or software. When the terminal device 101 is hardware, it can be various electronic devices, including but not limited to independent electronic devices such as smart phones, tablet computers, laptop computers, desktop computers, and handheld navigation devices, and can also include vehicle-mounted terminals. When the terminal device 101 is software, it can be installed in the above electronic devices. It can be implemented as multiple software or software modules, or as a single software or software module. No specific limitation is made here.

[0035] The server 103 may be hardware or software. When the server 103 is hardware, it may be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server 103 is software, it may be implemented as multiple software or software modules (for example, for providing distributed services), or as a single software or software module. No specific limitation is made here.

[0036] The trajectory processing method provided in the embodiment of the present disclosure is generally executed by a terminal device 101 (such as a vehicle-mounted terminal or a mobile terminal) or a server 103 , and accordingly, a trajectory processing device is generally disposed in the terminal device 101 or the server 103 .

[0037] It should be noted that Figure 1 The number of terminal devices 101, networks 102 and servers 103 in the embodiment is only for illustration. Any number of terminal devices 101, networks 102 and servers 103 may be provided according to implementation requirements.

[0038] Figure 2 A process 200 of an embodiment of a trajectory processing method according to the present disclosure is shown, referring to Figure 2 As shown, the trajectory processing method includes the following steps:

[0039] Step S201, determining the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group.

[0040] In the embodiment of the present disclosure, the execution subject of the trajectory processing method, for example Figure 1 The terminal device 101 or the server 103 shown determines the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on its candidate road.

[0041] The trajectory to be processed may include a trajectory that is being recorded and generated in real time during the movement of the vehicle or user (an unfinished trajectory), or may include an existing trajectory that has been generated and stored (a generated complete trajectory).

[0042] The execution subject first determines the candidate road that the trajectory point may correspond to in the actual environment based on the trajectory point in the trajectory to be processed, that is, on which road in the actual environment the trajectory may be generated and recorded. Each trajectory point in the trajectory to be processed has its corresponding candidate road, and two different trajectory points in the same trajectory to be processed may correspond to different candidate roads, such as two trajectory points before and after a fork in the road.

[0043] For example, there may be only one candidate road corresponding to a trajectory point in the trajectory to be processed, for example, in an open single road environment.

[0044] In some optional implementations, a track point in the to-be-processed track may correspond to more than one candidate road, for example, there may be two or more candidate roads. In particular, in parallel road areas or areas with severe environmental occlusion, such as overpasses or tunnels where different roads overlap on the map, it is difficult to determine on which road a single track point was generated, resulting in at least two corresponding candidate roads for a track point (for example, an overpass upper road and an overpass lower road).

[0045] Among them, the judgment rules of parallel roads can be preset. For example, for two roads whose road angle (such as the angle between the driving direction and the road centerline) is less than or equal to a preset threshold (such as 30 degrees or 15 degrees), the execution subject can determine that the roads are parallel to each other. For an environment where there are two or more parallel roads, the execution subject can determine that it is a parallel road area.

[0046] It should be pointed out that in this solution, in the process of determining the candidate roads of the track point on the to-be-processed track, the execution subject only needs to distinguish whether the driving direction of the candidate road is consistent with the driving direction indicated by the track point on the to-be-processed track and whether the track point belongs to the road range, without considering which lane the track point belongs to in the road. Therefore, in the process of determining the candidate roads, the execution subject can refer to the road-level map data without referring to the lane-level high-precision map data; it can also use the road-level data in the lane-level high-precision map data.

[0047] In some optional implementations, the execution subject first determines each candidate road in the candidate road group corresponding to the trajectory point of the trajectory to be processed, and then determines the first projection relationship of the trajectory point on each candidate road, for example, the projection distance and projection direction of the trajectory point to the center line of each candidate road, or the projection distance of the trajectory point to a side of each candidate road, etc. Then, the initial confidence of the trajectory to be processed at each trajectory point is determined according to the first projection information of the trajectory point of the trajectory to be processed on each candidate road.

[0048] In this solution, the initial confidence can represent the probability value of the trajectory point of the to-be-processed trajectory on each corresponding candidate road.

[0049] In some optional implementations of the embodiments of the present disclosure, the initial confidence of the trajectory to be processed is determined according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group, including: determining the candidate road of the trajectory to be processed according to the driving direction of the trajectory to be processed; determining the first projection distance from the trajectory point of the trajectory to be processed to the center line of the candidate road; and determining the initial confidence of the trajectory to be processed according to the first projection distance and the road width of the candidate road.

[0050] The above-mentioned execution subject can determine the candidate roads corresponding to the trajectory points of the trajectory to be processed according to the driving direction of the trajectory to be processed and the driving directions of each road, and form a candidate road group of the trajectory to be processed, thereby determining each candidate road corresponding to the trajectory to be processed; and then determine the first projection distance from the trajectory point of the trajectory to be processed to the road centerline (i.e., center line) of the candidate road.

[0051] refer to Figure 3 As shown, the road to which the road centerline 301 belongs is one of the candidate roads of the trajectory point 302 , and its first projection distance is the vertical distance from the trajectory point 302 to the road centerline 301 .

[0052] Exemplarily, in the present embodiment, the first projection distance can be calculated based on the electronic map data. For example, based on the map coordinates of the trajectory point 302 in the electronic map and the map coordinates of the road centerline 301 in the electronic map, the vertical distance from the trajectory point 302 to the road centerline 301 is calculated as its first projection distance.

[0053] In the case that there is only one candidate road corresponding to the trajectory point of the trajectory to be processed, the above-mentioned execution entity determines the first projection distance of the trajectory point of the trajectory to be processed on the corresponding candidate road, and then determines the initial confidence of the trajectory to be processed at the trajectory point according to the first projection distance and the road width of the candidate road.

[0054] In the present implementation, the execution subject first determines the corresponding candidate road according to the driving direction of the trajectory to be processed, and determines the initial confidence of the trajectory to be processed at the trajectory point according to the first projection distance from the trajectory point of the trajectory to be processed to the road centerline of the candidate road, combined with the road width of the candidate road, so as to first ensure the accuracy of the candidate road, and then ensure the accuracy of the projection distance from the trajectory point of the trajectory to be processed to the candidate road, thereby ensuring the accuracy and reliability of the initial confidence of the trajectory to be processed at the trajectory point.

[0055] In some optional implementations, the execution subject may determine the initial confidence of the trajectory to be processed at the trajectory point according to the ratio of the first projection distance to 1 / 2 of the road width of the candidate road. For example, in the case of only one candidate road, the execution subject may use the vertical distance from the trajectory point to the center line of the candidate road as its first projection distance, and then determine the confidence of the trajectory point according to the ratio of the first projection distance to the median of the road width of the candidate road (e.g., the vertical distance between the road edge on one side of the candidate road where the trajectory point is located and its road center line), and use it as the initial confidence of the trajectory to be processed at the trajectory point.

[0056] In some optional implementations, the execution entity may determine the confidence of the trajectory point on each candidate road according to the ratio of the first projection distance to the road width of the candidate road as the initial confidence of the trajectory to be processed at the trajectory point.

[0057] In some optional implementations of the embodiments of the present disclosure, the initial confidence of the trajectory to be processed is determined based on the first projection distance and the road width of the candidate road, including: in response to the existence of multiple candidate roads, the initial confidence of the trajectory to be processed is determined based on the sum of multiple first projection distances and the widths of the multiple candidate roads.

[0058] In the case where a certain trajectory point of the trajectory to be processed corresponds to multiple candidate roads, for example, in a parallel road scene with a tunnel or an overpass, the above-mentioned execution entity determines the first projection distance from the trajectory point to the center line of each candidate road after determining that the trajectory point corresponds to at least two candidate roads, and then determines the initial confidence of the trajectory to be processed at the trajectory point based on the multiple first projection distances combined with the sum of the widths of the multiple candidate roads.

[0059] Taking the case of two candidate roads as an example, the execution subject can determine the initial confidence of the pending track at the track point based on the ratio of the difference between the two first projection distances and the sum of the widths of the two candidate roads (or the sum of 1 / 2 widths). If the difference between the two first projection distances is larger, it means that the distinction between the two candidate tracks at the track point is more obvious, and the judgment that the track point belongs to a candidate track is more accurate, that is, the initial confidence of the pending track at this track point is higher; on the contrary, if the difference between the two first projection distances is smaller, it means that the distinction between the two candidate tracks at the track point is less obvious, that is, it is more difficult to distinguish which candidate road the track point belongs to, so the initial confidence of the pending track at this point is lower.

[0060] Figure 4 FIG. 4 is an exemplary schematic diagram showing a scenario 400 in which a trajectory point corresponds to two candidate roads. Figure 4 As shown, the road Link a and Link b There are two candidate roads corresponding to the track point GPS. We can first calculate the track point GPS to Link a and Link b The first projection distance L a 、Track point GPS to Link b The first projection distance L b , and then calculate the projection distance difference between the trajectory point and the two candidate roads L = abs(L a -L b ), assuming Link a The road width is S 1 , Link b The road width is S 2 , then the initial confidence of the trajectory to be processed at the trajectory point GPS can be expressed as R g = abs(L a -L b ) / (S 1 / 2+S 2 / 2).

[0061] In this solution, the execution entity uses the first projection distance of the trajectory point on multiple candidate roads and the road widths of the multiple candidate roads to determine the initial confidence of the trajectory to be processed at the trajectory point, and comprehensively considers the relative distances between the multiple candidate roads and the projection distances of the trajectory point on each candidate road, effectively ensuring the accuracy and reliability of the initial confidence.

[0062] Step S202 , determining a target confidence of the trajectory to be processed according to the initial confidence, the road confidence of the candidate road group, and the device confidence of the source device of the trajectory to be processed.

[0063] In the embodiment of the present disclosure, the execution subject of the trajectory processing method, for example Figure 1 The terminal device 101 or the server 103 shown determines the target confidence of the trajectory to be processed based on the initial confidence determined in step S201, combined with the road confidence of its candidate roads and the device confidence of the source device of the trajectory to be processed.

[0064] Exemplarily, the above-mentioned execution entity can determine the final confidence of the trajectory to be processed according to a preset algorithm or a pre-trained neural network model, according to the initial confidence of the trajectory to be processed on the candidate road, the road confidence of the candidate road and the device confidence of the source device of the trajectory to be processed.

[0065] The road confidence of the candidate road and the device confidence of the source device of the trajectory to be processed may be pre-processed data, or may be obtained by obtaining related data in real time according to the trajectory to be processed.

[0066] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined by the following steps: determining the road confidence of the candidate road group based on the second projection information of the historical trajectory points on each candidate road in the candidate road group.

[0067] In this solution, after the above-mentioned execution subject determines the candidate road group composed of the candidate roads corresponding to each trajectory point of the trajectory to be processed, for each candidate road in the candidate road group, the historical trajectory data of the candidate road can be obtained, and the road confidence of the candidate road group can be determined based on the obtained historical trajectory data.

[0068] For example, in the case where there is only one candidate road in the candidate road group, the execution entity may obtain all historical trajectory points of the candidate road within a preset period, and determine the road confidence of the candidate road, that is, the road confidence of the candidate road group, based on the second projection information of each historical trajectory point on the candidate road. For example, the deviation value between the second projection information of each historical trajectory point on the candidate road and its road centerline is first calculated, and then each deviation value is statistically processed to determine the road confidence of the candidate road group.

[0069] In this scheme, the execution entity determines the road confidence of the candidate road group based on the second projection information of the historical trajectory points on each candidate road in the candidate road group. The use of historical trajectory data can effectively ensure the accuracy and reliability of the road confidence, and provide a stable data basis for the subsequent determination of the target confidence of the trajectory to be processed.

[0070] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined based on the second projection information of the historical trajectory points on each candidate road in the candidate road group, including: determining the second projection distance from the historical trajectory points to the center line of the candidate road; and determining the road confidence of the candidate road group based on the second projection distance and the road width of the candidate road.

[0071] The execution subject determines the second projection distance from each historical trajectory point to the road centerline of the candidate road based on the acquired historical trajectory data, and then calculates the road confidence of the candidate road group according to the second projection distance and the road width of the candidate road.

[0072] In this implementation, the above-mentioned execution entity determines the road confidence of the candidate road group based on the second projection distance from each historical trajectory point to the center line of the candidate road, combined with the road width of the candidate road, and uses the reliability of the historical trajectory points to effectively ensure the accuracy and reliability of the road confidence.

[0073] In some optional implementations, the execution entity may determine the confidence of the historical trajectory point on the candidate road based on the ratio of the second projection distance of the historical trajectory point on the candidate road to 1 / 2 of the road width of the candidate road. For example, in the case of only one candidate road, the execution entity may use the vertical distance from the historical trajectory point to the center line of the candidate road as its second projection distance, and then determine the confidence of the historical trajectory point on the candidate road based on the ratio of the second projection distance to the median of the road width of the candidate road (e.g., the vertical distance between the road edge on one side of the candidate road where the trajectory point is located and its road center line). Then, the confidence of all historical trajectory points on the candidate road is averaged to obtain the average confidence of all historical trajectory points on the candidate road as the road confidence of the candidate road, that is, the road confidence of the candidate road group.

[0074] In some optional implementations, the above-mentioned execution entity can determine the confidence of the historical trajectory point based on the ratio of the difference in the second projection distances of the same historical trajectory point on different candidate roads to the sum of the road widths of the different candidate roads, and further determine the road confidence of the candidate road group based on the confidence of all historical trajectory points.

[0075] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined based on the second projection distance and the road width of the candidate roads, including: in response to the existence of multiple candidate roads, the road confidence of the candidate road group is determined based on the sum of multiple second projection distances and the widths of the multiple candidate roads.

[0076] In the case where there are multiple candidate roads in the candidate road group corresponding to a certain trajectory point of the trajectory to be processed, for example, in a parallel road scene with a tunnel or an overpass, the above-mentioned execution entity determines the second projection distance from each historical trajectory point to the road centerline of each candidate road after determining that the trajectory point corresponds to at least two candidate roads, and then determines the confidence of the historical trajectory point based on the multiple second projection distances combined with the sum of the widths of the multiple candidate roads, and further determines the road confidence of the candidate road group based on this.

[0077] Taking the case where there are two candidate roads in the candidate road group Link as an example, the execution subject can determine the confidence of the historical trajectory point based on the ratio of the difference between the two second projection distances and the sum of the widths of the two candidate roads (or the sum of 1 / 2 widths). If the difference between the two second projection distances is larger, it means that the distinction between the two candidate trajectories at the historical trajectory point is more obvious, and the judgment that the historical trajectory point belongs to a certain candidate trajectory is more accurate, that is, the confidence of the historical trajectory point is higher; on the contrary, if the difference between the two second projection distances is smaller, it means that the distinction between the two candidate trajectories at the historical trajectory point is less obvious, that is, it is more difficult to distinguish which candidate road the historical trajectory point belongs to, so the confidence of the historical trajectory point is lower.

[0078] Reference Figure 5 As shown, if the candidate road group Link for a certain trajectory point in the trajectory to be processed includes candidate road A and candidate road B, for example, candidate road A is the main road (or the upper road of the elevated road) and candidate road B is the secondary road (or the lower road of the elevated road). The historical trajectory points on the candidate road A and the candidate road B are obtained, including at least g1 to g5. Taking the historical trajectory point g1 as an example, the second projection distance L from the historical trajectory point g1 to the road center line of the candidate road A is calculated respectively. 1A , the second projection distance L from the historical trajectory point g1 to the center line of the candidate road A 1B , and then calculate the projection distance difference L from the historical trajectory point to the two candidate roads 1 = abs(L 1A -L 1B ); Assume that the width of candidate road A is S A , the road width of candidate road B is S B , then the confidence of the historical trajectory point g1 can be expressed as R g1 = abs(L 1A -L 1B ) / (S A / 2+S B / 2). Similarly, the confidence R of historical trajectory points g2~g5 can be calculated respectively g2 ~R g5 Based on this, the confidence of the candidate road group Link can be calculated as R L=(R g1 +R g2 +R g3 +R g4 +R g5 ) / 5.

[0079] In this solution, the execution entity uses the second projection distance from the historical trajectory point on each candidate road in the candidate road group to each candidate road to comprehensively calculate the distribution of all historical trajectory points in the candidate road group, and combines it with the sum of the road widths of each candidate road in the candidate road group to determine the road confidence of the candidate road group. According to the distribution of historical trajectory data and the width distribution of multiple candidate roads, the accuracy and reliability of the road confidence are effectively guaranteed.

[0080] In some optional implementations of the embodiments of the present disclosure, the device confidence of the source device of the trajectory to be processed is determined based on the following steps: determining the device confidence of the source device according to the device configuration information of the source device of the trajectory to be processed.

[0081] Considering that different devices generate and record trajectory points, the confidence of the trajectory points is also different. For example, for mobile terminals such as smartphones, the accuracy of the trajectory recorded by different brands of mobile phones is different, and even the same brand of mobile phones has different software configurations or hardware configurations, and the accuracy of the trajectory recorded is also different. For another example, the trajectory data recorded and generated by different brands of cars during driving is also different, and the reason is also due to the differences in the on-board terminals or on-board positioning devices on the cars.

[0082] In the present disclosure, the confidence of the source device of the trajectory to be processed is fully considered to improve the accuracy and reliability of the determined trajectory confidence.

[0083] In this implementation, the execution subject determines the device confidence of the source device according to the device configuration information of the source device of the trajectory to be processed.

[0084] To ensure the reliability of device confidence, in this solution, the device confidence of a source device is determined not only based on the historical data of the source device, but also based on the historical data recorded by other devices having the same configuration information as the source device.

[0085] The device configuration information of the source device may include, but is not limited to: device type, device brand, device model, device built-in hardware information, device built-in software information (such as software version, etc.), etc.

[0086] For example, taking a smartphone as the source device of the trajectory to be processed, the device confidence of the smartphone can be determined based on the historical trajectory data recorded by smartphones of the same brand; the device confidence of the smartphone can also be determined based on the historical trajectory data recorded by smartphones of the same brand and model; the device configuration information of the smartphone can also be comprehensively determined based on the historical trajectory data recorded by all positioning devices (including mobile phones of the same brand or other brands, vehicle-mounted positioning devices, etc.) with the same positioning-related hardware configuration, the same software type and version as the smartphone application.

[0087] In this scheme, the above-mentioned execution entity determines the device confidence of the source device based on the device configuration information of the source device of the trajectory to be processed, which can greatly expand the data basis of the device confidence and ensure the correlation between its data basis and the source device, thereby ensuring the accuracy and reliability of the device confidence of the source device.

[0088] In some optional implementations of the embodiments of the present disclosure, the device confidence of the source device is determined based on the device configuration information of the source device of the trajectory to be processed, including: determining, based on the device configuration information of the source device of the trajectory to be processed, a reference trajectory point corresponding to a reference lane from historical trajectories having the device configuration information; and determining the device confidence of the source device based on third projection information of the reference trajectory point onto the reference lane.

[0089] In this solution, the execution subject searches for historical tracks with the same device configuration information according to the device configuration information of the source device of the device to be processed, and determines the reference track point corresponding to the reference lane from the historical tracks found.

[0090] It should be pointed out that for the evaluation of device confidence, the present disclosure uses high-precision lane-level map data to ensure the matching of the reference trajectory points used and the reference lanes, thereby ensuring the accuracy and reliability of the device confidence.

[0091] Exemplarily, the execution entity further screens reference trajectory points that meet the requirements from the found historical trajectory data according to preset reference lane screening conditions to ensure the average accuracy of the reference trajectory points and reference lanes for device confidence.

[0092] In this implementation, the above-mentioned execution entity first searches for the historical trajectory according to the device configuration information, and then searches for the corresponding reference trajectory point from the historical trajectory according to the reference trajectory, and further determines the device confidence of the source device based on the third projection information of the reference trajectory point on the reference lane. This can effectively ensure that the reference trajectory point has the same device configuration information as the trajectory to be processed and meets the environmental requirements of the reference lane, and combined with its projection information on the reference lane, effectively ensure the accuracy and reliability of the evaluation of the device configuration information.

[0093] In some optional implementations of the embodiments of the present disclosure, the historical trajectory with device configuration information includes at least one of the following: the historical trajectory of the source device of the trajectory to be processed, and the historical trajectory of a reference device with the same device configuration information as the source device.

[0094] In this implementation, the selection of historical trajectories by the execution subject may include both historical trajectory data recorded and generated by the source device of the trajectory to be processed itself and historical trajectory data recorded and generated by a reference device having the same device configuration information as the source device. In other words, this solution may use the historical trajectory of the source device to determine the device confidence of the source device, or may use the historical trajectory of a reference device having the same device configuration information to assist in determining the device confidence of the source device.

[0095] This solution can effectively avoid inaccurate device confidence caused by the small amount of historical trajectory data of the source device. By using the historical trajectory of the reference device with the same device configuration information, the data basis of the device confidence can be effectively expanded, thereby ensuring the accuracy and reliability of the device confidence.

[0096] In some optional implementations of the disclosed embodiments, the reference lane does not have a lane in the same direction on the road to which it belongs. That is, in this solution, there is only one pass path upstream and downstream of the reference lane, and the reference trajectory point corresponding to the reference lane can only be bound to the reference lane, and there are no other possible candidate lanes.

[0097] This solution uses historical trajectory data in a one-way single-lane environment as reference trajectory points to evaluate the equipment confidence, which can effectively avoid the inaccurate correspondence between trajectory points and lanes, improve the accuracy of the equipment confidence assessment, and ensure the reliability of the obtained equipment confidence.

[0098] In some optional implementations of the embodiments of the present disclosure, the device confidence of the source device is determined based on the third projection information from the reference trajectory point to the reference lane, including: determining the third projection distance from the reference trajectory point to the reference lane; determining the device confidence of the source device based on the third projection distance and the median width of the reference lane.

[0099] In this solution, the reference lane corresponding to the reference trajectory point is unique. Therefore, the confidence of the reference trajectory point can be determined based on the third projection distance of the reference trajectory point on the reference lane combined with the lane width or median width of the reference lane, and the device confidence of the source device corresponding to the reference trajectory point can be further determined based on the confidence of all reference trajectory points on the reference lane.

[0100] Exemplarily, for any reference trajectory point, after the above-mentioned execution entity calculates the third projection distance of the reference trajectory point on the corresponding reference lane, the ratio of the third projection distance to the lane median of the reference lane (1 / 2 of the lane width) is used as the confidence of the reference trajectory point on the reference lane.

[0101] In some optional implementations, the execution entity may take an average value based on the confidence levels of all reference trajectory points on a reference lane as the device confidence level of the source device.

[0102] In some optional implementations, the execution entity may obtain reference trajectory points corresponding to multiple reference lanes, and calculate the confidence of each reference trajectory point on each reference lane respectively; then, for a reference lane, calculate the average confidence of all reference trajectory points on it as the confidence of the source device on the reference lane; and then calculate the average confidence of multiple reference devices as the device confidence of the source device.

[0103] In this scheme, the execution entity can effectively evaluate the confidence of the reference trajectory point on the reference lane, that is, the positioning confidence of the reference trajectory point, based on the reference trajectory point and its unique corresponding reference lane, according to the third projection distance of the reference trajectory point on its corresponding reference lane and the median width of the reference lane, so as to further evaluate the device confidence of the source device of the reference trajectory point, and effectively ensure the accuracy and reliability of the device confidence.

[0104] According to the trajectory processing method provided by the embodiment of the present disclosure, the execution body first determines the initial confidence of the trajectory to be processed based on the first projection information of the trajectory to be processed on each candidate road in the candidate road group, and then combines the road confidence of the candidate road group and the device confidence of the source device of the trajectory to be processed to comprehensively determine the target confidence of the trajectory to be processed from three aspects: trajectory, road, and device, effectively ensuring the comprehensiveness and completeness of the evaluation of the target confidence, thereby ensuring the average accuracy and reliability of the trajectory confidence.

[0105] Figure 6 FIG. 6 shows a process 600 of a second embodiment of a trajectory processing method according to the present disclosure, referring to FIG. Figure 6As shown, the trajectory processing method includes the following steps:

[0106] Step S601, determining the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group.

[0107] Step S601 and Figure 2 Step S201 of the illustrated embodiment is basically the same, and the specific implementation method can refer to the above description of step S201, which will not be repeated here.

[0108] Step S602 , determining a target confidence of the trajectory to be processed according to the initial confidence, the road confidence of the candidate road group, and the device confidence of the source device of the trajectory to be processed.

[0109] Step S602 and Figure 2 Step S201 of the illustrated embodiment is basically the same, and the specific implementation method can refer to the above description of step S202, which will not be repeated here.

[0110] Step S303, in response to the target confidence being greater than or equal to a preset confidence threshold, performing at least one of the following according to the trajectory to be processed: determining road traffic information; determining a candidate planned trajectory; and determining a vehicle control strategy.

[0111] After the confidence of the trajectory to be processed is evaluated by this scheme, the trajectory can be filtered and screened according to its target confidence to select the trajectory with high enough target confidence, which can be used to assist modeling, model training, data calculation, etc.

[0112] Exemplarily, the trajectories filtered out according to the target confidence can be used in the field of intelligent transportation to monitor the operation status of road traffic and realize intelligent vehicle scheduling, for example, to assist in determining road traffic information; it can also be used to assist route mining, navigation, etc., for example, to determine candidate planned trajectories or planned routes; it can also be used to assist autonomous driving, for example, to determine vehicle control strategies, etc.

[0113] According to the trajectory processing method provided by the embodiment of the present disclosure, after a comprehensive evaluation is performed on the trajectory to be processed based on the initial confidence of the trajectory to be processed, the road confidence of the candidate road group, and the device confidence of the source device to obtain its target confidence, the trajectory can be screened according to the target confidence, and low-quality trajectory data can be filtered and eliminated to avoid interference with the trajectory application, effectively improving the application effect of the trajectory data, such as improving the reliability of the traffic information, candidate planning trajectories, vehicle control strategies, etc. obtained therefrom.

[0114] It should be pointed out that in the technical solution of the present invention, the acquisition, storage and application of user personal information (such as vehicle user information, driving trajectory, speed, location, etc.) are all carried out with the user's knowledge and explicit authorization, and are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0115] As an implementation of the trajectory processing method shown in the above figures, Figure 7 FIG. 700 is a diagram showing an embodiment of a trajectory processing device according to the present disclosure. Figure 2 Corresponding to the method embodiment shown, the device can be applied to various electronic devices.

[0116] Reference Figure 7 As shown, the trajectory processing device 700 provided by the embodiment of the present disclosure includes: an initial confidence determination module 701 and a target confidence determination module 702. The initial confidence determination module 701 is configured to determine the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group; the target confidence determination module 702 is configured to determine the target confidence of the trajectory to be processed according to the initial confidence, the road confidence of the candidate road group and the device confidence of the source device of the trajectory to be processed.

[0117] In the trajectory processing device 700 of the embodiment of the present disclosure, the specific processing of the initial confidence length determination module 701 and the target confidence determination module 702 and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of steps S201 - S202 in the corresponding embodiment are not repeated here.

[0118] In some optional implementations of the embodiments of the present disclosure, the initial confidence determination module is configured to: determine the candidate roads of the trajectory to be processed according to the driving direction of the trajectory to be processed; determine the first projection distance from the trajectory point of the trajectory to be processed to the centerline of the candidate road; determine the initial confidence of the trajectory to be processed according to the first projection distance and the road width of the candidate road.

[0119] In some optional implementations of the embodiments of the present disclosure, an initial confidence of the trajectory to be processed on the candidate road is determined based on the first projection distance and the road width of the candidate road, including: in response to the existence of multiple candidate roads, determining the initial confidence of the trajectory to be processed based on the sum of multiple first projection distances and the widths of the multiple candidate roads.

[0120] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined by the following steps: determining the road confidence of the candidate road group based on the second projection information of the historical trajectory points on each candidate road in the candidate road group.

[0121] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined based on the second projection information of the historical trajectory points on each candidate road in the candidate road group, including: determining the second projection distance from the historical trajectory point to the road centerline of the candidate road; and determining the road confidence of the candidate road group based on the second projection distance and the road width of the candidate road.

[0122] In some optional implementations of the embodiments of the present disclosure, the road confidence of the candidate road group is determined based on the second projection distance and the road width of the candidate roads, including: in response to the existence of multiple candidate roads, the road confidence of the candidate road group is determined based on the sum of multiple second projection distances and the widths of the multiple candidate roads.

[0123] In some optional implementations of the embodiments of the present disclosure, the device confidence of the source device of the trajectory to be processed is determined based on the following steps: determining the device confidence of the source device according to the device configuration information of the source device of the trajectory to be processed.

[0124] In some optional implementations of the embodiments of the present disclosure, the device configuration information of the source device is determined based on the device configuration information of the source device of the trajectory to be processed, including: determining, based on the device configuration information of the source device of the trajectory to be processed, a reference trajectory point corresponding to a reference lane from historical trajectories with the device configuration information; and determining the device confidence of the source device based on third projection information of the reference trajectory point onto the reference lane.

[0125] In some optional implementations of the embodiments of the present disclosure, the historical track with device configuration information includes at least one of the following: a historical track of a source device, and a historical track of a reference device with the same device configuration information as the source device.

[0126] In some optional implementations of the embodiments of the present disclosure, the reference lane does not have a lane in the same direction in the corresponding road.

[0127] In some optional implementations of the embodiments of the present disclosure, the device confidence of the source device is determined based on the third projection information from the reference trajectory point to the reference lane, including: determining the third projection distance from the reference trajectory point to the reference lane; determining the device confidence of the source device based on the third projection distance and the median width of the reference lane.

[0128] In some optional implementations of the embodiments of the present disclosure, the trajectory processing device also includes: a trajectory application module, which is configured to, in response to the target confidence being greater than or equal to a preset confidence threshold, perform at least one of the following according to the trajectory to be processed: determining road traffic information; determining candidate planning trajectories; determining a vehicle control strategy.

[0129] In the trajectory processing device of the embodiment of the present disclosure, the specific processing of the trajectory application module and the technical effects brought about by it can be referred to Figure 6 The relevant description of step S603 in the corresponding embodiment will not be repeated here.

[0130] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0131] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0132] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0133] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the trajectory processing method. For example, in some embodiments, the trajectory processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the trajectory processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the trajectory processing method in any other appropriate manner (e.g., by means of firmware).

[0135] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0137] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0140] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0142] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A trajectory processing method, comprising: Determining an initial confidence level of the trajectory to be processed according to first projection information of the trajectory to be processed on each candidate road in the candidate road group; A target confidence of the trajectory to be processed is determined according to the initial confidence, the road confidence of the candidate road group, and the device confidence of the source device of the trajectory to be processed.

2. The method according to claim 1, wherein: The step of determining the initial confidence of the trajectory to be processed according to the first projection information of the trajectory to be processed on each candidate road in the candidate road group includes: Determining candidate roads for the trajectory to be processed according to the driving direction of the trajectory to be processed; Determine a first projection distance from the trajectory point of the to-be-processed trajectory to the road centerline of the candidate road; An initial confidence level of the trajectory to be processed is determined according to the first projection distance and a road width of the candidate road.

3. The method according to claim 2, wherein: The determining, according to the first projection distance and the road width of the candidate road, the initial confidence of the trajectory to be processed includes: In response to the existence of a plurality of candidate roads, an initial confidence of the to-be-processed trajectory is determined according to a sum of a plurality of first projection distances and widths of the plurality of candidate roads.

4. The method according to claim 1, wherein: The road confidence of the candidate road group is determined by the following steps: The road confidence of the candidate road group is determined according to the second projection information of the historical trajectory points on each candidate road in the candidate road group.

5. The method according to claim 4, wherein: The determining the road confidence of the candidate road group according to the second projection information of the historical trajectory point on each candidate road in the candidate road group comprises: Determine a second projection distance from the historical trajectory point to the road centerline of the candidate road; The road confidence of the candidate road group is determined according to the second projection distance and the road width of the candidate road.

6. The method according to claim 5, wherein: The determining the road confidence of the candidate road group according to the second projection distance and the road width of the candidate road includes: In response to the existence of a plurality of candidate roads, the road confidence of the candidate road group is determined according to a sum of a plurality of second projection distances and widths of the plurality of candidate roads.

7. The method according to claim 1, wherein: The device confidence of the source device of the to-be-processed trajectory is determined based on the following steps: The device confidence of the source device is determined according to the device configuration information of the source device of the trajectory to be processed.

8. The method according to claim 7, wherein: The determining the device confidence of the source device according to the device configuration information of the source device of the trajectory to be processed includes: According to the device configuration information of the source device of the trajectory to be processed, determining a reference trajectory point corresponding to the reference lane from the historical trajectory having the device configuration information; The device confidence of the source device is determined according to third projection information from the reference trajectory point to the reference lane.

9. The method according to claim 8, wherein: The historical track having the device configuration information includes at least one of the following: a historical track of the source device, and a historical track of a reference device having the same device configuration information as the source device.

10. The method according to claim 8, wherein: The reference lane has no lane in the same direction on the associated road.

11. The method according to claim 8, wherein: The determining, according to the third projection information from the reference trajectory point to the reference lane, the device confidence of the source device comprises: Determine a third projection distance from the reference trajectory point to the reference lane; The device confidence of the source device is determined according to the third projection distance and a median value of the width of the reference lane.

12. The method according to any one of claims 1 to 11, further comprising: In response to the target confidence being greater than or equal to a preset confidence threshold, performing at least one of the following according to the trajectory to be processed: Determine road traffic information; Determining candidate planning trajectories; Determine the vehicle control strategy.

13. A trajectory processing device, comprising: An initial confidence determination module is configured to determine an initial confidence of the trajectory to be processed according to first projection information of the trajectory to be processed on each candidate road in the candidate road group; The target confidence determination module is configured to determine the target confidence of the trajectory to be processed according to the initial confidence, the road confidence of the candidate road group and the device confidence of the source device of the trajectory to be processed.

14. The device according to claim 13, wherein: The initial confidence determination module is configured as follows: Determining candidate roads for the trajectory to be processed according to the driving direction of the trajectory to be processed; Determine a first projection distance from the trajectory point of the to-be-processed trajectory to the road centerline of the candidate road; An initial confidence level of the trajectory to be processed is determined according to the first projection distance and a road width of the candidate road.

15. The device according to claim 14, wherein: The determining, according to the first projection distance and the road width of the candidate road, the initial confidence of the trajectory to be processed includes: In response to the existence of a plurality of candidate roads, an initial confidence of the to-be-processed trajectory is determined according to a sum of a plurality of first projection distances and widths of the plurality of candidate roads.

16. The device according to claim 13, wherein: The road confidence of the candidate road group is determined by the following steps: The road confidence of the candidate road group is determined according to the second projection information of the historical trajectory points on each candidate road in the candidate road group.

17. The device according to claim 16, wherein: The determining the road confidence of the candidate road group according to the second projection information of the historical trajectory point on each candidate road in the candidate road group comprises: Determine a second projection distance from the historical trajectory point to the road centerline of the candidate road; The road confidence of the candidate road group is determined according to the second projection distance and the road width of the candidate road.

18. The device according to claim 17, wherein: The determining the road confidence of the candidate road group according to the second projection distance and the road width of the candidate road includes: In response to the existence of a plurality of candidate roads, the road confidence of the candidate road group is determined according to a sum of a plurality of second projection distances and widths of the plurality of candidate roads.

19. The device according to claim 13, wherein: The device confidence of the source device of the to-be-processed trajectory is determined based on the following steps: The device confidence of the source device is determined according to the device configuration information of the source device of the trajectory to be processed.

20. The device according to claim 19, wherein The determining the device confidence of the source device according to the device configuration information of the source device of the trajectory to be processed includes: According to the device configuration information of the source device of the trajectory to be processed, determining a reference trajectory point corresponding to the reference lane from the historical trajectory having the device configuration information; The device confidence of the source device is determined according to third projection information from the reference trajectory point to the reference lane.

21. The device according to claim 20, wherein: The historical track having the device configuration information includes at least one of the following: a historical track of the source device, and a historical track of a reference device having the same device configuration information as the source device.

22. The device according to claim 20, wherein: The reference lane has no lane in the same direction on the associated road.

23. The device according to claim 20, wherein: The determining, according to the third projection information from the reference trajectory point to the reference lane, the device confidence of the source device comprises: Determine a third projection distance from the reference trajectory point to the reference lane; The device confidence of the source device is determined according to the third projection distance and a median value of the width of the reference lane.

24. The apparatus according to claims 13-23, further comprising: The trajectory application module is configured to, in response to the target confidence being greater than or equal to a preset confidence threshold, perform at least one of the following according to the trajectory to be processed: Determine road traffic information; Determining candidate planning trajectories; Determine the vehicle control strategy.

25. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 12.

26. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-12.

27. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 12.

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