Methods, devices, electronic equipment, and storage media for determining the orientation of a vehicle.

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for determining the heading of a vehicle, relating to the field of vehicle navigation technology, and further to the fields of vehicle heading positioning, big data, etc., particularly a method, apparatus, electronic device, and storage medium for determining the heading of a vehicle, to at least solve the technical problem of low accuracy in determining the heading in related technologies. The specific implementation scheme is as follows: In response to a navigation request from a vehicle on the current road, acquire a set of navigation trajectory points collected within a historical time period and the centerline of the current road; determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline; cluster the set of navigation trajectory points based on the positional relationship to obtain a cluster centerline; determine the heading of the vehicle at the current position based on the centerline and the cluster centerline.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle navigation technology, and further to the fields of vehicle front positioning, big data, etc., and particularly to a method, device, electronic device and storage medium for determining the front orientation of a vehicle. Background Technology

[0002] Navigation software is increasingly becoming an indispensable tool for people's travel, mainly used to locate the user's current location and the destination, and how to get there. Determining the vehicle's orientation before navigation is crucial for driving navigation. Currently, missing or inaccurate directional information affects the accuracy of the vehicle's orientation. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for determining the frontal orientation of a vehicle, to at least solve the technical problem of low accuracy in determining the frontal orientation in related technologies.

[0004] According to one aspect of this disclosure, a method for determining the heading of a vehicle is provided, comprising: in response to a navigation request from the vehicle on the current road, acquiring a set of navigation trajectory points collected in a historical time period and the centerline of the current road, wherein the centerline is a pre-set directional centerline used to characterize and distinguish different traffic directions on the current road; determining the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline; clustering the set of navigation trajectory points based on the positional relationship to obtain a cluster centerline; and determining the heading of the vehicle at the current position based on the centerline and the cluster centerline.

[0005] According to another aspect of this disclosure, a vehicle orientation determination device is provided, comprising: an acquisition module, configured to, in response to a navigation request from a vehicle on a current road, acquire a set of navigation trajectory points on the current road collected within a historical time period and a centerline of the current road, wherein the centerline is a pre-set directional centerline used to characterize and distinguish different traffic directions on the current road; a position determination module, configured to determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline; a clustering module, configured to cluster the set of navigation trajectory points based on the positional relationship to obtain a cluster centerline; and an orientation determination module, configured to determine the orientation of the vehicle's front end at the current position based on the centerline and the cluster centerline.

[0006] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for determining the frontal orientation of a vehicle as proposed in this disclosure.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to execute a method for determining the heading of a vehicle as proposed in this disclosure.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, provides a method for determining the frontal orientation of a vehicle.

[0009] In this disclosure, firstly, in response to a navigation request from a vehicle on the current road, a set of navigation trajectory points collected over a historical period and the centerline of the current road are acquired. The centerline is a pre-defined directional centerline used to distinguish different traffic directions on the current road. Secondly, based on the centerline, the positional relationship of any navigation trajectory point in the set relative to the current road is determined. Thirdly, the set of navigation trajectory points is clustered based on the positional relationship to obtain a cluster centerline. The orientation of the vehicle's front at the current position can be determined based on the centerline and the cluster centerline. A cluster centerline in the real road can be obtained by clustering the navigation trajectory points collected over a historical period. This cluster centerline can be used to determine the accuracy of the original centerline. If the deviation between the two is small, it indicates that the original centerline is highly accurate. In this case, the orientation of the vehicle's front at the current position can be directly determined based on the original centerline. Using the cluster centerline as a comparison reference improves the accuracy of the determined vehicle orientation, thereby at least solving the technical problem of low accuracy in determining the vehicle orientation in related technologies.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0012] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for determining the frontal orientation of a vehicle, according to the present disclosure.

[0013] Figure 2 This is a flowchart of a method for determining the frontal orientation of a vehicle according to an embodiment of the present disclosure;

[0014] Figure 3 This is a schematic diagram of a navigation trajectory point according to an embodiment of the present disclosure;

[0015] Figure 4 This is a schematic diagram of a cluster centerline according to an embodiment of the present disclosure;

[0016] Figure 5 This is a schematic diagram illustrating the determination of the vehicle's frontal orientation according to an embodiment of this disclosure;

[0017] Figure 6 This is a flowchart of a method for determining the frontal orientation of a vehicle according to an embodiment of the present disclosure;

[0018] Figure 7 This is a schematic diagram of a device for determining the frontal orientation of a vehicle according to an embodiment of the present disclosure. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Currently, when using navigation software to bind a starting point to a route—that is, responding to a user's route planning request—the system obtains the vehicle's location information and binds it to the actual road it is currently on. This starting point binding primarily relies on the current Global Positioning System (GPS) information, sensor orientation information, and the current trajectory movement trend. This allows the system to match the starting point to the corresponding road, thereby determining the vehicle's orientation based on the road's direction of travel. However, incomplete or inaccurate orientation information can lead to poor starting point positioning, directly causing problems such as inaccurate voice prompts and incorrect routes. This increases the user's judgment cost when starting, and may even result in traffic violations, accidents, and detours.

[0022] According to embodiments of this disclosure, a method for determining the frontal orientation of a vehicle is provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] The method embodiments provided in this disclosure can be performed in a mobile terminal, computer terminal, or similar electronic device. 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 processors, cellular phones, smartphones, 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 disclosure described and / or claimed herein.

[0024] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for determining the frontal orientation of a vehicle, according to the present disclosure.

[0025] like Figure 1 As shown, the computer terminal 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the computer terminal 100. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0026] Multiple components in the computer terminal 100 are connected to the I / O interface 105, including: an input unit 106, such as a keyboard and mouse; an output unit 107, such as various types of displays and speakers; a storage unit 108, such as a hard disk and optical disk; and a communication unit 109, such as a network interface card (NIC), a modem, or a wireless transceiver. The communication unit 109 allows the computer terminal 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0027] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 executes the data search method described herein. For example, in some embodiments, the data search method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer terminal 100 via ROM 102 and / or communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the data search method described herein may be performed. Alternatively, in other embodiments, the computing unit 101 may be configured to execute the data search method by any other suitable means (e.g., by means of firmware).

[0028] Various implementations of the systems and techniques described 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0029] It should be noted here that, in some optional embodiments, the above... Figure 1The electronic device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular example, and is intended to illustrate the types of components that may exist in the aforementioned electronic devices.

[0030] Under the aforementioned operating environment, this disclosure provides, for example... Figure 2 The method shown is for determining the vehicle's frontal orientation, and this method can be used by... Figure 1 The computer terminal or similar electronic device shown is used for execution. Figure 2 This is a flowchart illustrating a method for determining the frontal orientation of a vehicle according to an embodiment of this disclosure. Figure 2 As shown, the method may include the following steps:

[0031] Step S202: In response to the vehicle's navigation request on the current road, obtain the set of navigation trajectory points on the current road collected within the historical time period and the center line of the current road.

[0032] The centerline is a pre-set directional centerline used to distinguish different traffic directions on the current road.

[0033] The aforementioned road can be a two-way road, and different traffic directions can be distinguished by the center line on such a road.

[0034] The navigation request mentioned above can be any navigation request initiated by the vehicle. At the beginning of each navigation, the vehicle's heading will be determined first, so that the navigation route can be planned based on the vehicle's heading.

[0035] The aforementioned vehicles can be manually driven vehicles, autonomous vehicles, or new energy vehicles. There is no limitation on the type of vehicle here. The method for determining the vehicle's front orientation proposed in this disclosure can be applied to any vehicle.

[0036] The historical time period mentioned above can be set by the user. It can be any time period before the current time, any single time period before the current time, or any multiple time periods before the current time. There are no strict restrictions on the historical time period here, and the historical time period can be determined according to actual needs.

[0037] The aforementioned set of navigation trajectory points includes the navigation trajectory points of multiple vehicles that actually pass through on the current road.

[0038] The centerline of the current road can be a pre-set centerline in the navigation map used to distinguish different directions of travel on the current road.

[0039] In one optional embodiment, when a vehicle needs to navigate on the current road, the system can obtain a set of navigation trajectory points collected in the historical time period and the center line of the current road based on the vehicle's navigation request, so as to determine whether the center line of the current road is accurate based on the set of navigation trajectory points.

[0040] Step S204: Determine the positional relationship of any navigation trajectory point in the navigation trajectory point set relative to the current road based on the centerline.

[0041] In one alternative embodiment, the positional relationship of any navigation trajectory point relative to the current road can be determined by determining the positional relationship between any navigation trajectory point in the set of navigation trajectory points and the centerline.

[0042] For example, if the current road is a north-south road, then the road to the right of the center line of the current road can be a south-to-north road, and the road to the left of the center line of the current road can be a north-to-south road. If the navigation trajectory point is located to the right of the center line, it means that the driving trajectory corresponding to the navigation trajectory point is a south-to-north road. If the navigation trajectory point is located to the left of the center line, it means that the driving trajectory corresponding to the navigation trajectory point is a north-to-south road.

[0043] The centerline allows for the differentiation of navigation trajectory points in different travel directions within the navigation trajectory point set, resulting in navigation trajectory points on both sides of the centerline.

[0044] In another alternative embodiment, a Hidden Markov Model (HMM) can be used to match the accumulated set of driving navigation trajectories to the actual roads traveled, resulting in a set of navigation trajectory points with the actual roads as the basic units. Figure 3 This is a schematic diagram of a navigation trajectory point according to an embodiment of the present disclosure, wherein the dots in the diagram can be navigation trajectory points, and the direction of movement of the navigation trajectory points can be determined according to the direction of traffic on the road.

[0045] Step S206: Cluster the navigation trajectory point set based on the positional relationship to obtain the cluster center line.

[0046] Navigation trajectory points on different sides of the centerline can be clustered based on their positional relationships to obtain trajectory lines on different sides of the centerline, and the cluster centerline can be obtained based on the trajectory lines on different sides.

[0047] The cluster centerline mentioned above is used to represent the centerline of the current road obtained from the set of navigation trajectory points in the actual navigation process.

[0048] In one optional embodiment, during actual navigation, due to the deviation in vehicle positioning, the obtained navigation trajectory points will also deviate from the actual trajectory points of the vehicle on the current road. By clustering the set of navigation trajectory points, a cluster center line can be obtained to distinguish different directions of travel on the current road in the navigation state.

[0049] Figure 4 This is a schematic diagram of a cluster centerline according to an embodiment of the present disclosure, such as... Figure 4 As shown, the diagram contains multiple navigation trajectory points from the set of navigation trajectory points. The thicker line is the cluster center line, and the thinner line is the center line.

[0050] Step S208: Determine the orientation of the vehicle's front end at the current position based on the centerline and the cluster centerline.

[0051] The cluster center lines mentioned above are mainly used to determine whether the current road center line is accurate or has reference value.

[0052] If the deviation between the centerline and the cluster centerline is small, it indicates that the centerline is depicted ideally. In this case, the direction of the vehicle's front end at the current position can be determined based on the positional relationship between the vehicle's current position and the centerline. If the current position is to the left of the centerline, the road traffic direction to the left of the centerline is determined as the direction of the vehicle's front end at the current position; if the current position is to the right of the centerline, the road traffic direction to the right of the centerline is determined as the direction of the vehicle's front end at the current position.

[0053] If the deviation between the centerline and the cluster centerline is large, it indicates that the centerline is not ideally depicted. In this case, determining the vehicle's orientation at the current position based on the centerline will be inaccurate. Other methods can be used to determine the vehicle's orientation at the current position. Optionally, an initial orientation can be determined based on the centerline and displayed so that the user can check its accuracy. If the initial orientation is accurate, it is used as the orientation for the current position; if it is inaccurate, it is modified to obtain the vehicle's orientation at the current position.

[0054] Through the above steps, firstly, in response to a navigation request from a vehicle on the current road, the system acquires a set of navigation trajectory points collected over a historical period and the centerline of the current road. The centerline is a pre-defined directional centerline used to distinguish different traffic directions on the current road. Secondly, based on the centerline, the system determines the positional relationship of any navigation trajectory point in the set relative to the current road. Thirdly, based on the positional relationship, the system clusters the navigation trajectory points to obtain a cluster centerline. The orientation of the vehicle's front at its current position can be determined based on the centerline and the cluster centerline. The system uses the navigation trajectory points collected over a historical period to obtain a cluster centerline on the real road. This cluster centerline can be used to determine the accuracy of the original centerline. If the deviation between the two is small, the original centerline is considered to be highly accurate. In this case, the vehicle's orientation at its current position can be directly determined based on the original centerline. Using the cluster centerline as a reference improves the accuracy of the determined vehicle orientation, thus at least solving the technical problem of low accuracy in determining the vehicle orientation in related technologies.

[0055] Optionally, determining the orientation of the vehicle's front end at the current position based on the centerline and the cluster centerline includes: obtaining a target deviation between the centerline and the cluster centerline, wherein the target deviation is used to represent the degree of deviation between the cluster centerline and the centerline; and determining the orientation based on the current position and the centerline in response to the target deviation being less than a preset deviation.

[0056] The aforementioned preset deviation can be a pre-set deviation, which can be represented by distance.

[0057] In one optional embodiment, a target deviation between the centerline and the cluster centerline can be obtained to determine the degree of deviation between the centerline and the cluster centerline. If the target deviation is less than a preset deviation, it indicates that the deviation between the cluster centerline and the centerline is small, and the centerline is accurately depicted. The vehicle's front orientation at the current position can be obtained with high accuracy through the centerline. If the target deviation is greater than or equal to the preset deviation, it indicates that the deviation between the cluster centerline and the centerline is large, and the accuracy of the centerline is low. This means that the orientation determined by the centerline is less accurate, and the vehicle's front orientation at the current position can be determined by other methods.

[0058] In another optional embodiment, if the target deviation is greater than or equal to the preset deviation, the orientation can be determined based on the current position and the cluster center line. Optionally, the navigation trajectory points within the historical time period can be clustered at preset intervals to obtain the latest cluster center line, and the latest cluster center line can be uploaded to the cloud server so that after receiving a navigation request, the cluster center line can be directly retrieved from the cloud server and pushed to the vehicle for center line verification.

[0059] Figure 5 This is a schematic diagram illustrating the determination of the vehicle's frontal orientation according to an embodiment of this disclosure, such as... Figure 5 As shown, the spatial expressions of the roads in two directions coincide. When the existing vehicle heading information is inaccurate or invalid, the traffic rule "motor vehicles should keep to the right" can be used to determine the vehicle heading. Specifically, the rule is to first determine whether the current position is on the left or right side of the two arrows. If the current position is on the right side of the arrow, then the direction of travel on the right road is determined to be the vehicle heading. If the current position is on the left side of the arrow, then the direction of travel on the left road is determined to be the vehicle heading.

[0060] By following the steps above, the reliability of the centerline can be determined based on the target deviation between the centerline and the cluster centerline. If the deviation is large, it indicates that the centerline error is large, and it is not suitable for determining the vehicle's orientation. If the deviation is small, it indicates that the centerline error is small, and it can be applied to the scenario of determining the vehicle's orientation.

[0061] Optionally, determining the orientation based on the current location and the centerline includes: determining the travel direction corresponding to the current location in the current road based on the centerline; and determining the travel direction as the orientation.

[0062] In one alternative embodiment, it can be determined whether the current position is to the left or right of the center line. If it is to the left of the center line, the road traffic direction to the left of the center line can be used as the direction the vehicle is facing, so as to formulate a navigation route based on that direction. If it is to the right of the center line, the road traffic direction to the right of the center line can be used as the direction the vehicle is facing, so as to formulate a navigation route based on that direction.

[0063] By following the steps above, you can determine whether your current position is to the left or right of the center line, and determine the vehicle's facing direction based on the traffic directions on both sides of the center line.

[0064] Optionally, clustering the navigation trajectory point set based on positional relationships to obtain cluster center lines includes: clustering the navigation trajectory point set based on positional relationships to obtain trajectory lines in different travel directions; and determining the cluster center lines based on the trajectory lines in different travel directions.

[0065] In one alternative embodiment, the navigation trajectory point set can be clustered according to the positional relationship using a trajectory clustering algorithm (traclus) to obtain trajectory lines in different travel directions, and the cluster center line can be determined based on the average value of the trajectory lines in different travel directions.

[0066] By performing the above steps, we can cluster the navigation trajectory points to obtain trajectory lines in different travel directions. By averaging the trajectory lines in different travel directions, we can determine the cluster center line that most navigation trajectory points refer to when the vehicle is actually driving, which is used to distinguish between different travel directions.

[0067] Optionally, the method further includes: obtaining multiple first candidate roads at the current location and the travel directions of the multiple first candidate roads; determining the target confidence of the multiple first candidate roads based on the travel directions and orientations of the multiple first candidate roads, wherein the target confidence is used to represent the confidence that the multiple first candidate roads are the current road; determining the target road from the multiple first candidate roads based on the target confidence of the multiple first candidate roads, wherein the target confidence of the target road is greater than the target confidence of the other first candidate roads in the multiple first candidate roads besides the target road; verifying the orientation based on the travel direction of the target road to obtain a verification result, wherein the verification result is used to indicate whether the orientation is accurate.

[0068] The aforementioned multiple first-candidate roads can be multiple roads that are close to the current location. Generally, during positioning, because two roads are close, a vehicle on the current road may be positioned on another nearby road. In this case, the current location can be determined as to whether the current road is the actual road where the vehicle is located, based on the traffic direction of the multiple first-candidate roads and the determined orientation of the vehicle.

[0069] In one optional embodiment, multiple first candidate roads and their traffic directions can be obtained for the current location. Based on the traffic directions and orientations of these first candidate roads, a target confidence level can be determined. A target road can then be determined from the multiple first candidate roads based on this target confidence level. This target road is the road where the vehicle is most likely to be located. If the target road has a traffic direction consistent with the given orientation, then the orientation is accurate. For example, if the target road is a north-south road with an orientation facing south, then the orientation is accurate, and the current road is the target road, meaning the road location at the current location is accurate. If the target road is a north-south road but has an orientation facing west, then the orientation is inaccurate, and the current road is not the target road, meaning the road location at the current location is inaccurate.

[0070] By identifying multiple first candidate roads, it can be determined whether the current location is accurately located. If the direction of travel of the target road with the highest confidence is the same as the orientation, it can be determined that the current location is relatively accurate. Furthermore, the accuracy of the orientation can be verified.

[0071] Optionally, obtaining multiple first candidate roads and the travel directions of the multiple first candidate roads at the current location includes: obtaining multiple roads within a preset area corresponding to the current location; determining a first preset number of multiple second candidate roads based on the distance between the multiple roads and the current location; determining the initial confidence level of the vehicle positioning on the multiple second candidate roads; determining a second preset number of multiple first candidate roads from the multiple second candidate roads based on the initial confidence level, and obtaining the travel directions of the multiple first candidate roads.

[0072] The aforementioned preset area can be a region within a preset range of the current location.

[0073] The first preset quantity mentioned above can be set by yourself. For example, the first preset quantity can be 8 or 10. This is just an example. The specific quantity can be set according to the actual situation.

[0074] The second preset quantity mentioned above can be set by yourself. For example, the second preset quantity can be 2. This is just an example. The specific quantity can be set according to the actual situation.

[0075] The initial confidence level mentioned above can be used to represent the confidence level of the current position on the second candidate road. It can be represented by the distance between the second candidate road and the current position. The closer the distance between the second candidate road and the current position, the higher the initial confidence level. The farther the distance between the second candidate road and the current position, the lower the initial confidence level.

[0076] In one optional embodiment, multiple roads within a preset area corresponding to the current location can be obtained. A first preset number of second candidate roads can be determined based on the distance between the multiple roads and the current location. The multiple second candidate roads can be filtered by the initial confidence level of the multiple second candidate roads to obtain a second preset number of first candidate roads. At this time, the travel direction of the multiple first candidate roads can be obtained.

[0077] For example, multiple roads within a preset area can be retrieved based on the user's current location. Then, the distance between the current location and these roads is calculated, and the N closest roads are selected as first-line candidate roads (typically N=8 or N=10). Finally, an iterative decision tree algorithm (Gradient Boosting Decision Tree, GBDT) is used to vote on the N first-line candidate roads, and the two roads with the highest number of votes are output as second-line candidate roads. It should be noted that the number of votes for a road generally corresponds to the confidence level of the vehicle on that road; the higher the number of votes, the higher the probability that the vehicle is located on that road.

[0078] By using the above steps, multiple second candidate roads within a preset area can be preprocessed to obtain multiple first candidate roads with high confidence, thereby reducing the amount of subsequent calculations.

[0079] Optionally, the target confidence of multiple first candidate roads is determined based on the travel direction and orientation of the multiple first candidate roads, including: weighting the initial confidence of the multiple first candidate roads based on the travel direction and orientation of the multiple first candidate roads to obtain the target confidence of the multiple first candidate roads.

[0080] In one optional embodiment, if the orientation is the same as any of the travel directions of the first candidate road, the weight of the first candidate road can be increased; if the orientation is different from all the travel directions of the first candidate road, the weight of the first candidate road can be decreased. By using different weights of the first candidate roads, the initial confidence of multiple first candidate roads can be weighted to obtain the target confidence of multiple first candidate roads.

[0081] By following the steps above, the initial confidence levels of multiple first-candidate roads can be weighted based on their travel direction and orientation, thereby increasing the confidence level of roads with the same travel direction and orientation.

[0082] Optionally, the initial confidence scores of the multiple first candidate roads are weighted based on their travel direction and orientation to obtain target confidence scores. This includes: in response to the first target candidate road having the same travel direction and orientation, the initial confidence scores of the first target candidate road are weighted based on a first weight value to obtain a first confidence score of the first target candidate road; in response to the second target candidate road having a different travel direction and orientation, the initial confidence scores of the second target candidate road are weighted based on a second weight value to obtain a second confidence score of the second target candidate road, wherein the second weight value is less than the first weight value; and the target confidence scores of the multiple first candidate roads are obtained based on the first and second confidence scores.

[0083] The first weight value mentioned above can be 1.3. The second weight value mentioned above can be 1.

[0084] In one optional embodiment, if there are two first candidate roads, the first first candidate road is the first target candidate road, and the initial confidence level of the first target candidate road is... The second candidate path is the second target candidate path, and the initial confidence level of the second target candidate path is... The direction of travel is L i Oriented towards Dir valid Then, the multiple first candidate roads are weighted based on the following formula:

[0085] If the result is not a default value, the votes for the top 2-link will be weighted according to formula (3.1):

[0086]

[0087] Where β can be a pre-set parameter, V i It can be the first target candidate road, 1.3 can be the first weight value mentioned above, and 1 can be the second weight value mentioned above.

[0088] When the direction of the vehicle's head is basically the same as the direction of the first target candidate road, the weight can be increased by 1.3. When the direction of the vehicle's head is significantly different from the direction of the second target candidate road, the weight can be left unchanged.

[0089] By following the steps above, the weight of roads with the same travel direction and orientation can be increased, while the weight of roads with different travel directions and orientations can be decreased, thereby increasing the confidence level of roads with the same travel direction and orientation.

[0090] Figure 6 This is a flowchart of a method for determining the frontal orientation of a vehicle according to an embodiment of the present disclosure, as follows: Figure 6 As shown, it includes:

[0091] Step S601: In response to the vehicle's navigation request on the current road, obtain the set of navigation trajectory points on the current road collected within the historical time period and the center line of the current road.

[0092] The centerline is a pre-set directional centerline used to distinguish different traffic directions on the current road.

[0093] Step S602: Determine the positional relationship of any navigation trajectory point in the navigation trajectory point set relative to the current road based on the centerline.

[0094] Step S603: Cluster the navigation trajectory point set based on the positional relationship to obtain the cluster center line.

[0095] Step S604: Determine the orientation of the vehicle's front end at the current position based on the centerline and the cluster centerline.

[0096] Step S605: Obtain multiple first candidate roads and the travel directions of the multiple first candidate roads at the current location.

[0097] Step S606: Determine the target confidence level of multiple first candidate roads based on their travel direction and orientation.

[0098] The target confidence level is used to represent the confidence level that multiple first candidate roads are the current road.

[0099] Step S607: Determine the target road from the multiple first candidate roads based on the target confidence of the multiple first candidate roads.

[0100] Among them, the target confidence score of the target road is greater than the target confidence scores of the other first candidate roads besides the target road.

[0101] Step S608: Verify the orientation based on the traffic direction of the target road to obtain the verification result.

[0102] The verification result is used to indicate whether the orientation is accurate.

[0103] Step S609: Verify the orientation based on the traffic direction of the target road to obtain the verification result.

[0104] The verification result is used to indicate whether the orientation is accurate.

[0105] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this disclosure.

[0107] Figure 7 This is a schematic diagram of a device for determining the frontal orientation of a vehicle according to an embodiment of the present disclosure, as shown below. Figure 7As shown, the device 700 for determining the frontal orientation of a vehicle includes: an acquisition module 702, a position determination module 704, a clustering module 706, and an orientation determination module 708.

[0108] The system includes the following modules: an acquisition module, which responds to a navigation request from a vehicle on the current road by acquiring a set of navigation trajectory points collected over a historical period and the centerline of the current road, where the centerline is a pre-defined directional centerline used to distinguish different traffic directions on the current road; a position determination module, which determines the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline; a clustering module, which clusters the set of navigation trajectory points based on their positional relationships to obtain a cluster centerline; and an orientation determination module, which determines the orientation of the vehicle's front end at the current position based on the centerline and the cluster centerline.

[0109] Optionally, the orientation determination module includes: an acquisition unit and a determination unit.

[0110] The acquisition unit is used to acquire the target deviation between the centerline and the cluster centerline, wherein the target deviation is used to represent the degree of deviation between the cluster centerline and the centerline; the determination unit is used to determine the orientation based on the current position and the centerline in response to the target deviation being less than a preset deviation.

[0111] Optionally, the orientation determination module includes: an acquisition unit for acquiring the target deviation between the centerline and the cluster centerline, wherein the target deviation is used to represent the degree of deviation between the cluster centerline and the centerline; and an orientation determination unit for determining the orientation based on the current position and the centerline in response to the target deviation being less than a preset deviation.

[0112] Optionally, the orientation determination unit includes: a traffic direction determination subunit, used to determine the traffic direction corresponding to the current position in the current road based on the centerline; and an orientation determination subunit, used to determine the traffic direction as the orientation.

[0113] Optionally, the clustering module includes: a clustering unit for clustering the set of navigation trajectory points based on positional relationships to obtain trajectory lines in different travel directions; and a trajectory line determination unit for determining the cluster center line based on the trajectory lines in different travel directions.

[0114] Optionally, the device further includes: a traffic direction acquisition module, used to acquire multiple first candidate roads at the current location and the traffic directions of the multiple first candidate roads; a confidence level determination module, used to determine the target confidence level of the multiple first candidate roads based on the traffic directions and orientations of the multiple first candidate roads, wherein the target confidence level is used to represent the confidence level that the multiple first candidate roads are the current road; a target road determination module, used to determine the target road from the multiple first candidate roads based on the target confidence level of the multiple first candidate roads, wherein the target confidence level of the target road is greater than the target confidence levels of the other first candidate roads besides the target road; and a verification module, used to verify the orientation based on the traffic direction of the target road and obtain a verification result, wherein the verification result is used to indicate whether the orientation is accurate.

[0115] Optionally, the traffic direction acquisition module includes: a road acquisition unit, a road determination unit, and a confidence level determination unit.

[0116] The road acquisition unit is used to acquire multiple roads within a preset area corresponding to the current location; the road determination unit is used to determine a first preset number of multiple second candidate roads based on the distance between the multiple roads and the current location; the confidence determination unit is used to determine the initial confidence of the vehicle positioning on the multiple second candidate roads; the road determination unit is also used to determine a second preset number of multiple first candidate roads from the multiple second candidate roads based on the initial confidence, and to acquire the travel direction of the multiple first candidate roads.

[0117] Optionally, the confidence determination module is also used to weight the initial confidence of multiple first candidate roads based on the travel direction and orientation of the multiple first candidate roads to obtain the target confidence of the multiple first candidate roads.

[0118] Optionally, the confidence determination module includes: a weighting unit and a determination unit.

[0119] The weighting unit is used to respond to the fact that the travel direction and orientation of the first target candidate road among multiple first candidate roads are consistent, and to perform weighting processing on the initial confidence of the first target candidate road based on a first weight value to obtain the first confidence of the first target candidate road; the weighting unit is also used to respond to the fact that the travel direction and orientation of the second target candidate road among multiple first candidate roads are inconsistent, and to perform weighting processing on the initial confidence of the second target candidate road based on a second weight value to obtain the second confidence of the second target candidate road, wherein the second weight value is less than the first weight value; the determination unit is used to obtain the target confidence of multiple first candidate roads based on the first confidence and the second confidence.

[0120] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0121] According to embodiments of this disclosure, this disclosure also provides an electronic device including a memory and at least one processor, the memory storing computer instructions, the processor being configured to execute the computer instructions to perform the steps in any of the above method embodiments.

[0122] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0123] Optionally, in this disclosure, the processor described above can be configured to perform the following steps via a computer program:

[0124] S1, in response to the vehicle's navigation request on the current road, obtains the set of navigation trajectory points on the current road collected within the historical time period and the center line of the current road, wherein the center line is a pre-set directional center line used to distinguish different traffic directions on the current road;

[0125] S2, determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline;

[0126] S3, cluster the navigation trajectory point set based on positional relationships to obtain the cluster center line;

[0127] S4 determines the orientation of the vehicle's front end at the current position based on the centerline and cluster centerline.

[0128] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0129] According to embodiments of the present disclosure, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to perform the steps in any of the above method embodiments at runtime.

[0130] Optionally, in this embodiment, the non-volatile storage medium described above can be configured to store a computer program for performing the following steps:

[0131] S1, in response to the vehicle's navigation request on the current road, obtains the set of navigation trajectory points on the current road collected within the historical time period and the center line of the current road, wherein the center line is a pre-set directional center line used to distinguish different traffic directions on the current road;

[0132] S2, determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline;

[0133] S3, cluster the navigation trajectory point set based on positional relationships to obtain the cluster center line;

[0134] S4 determines the orientation of the vehicle's front end at the current position based on the centerline and cluster centerline.

[0135] Optionally, in this embodiment, the aforementioned non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0136] According to embodiments of this disclosure, a computer program product is also provided. Program code for implementing the audio processing methods of this disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on a machine, partially on a machine, partially on a remote machine as a standalone software package, or entirely on a remote machine or server.

[0137] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this disclosure, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0142] The above description is only a preferred embodiment of this disclosure. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for determining the orientation of a vehicle's front end, comprising: In response to a navigation request from a vehicle on the current road, the system acquires a set of navigation trajectory points on the current road collected within a historical time period and the centerline of the current road, wherein the centerline is a pre-set directional centerline used to distinguish different travel directions on the current road. Based on the centerline, determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road; Based on the positional relationships, the navigation trajectory point set is clustered to obtain the cluster center lines; Obtain the target deviation between the center line and the cluster center line, wherein the target deviation is used to represent the degree of deviation between the cluster center line and the center line; In response to the target deviation being less than a preset deviation, the orientation is determined based on the current position and the centerline.

2. The method according to claim 1, wherein, Determining the orientation based on the current position and the centerline includes: The current travel direction corresponding to the current position in the current road is determined based on the centerline; The direction of passage is determined as the orientation.

3. The method according to claim 1, wherein, Based on the aforementioned positional relationships, the navigation trajectory point set is clustered to obtain cluster center lines, including: Based on the positional relationship, the navigation trajectory point set is clustered to obtain trajectory lines in different travel directions; The cluster center line is determined based on the trajectory lines in the different travel directions.

4. The method according to claim 1, wherein, The method further includes: Obtain multiple first candidate roads at the current location and the travel directions of the multiple first candidate roads; The target confidence level of the plurality of first candidate roads is determined based on the travel direction and orientation of the plurality of first candidate roads, wherein the target confidence level is used to represent the confidence level of the plurality of first candidate roads as the current road; A target road is determined from the plurality of first candidate roads based on the target confidence scores of the plurality of first candidate roads, wherein the target confidence score of the target road is greater than the target confidence scores of the other first candidate roads in the plurality of first candidate roads besides the target road. The orientation is verified based on the travel direction of the target road to obtain a verification result, wherein the verification result is used to indicate whether the orientation is accurate.

5. The method according to claim 4, wherein, Obtaining multiple first candidate roads at the current location and the travel directions of the multiple first candidate roads, including: Obtain multiple roads within the preset area corresponding to the current location; A first preset number of second candidate roads are determined based on the distances between the multiple roads and the current location; Determine the initial confidence level of the vehicle's location on the plurality of second candidate roads; Based on the initial confidence level, a second preset number of first candidate roads are determined from the plurality of second candidate roads, and the travel direction of the plurality of first candidate roads is obtained.

6. The method according to claim 5, wherein, Determining the target confidence level of the plurality of first candidate roads based on the travel direction and orientation of the plurality of first candidate roads includes: The initial confidence scores of the multiple first candidate roads are weighted based on their travel direction and orientation to obtain the target confidence scores of the multiple first candidate roads.

7. The method according to claim 6, wherein, The initial confidence scores of the multiple first candidate roads are weighted based on their travel direction and orientation to obtain the target confidence scores, including: In response to the fact that the travel direction of the first target candidate road among the plurality of first candidate roads is consistent with the orientation, the initial confidence of the first target candidate road is weighted based on the first weight value to obtain the first confidence of the first target candidate road; In response to the fact that the travel direction of the second target candidate road among the plurality of first candidate roads is inconsistent with the orientation, the initial confidence of the second target candidate road is weighted based on the second weight value to obtain the second confidence of the second target candidate road, wherein the second weight value is less than the first weight value; The target confidence levels of the multiple first candidate roads are obtained based on the first confidence level and the second confidence level.

8. A device for determining the heading of a vehicle, wherein, include: The acquisition module is used to respond to a navigation request from a vehicle on the current road and acquire a set of navigation trajectory points on the current road collected within a historical time period and the center line of the current road, wherein the center line is a pre-set directional center line used to distinguish different traffic directions on the current road; The position determination module is used to determine the positional relationship of any navigation trajectory point in the set of navigation trajectory points relative to the current road based on the centerline; The clustering module is used to cluster the navigation trajectory point set based on the positional relationship to obtain the cluster center line; An orientation determination module includes an acquisition unit for acquiring a target deviation between the centerline and the cluster centerline, wherein the target deviation represents the degree of deviation between the cluster centerline and the centerline; and an orientation determination unit for determining the orientation based on the current position and the centerline in response to the target deviation being less than a preset deviation.

9. The apparatus according to claim 8, wherein, Orientation determining unit, including: The traffic direction determination subunit is used to determine the traffic direction corresponding to the current position in the current road based on the centerline; An orientation determination subunit is used to determine the passage direction as the orientation.

10. The apparatus according to claim 8, wherein, The clustering module includes: Clustering unit, used to cluster the navigation trajectory point set based on the positional relationship to obtain trajectory lines in different travel directions; The trajectory line determination unit is used to determine the cluster center line based on the trajectory lines in the different travel directions.

11. The apparatus according to claim 8, wherein, The device further includes: The travel direction acquisition module is used to acquire multiple first candidate roads at the current location and the travel directions of the multiple first candidate roads; A confidence determination module is used to determine the target confidence of the plurality of first candidate roads based on the travel direction and the orientation of the plurality of first candidate roads, wherein the target confidence is used to represent the confidence that the plurality of first candidate roads are the current road; A target road determination module is used to determine a target road from the plurality of first candidate roads based on the target confidence of the plurality of first candidate roads, wherein the target confidence of the target road is greater than the target confidence of other first candidate roads in the plurality of first candidate roads besides the target road; The verification module is used to verify the orientation based on the traffic direction of the target road and obtain a verification result, wherein the verification result is used to indicate whether the orientation is accurate.

12. 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 to enable the at least one processor to perform the method of any one of claims 1-7.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

14. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

15. A vehicle, said vehicle being executed by a controller to implement the method according to any one of claims 1-7.

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