Method, device, apparatus, and computer-readable storage medium for vehicle positioning

Abstract

According to embodiments of the present disclosure, a method, apparatus, device, and computer-readable storage medium for vehicle positioning are provided. The method includes obtaining onboard positioning information of a vehicle at multiple different moments in time; receiving external positioning information for the vehicle determined by a sensing device located outside the vehicle, the external positioning information including information at a first moment corresponding to the external positioning information; determining a second moment from the multiple different moments that matches the first moment in time; and determining the vehicle's position at the current moment based on the onboard positioning information corresponding to the second moment and the external positioning information corresponding to the first moment in time. This method integrates the external positioning information and eliminates the delay in the vehicle positioning information determined by the sensing device outside the vehicle, thereby improving positioning accuracy.

Description

[0001] This application is a divisional application of the application document with application number 201811217365.4. Technical Field

[0002] Embodiments of the present disclosure generally relate to the field of intelligent driving, and more particularly, to a method, apparatus, device, and computer-readable storage medium for vehicle positioning. Background Art

[0003] In recent years, technologies related to autonomous driving and assisted driving have developed rapidly. High-precision vehicle positioning is particularly important in these fields. In practical applications, the error introduced by Global Navigation Satellite System (GNSS) positioning can be as high as 10 meters or even greater. Some autonomous and assisted vehicles can achieve higher-precision positioning by combining high-precision inertial navigation (INS) and Global Navigation Satellite System (GNSS), or by combining high-precision maps and lidar. However, such methods are costly and may be affected by external environmental factors. Therefore, achieving high-precision vehicle positioning has become a focus of attention. Summary of the Invention

[0004] According to an embodiment of the present disclosure, a solution for vehicle positioning is provided.

[0005] In a first aspect of the present disclosure, a method for positioning a vehicle is provided. The method includes: obtaining onboard positioning information of the vehicle at multiple different time instants; receiving external positioning information for the vehicle determined by a sensing device located outside the vehicle, the external positioning information including information at a first time instant corresponding to the external positioning information; determining a second time instant that matches the first time instant from the multiple different time instants; and determining the vehicle's position at a current time instant based on the onboard positioning information corresponding to the second time instant and the external positioning information corresponding to the first time instant.

[0006] In a second aspect of the present disclosure, a device for vehicle positioning is provided. The device includes: an acquisition module configured to acquire onboard positioning information of the vehicle at multiple different moments; a receiving module configured to receive external positioning information for the vehicle determined by a sensing device located outside the vehicle, the external positioning information including information at a first moment corresponding to the external positioning information; a second moment determination module configured to determine a second moment matching the first moment from the multiple different moments; and a position determination module configured to determine the vehicle's position at the current moment based on the onboard positioning information corresponding to the second moment and the external positioning information corresponding to the first moment.

[0007] In a third aspect of the present disclosure, a device is provided, comprising one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, enables the one or more processors to implement the method according to the first aspect of the present disclosure.

[0008] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0009] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram illustrating an example environment in which various embodiments of the present disclosure can be implemented;

[0012] Figure 2 A flowchart illustrating a process for vehicle positioning according to some embodiments of the present disclosure is shown;

[0013] Figure 3 A schematic diagram illustrating vehicle positioning according to some embodiments of the present disclosure is shown;

[0014] Figure 4 A flowchart illustrating a process of determining a second time according to some embodiments of the present disclosure is shown;

[0015] Figure 5 A flowchart illustrating a process for determining a vehicle position according to some embodiments of the present disclosure is shown;

[0016] Figure 6 A flowchart showing a process of updating a vehicle position status list according to an embodiment of the present disclosure is shown;

[0017] Figure 7 A flowchart showing a process of updating a vehicle location status list according to another embodiment of the present disclosure

[0018] Figure 8 A block diagram showing an apparatus for vehicle positioning according to some embodiments of the present disclosure; and

[0019] Figure 9A block diagram is shown of a computing device capable of implementing various embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0021] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to." The term "based on" should be understood as "based at least in part on." The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment." The terms "first," "second," etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0022] As mentioned above, high-precision vehicle positioning is fundamental to autonomous driving. Traditional positioning technologies, such as Global Navigation Satellite System (GNSS), cannot meet the accuracy requirements of autonomous driving. High-precision map-based positioning requires expensive LiDAR installations on vehicles and requires map vendors to maintain and maintain the HD maps to ensure their accuracy.

[0023] In recent years, with the advancement of communication technology, V2X technology has developed rapidly. When determining its own position, vehicles can use positioning information determined by roadside equipment or positioning information determined by other vehicles to improve positioning accuracy. For example, vehicles on the road can be located based on vehicle images captured by roadside cameras. However, due to communication delays between the vehicle to be located and the roadside equipment, or between the vehicle to be located and another vehicle, the external positioning information of the vehicle to be located determined by another sensing device often differs from the actual position of the vehicle to be located at the current moment. Therefore, the positioning results based on the fusion of on-board positioning information and external positioning information often have large errors.

[0024] According to an embodiment of the present disclosure, a solution for vehicle positioning based on an external sensing device and an on-board platform is proposed. In this solution, the vehicle can obtain the on-board positioning information of the vehicle at multiple different times through the on-board positioning platform. The vehicle can also receive external positioning information determined by a sensing device located outside the vehicle, wherein the positioning information can also include information of the first moment corresponding to the external positioning information. The vehicle can then determine a second moment that matches the first moment from different moments, and determine the position of the vehicle at the current moment based on the on-board positioning information corresponding to the second moment and the external positioning information. By obtaining the first moment corresponding to the external positioning information, the vehicle can use the on-board positioning information corresponding to the second moment that matches the first moment as a reference, fuse the external positioning information, and thus predict the position of the vehicle at the current moment, thereby improving the accuracy of vehicle positioning.

[0025] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0026] Figure 1 A schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented is shown. Typical objects are schematically illustrated in the example environment 100, including a road 102, one or more sensing devices 105-1, 105-2, 105-3, and 105-4, and one or more vehicles 110-1 and 110-2. For ease of description, the multiple sensing devices 105-1, 105-2, 105-3, and 105-4 are collectively referred to as sensing devices 105, and the multiple vehicles 110-1 and 110-2 are collectively referred to as vehicles 110. It should be understood that the facilities and objects shown are merely examples, and that the objects that may appear in different traffic environments will vary depending on actual circumstances. The scope of the present disclosure is not limited in this respect.

[0027] exist Figure 1 In the example shown in FIG1 , one or more vehicles 110 - 1 and 110 - 2 are traveling on road 102 . Vehicle 110 may be any type of vehicle that can carry people and / or objects and is propelled by a power system such as an engine, including but not limited to cars, trucks, buses, electric vehicles, motorcycles, RVs, trains, and the like. One or more vehicles 110 in environment 100 may have certain autonomous driving capabilities, also known as unmanned vehicles. Of course, one or more vehicles 110 in environment 100 may also not have autonomous driving capabilities.

[0028] In some embodiments, the sensing devices 105 (e.g., 105-1 and 105-2) in the environment 100 may be roadside devices independent of the vehicle 110, configured to monitor the conditions of the environment 100 to obtain perception information related to the environment 100. In some embodiments, the sensing devices 105 (e.g., sensing device 105-1) may be arranged above the road 102. In some embodiments, the sensing devices 105 (e.g., sensing device 105-2) may also be arranged on both sides of the road 102. In some embodiments, the sensing devices 105 (e.g., 105-3 and 105-4) may also be perception devices mounted on the vehicle 110. In an embodiment of the present disclosure, the sensing devices 105 include image sensors to obtain image information of the road 102 and the vehicle 110 in the environment 100. In some embodiments, the sensing devices may also include one or more other types of sensors, such as a lidar, a millimeter-wave radar, and the like.

[0029] The following will be combined Figures 2 to 7 The process of vehicle positioning according to an embodiment of the present disclosure is described below. Figure 2 FIG. 2 shows a flow chart of a method 200 for vehicle positioning according to an embodiment of the present disclosure. The method 200 may be performed by, for example Figure 1 This is performed by the vehicle 110 - 1 shown in FIG.

[0030] In box 202, vehicle 110-1 obtains the on-board positioning information of vehicle 110-1 at multiple different times. In some embodiments, vehicle 110-1 may include at least one of the following sensors: a global navigation satellite system GNSS receiver, an inertial navigation sensor, a visual sensor, a lidar, and a millimeter wave radar. It should be understood that these different sensors have different sampling frequencies. For example, inertial navigation sensors tend to have a higher sampling frequency, while the sampling frequencies of lidar and millimeter waves are relatively low. Based on the information obtained from these sensors (for example, global navigation satellite system GNSS information, inertial navigation information, visual sensor information, and lidar information), vehicle 110-1 can use a Kalman filter to combine historical positions and different source information to predict the position of the vehicle at different times, that is, determine the on-board positioning information of vehicle 110-1 at multiple different times.

[0031] The following will be combined Figure 3 Describe the process used for vehicle localization, Figure 3 Schematic diagram of vehicle positioning according to some embodiments of the present disclosure is shown. Figure 3As shown, the vehicle 110-1 may store the vehicle positioning information at multiple different times determined based on the Kalman filter in the vehicle position state list 310. The vehicle position state list 310 may include the vehicle positioning information corresponding to multiple different times, for example, the vehicle positioning information 312 corresponding to the current time t0, the vehicle positioning information 314 corresponding to the time t1, the vehicle positioning information 316 corresponding to the time t2, the vehicle positioning information 318 corresponding to the time t3, and the vehicle positioning information 319 corresponding to the time t4. n The corresponding vehicle positioning information 320, etc. The vehicle 110-1 can read the vehicle position status list 310 at multiple different times (t0, t1, t2, t3...t n )'s vehicle positioning information (312, 314, 316, 318 and 320).

[0032] Continue to refer Figure 2 In block 204, the vehicle 110-1 receives external positioning information for the vehicle 110-1 determined by the sensing device 105 located outside the vehicle 110-1. The external positioning information includes information of a time (hereinafter referred to as the first time) corresponding to the external positioning information. In some embodiments, the sensing device 105 may be a roadside device located outside the vehicle 110-1. The vehicle 110-1 may receive information from the roadside device (e.g., Figure 1 1 and 105 - 2 shown in FIG. For example, the roadside equipment may determine the position of vehicle 110 - 1 relative to the roadside equipment through image recognition or radar ranging, and calculate the external positioning information of vehicle 110 - 1 based on the absolute position of the roadside equipment in the world coordinate system.

[0033] In some embodiments, the sensing device 105 may also be another vehicle (eg, Figure 1 Vehicle 110-1 can receive the sensing device in vehicle 110-2 (such as vehicle 110-2). Figure 1 For example, vehicle 110-2 may determine the location of vehicle 110-1 through image recognition or radar ranging, and calculate the external location of vehicle 110-1 based on the absolute position of vehicle 110-2 in the world coordinate system.

[0034] In some embodiments, the sensing device 105 located outside the vehicle 110-1 may include a GNSS receiver. The sensing device 105 may determine the timestamp of the external positioning information of the vehicle 110-1 when it is determined by the sensing device 105 through the GNSS receiver, and the timestamp may indicate the first moment corresponding to the external positioning information. In some embodiments, the sensing device 105 may include the timestamp in the external positioning information and send the external positioning information to the vehicle 110-1. By using the GNSS receiver, the clocks of the external sensing device 105 and the vehicle 110-1 may be aligned, so that the first moment of the same time reference can be calculated based on the timestamp. In some embodiments, the vehicle 110-1 may also convert the timestamp contained in the external positioning information into the first moment under the clock of the vehicle 110-1 based on the difference between the clock of the vehicle 110-1 and the clock of the sensing device 105. As Figure 3 As shown, the vehicle 110 - 1 may receive external positioning information 330 from the external sensing device 105 and determine the first time T based on a timestamp included in the external positioning information 330 .

[0035] Continue to refer Figure 2 In block 206, the vehicle 110-1 determines a second time that matches the first time from a plurality of different time. Figure 4 Describe the process of determining the second moment. Specifically, Figure 4 A flow chart of a process 400 for determining a second time in accordance with some embodiments of the present disclosure is shown.

[0036] like Figure 4 As shown, at block 402, vehicle 110-1 determines a set of times not later than a first time T from a plurality of times. Figure 3 As shown, multiple different moments include: t0, t1, t2, t3...t n In this example, assuming that t0, t1, and t2 are earlier than the first time T, the vehicle 110-1 can determine a set of times not later than the first time T as {t3…t n}.

[0037] At block 404 , the vehicle 110 - 1 selects a second time from a set of times such that a difference between the second time and the first time T is less than a first threshold. Figure 3 For example, vehicle 110 - 1 may traverse the set of moments {t3…t n}, and selects a second time from the group of time moments whose difference from the first time moment T is less than a first threshold. In some embodiments, the vehicle 110-1 may also select the latest time from the group of time moments as the second time moment. For example, Figure 3In the example, the determined second time is time t3 corresponding to the vehicle positioning information 318. Based on this approach, vehicle 110-1 can determine the previous time closest to the time when the sensing device 105 positioned vehicle 110-1, thereby minimizing errors caused by inaccurate predictions such as inertial navigation.

[0038] Continue to refer Figure 2 In block 208, the vehicle 110-1 determines the location of the vehicle 110-1 at the current moment based on the vehicle-mounted positioning information corresponding to the second moment and the external positioning information corresponding to the first moment. Figure 5 Describe the process of determining the vehicle's position. Specifically, Figure 5 A flow chart is shown of a process 500 for determining vehicle location, according to some embodiments of the present disclosure.

[0039] like Figure 5 As shown, in block 502, the vehicle 110-1 predicts the predicted positioning information of the vehicle 110-1 at the first moment based on the vehicle-mounted positioning information of the vehicle 110-1 at the second moment. Figure 3 In the example of FIG, using the sensor information of the vehicle platform, vehicle 110-1 can use a Kalman filter to predict the predicted positioning information 322 of vehicle 110-1 at the first time T based on the vehicle positioning information 318 of vehicle 110-1 at the second time t3. It should be understood that any appropriate Kalman filter in the art can be used to predict the predicted positioning information 322, which will not be described in detail here.

[0040] At block 504 , vehicle 110 - 1 fuses the predicted location information and the external location information to determine the fused location information of the vehicle at the first moment. Figure 3 In the example of FIG. 3 , vehicle 110 - 1 may utilize a Kalman filter to perform weighted fusion on predicted positioning information 322 and external positioning information 330 to determine fused positioning information 332 of vehicle 110 - 1 at the first time T. It should be understood that any appropriate Kalman filter in the art may be used to perform the above fusion process, which will not be described in detail herein.

[0041] At block 506 , based on the fused positioning information, vehicle 110 - 1 predicts the location of vehicle 110 - 1 at the current moment. Figure 3For example, vehicle 110-1 can use the fused positioning information 332 and the Kalman filter to predict the positioning information 342 of vehicle 110-1 at the current moment, and then determine the position of vehicle 110-1 at the current moment. Specifically, vehicle 110-1 can use the fused positioning information of the vehicle at time T and the position, inertial navigation, acceleration and other information in the positioning information to predict the positioning information at the current moment. Based on this approach, method 200 can fuse the positioning information sent by the external sensing device 105 with the on-board positioning information that is closer to the moment when the positioning information is determined, which can not only improve the positioning accuracy by using the positioning result of the external sensing device 105, but also avoid the positioning error caused by transmission delay.

[0042] Continue to refer Figure 2 In some embodiments, the method 200 may also proceed to block 210 where the vehicle 110-1 updates the vehicle location status list. Figures 6 and 7 The process of updating the vehicle position status list according to an embodiment of the present disclosure is described. Specifically, Figure 6 A flow chart illustrating a process 600 of updating a vehicle location status list according to some embodiments of the present disclosure is shown.

[0043] like Figure 6 As shown, in block 602, vehicle 110-1 determines a third time that is later than the second time from a plurality of different time. It should be understood that there may be one or more third times that are later than the second time. Figure 3 For example, the vehicle 110-1 can be viewed from multiple different time points (t0, t1, t2, t3...t n ) determines a moment later than the second moment t3, for example including t0, t1 and t2.

[0044] At block 604, vehicle 110-1 removes the vehicle location information corresponding to the third time from the vehicle location status list. Figure 3 For example, vehicle 110-1 may remove the vehicle positioning information corresponding to the third moment from vehicle position status list 310. Figure 3In the example, vehicle 110-1 can remove the vehicle-mounted positioning information 312 corresponding to t0, the vehicle-mounted positioning information 314 corresponding to t1, and the vehicle-mounted positioning information 316 corresponding to t2 from the list 310. Since these vehicle-mounted positioning information are only predicted based on the vehicle-mounted Kalman filter, their accuracy is not as good as the positioning information 342 based on the fusion of external positioning information. Therefore, the vehicle-mounted positioning information related to these moments can be removed from the vehicle position state list 310. By removing the discarded vehicle-mounted positioning information, vehicle 110-1 can reduce the storage overhead required for maintaining the vehicle position state list. In some embodiments, vehicle 110-1 can also store the positioning information 342 in association with the current moment in a new vehicle position state list 340, thereby ensuring that the new vehicle position state list 340 includes accurate positioning information 342 at the current moment and can be used as the basis for the next prediction, thereby also improving the accuracy of subsequent positioning predictions.

[0045] Figure 7 FIG. 7 is a flow chart showing a process 700 of updating a vehicle position status list according to another embodiment of the present disclosure. Figure 7 As shown, in block 702, the vehicle 110-1 determines a fourth time from a plurality of different time, and the difference between the fourth time and the current time is less than a second threshold. Figure 3 For example, the vehicle 110-1 may generate a plurality of different time instants (t0, t1, t2, t3, ..., t n ) and determine the fourth time t whose difference with the current time is less than the predetermined second threshold value n .

[0046] At block 704, vehicle 110-1 removes the vehicle location information corresponding to the fourth moment from the vehicle location status list. Figure 3 For example, the vehicle 110 - 1 may remove the vehicle positioning information 320 corresponding to the fourth moment from the vehicle position status list 310 , thereby avoiding the vehicle position status list that needs to be maintained from growing infinitely and causing additional storage burden.

[0047] based on Figure 6 The method 600 and Figure 7 In the method 700 shown, the vehicle 110-1 can obtain a new vehicle location status list 340, which not only reduces the storage of unnecessary vehicle location information, but also ensures that the vehicle location information within the threshold time from the current moment can be retained, thereby ensuring that the next time external location information is input, it can find the corresponding vehicle location information.

[0048] Figure 8 FIG. 8 is a block diagram of an apparatus 800 for determining vehicle positioning according to an embodiment of the present disclosure. The apparatus 800 may include Figure 1 In the vehicle 110-1 or as the vehicle 110-1. Figure 8 As shown, apparatus 800 includes an acquisition module 802 configured to acquire vehicle-mounted positioning information of a vehicle at multiple different moments. Apparatus 800 also includes a receiving module 804 configured to receive external positioning information for the vehicle determined at a first moment by a sensing device located outside the vehicle, the external positioning information including information indicating the first moment. Apparatus 800 also includes a second moment determination module 806 configured to determine a second moment that matches the first moment from among the multiple different moments. Apparatus 800 also includes a position determination module 808 configured to determine the vehicle's position at the current moment based on the vehicle-mounted positioning information corresponding to the second moment and the external positioning information corresponding to the first moment.

[0049] In some embodiments, the vehicle's onboard positioning information is determined based on the vehicle's sensor information, and the sensor information includes at least one of the following: global navigation satellite system GNSS information, inertial navigation information, visual sensor information, and lidar information.

[0050] In some embodiments, the sensing device is a roadside device; or the sensing device is located in another vehicle different from the vehicle.

[0051] In some embodiments, the second moment determination module 806 may include: a moment group determination module, configured to determine a group of moments that are no later than the first moment from multiple moments; and a second moment selection module, configured to select the second moment from a group of moments so that the difference between the second moment and the first moment is less than a first threshold.

[0052] In some embodiments, the location determination module 808 may include: a first prediction module, configured to predict the predicted location information of the vehicle at the first moment based on the on-board location information of the vehicle at the second moment; a fusion module, configured to fuse the predicted location information and the external location information to determine the fused location information of the vehicle at the first moment; and a second prediction module, configured to predict the location of the vehicle at the current moment based on the fused location information.

[0053] In some embodiments, the acquisition module 802 may include: a reading module configured to read vehicle positioning information at multiple different times from the vehicle position status list, and the device 800 further includes: an updating module configured to update the vehicle position status list.

[0054] In some embodiments, the update module includes: a third time determination module, configured to determine a third time later than the second time from a plurality of different time moments; and a first removal module, configured to remove the vehicle positioning information corresponding to the third time moment from the vehicle position status list.

[0055] In some embodiments, the update module includes: a fourth moment determination module, configured to determine a fourth moment from multiple different moments, and the difference between the fourth moment and the current moment is less than a second threshold; and a second removal module, configured to remove the vehicle-mounted positioning information corresponding to the fourth moment from the vehicle position status list.

[0056] Figure 9 A schematic block diagram of an example device 900 that can be used to implement an embodiment of the present disclosure is shown. As shown, the device 900 includes a computing unit 901 that can perform various appropriate actions and processes based on computer program instructions stored in a read-only memory (ROM) 902 or loaded from a storage unit 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the device 900 can also be stored in the RAM 903. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0057] Various components in the device 900 are connected to the I / O interface 905, including an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0058] The computing unit 901 may be a variety of general and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 901 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 that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 901 may perform the various methods and processes described above, such as process 200, process 400, process 500, process 600, and / or process 700. For example, in some embodiments, process 200, process 400, process 500, process 600, and / or process 700 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into RAM 903 and executed by computing unit 901, one or more steps of process 200 described above may be performed. Alternatively, in other embodiments, computing unit 901 may be configured to perform process 200, process 400, process 500, process 600, and / or process 700 in any other appropriate manner (e.g., by means of firmware).

[0059] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), and the like.

[0060] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0061] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 can 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.

[0062] In addition, although adopting specific order to describe each operation, this should be understood as requiring such operation to be carried out in the specific order shown or in sequential order, or requiring that all illustrated operations should be carried out to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation also can be implemented in a plurality of implementations individually or in the mode of any suitable subcombination.

[0063] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for vehicle positioning, comprising: Obtain vehicle positioning information at multiple different times; receiving external positioning information for the vehicle determined at a first moment by a sensing device located outside the vehicle, the external positioning information including information indicating the first moment; Determining a second moment that matches the first moment from the multiple different moments; wherein the second moment is a historical moment that is no later than the first moment and has the smallest time difference with the first moment; determining, based on the on-vehicle positioning information corresponding to the second moment and the external positioning information corresponding to the first moment, fused positioning information of the vehicle at the first moment; and Determining the vehicle's current position based on the fused positioning information; Determining the fused positioning information of the vehicle at the first moment includes: Predicting predicted positioning information of the vehicle at the first moment based on the onboard positioning information of the vehicle at the second moment; and The predicted positioning information and the external positioning information are fused to determine the fused positioning information of the vehicle at the first moment.

2. The method according to claim 1, wherein the on-board positioning information of the vehicle is determined based on sensor information of the vehicle, and the sensor information includes at least one of the following: global navigation satellite system GNSS information, inertial navigation information, visual sensor information and lidar information. 3 . The method according to claim 1 , wherein the sensing device is a roadside device; or the sensing device is located in another vehicle different from the vehicle.

4. The method of claim 1 , wherein determining the second time comprises: determining a set of time instants no later than the first time instant from the plurality of time instants; as well as The second moment is selected from the set of moments such that a difference between the second moment and the first moment is less than a first threshold.

5. The method according to claim 1, wherein obtaining vehicle-mounted positioning information of the vehicle at multiple different moments comprises: Read the vehicle positioning information at the plurality of different moments from the vehicle position status list, and The method further includes: updating the vehicle location status list.

6. The method according to claim 5, wherein updating the vehicle position status list comprises: determining a third time point later than the second time point from the different multiple time points; as well as The vehicle positioning information corresponding to the third moment is removed from the vehicle position status list.

7. The method according to claim 5, wherein updating the vehicle position status list comprises: Determining a fourth moment from the multiple different moments, where a difference between the fourth moment and the current moment is less than a second threshold; as well as The vehicle positioning information corresponding to the fourth moment is removed from the vehicle position status list.

8. A device for vehicle positioning, comprising: An acquisition module is configured to acquire vehicle-mounted positioning information of the vehicle at multiple different moments; a receiving module configured to receive external positioning information of the vehicle determined at a first moment by a sensing device located outside the vehicle, the external positioning information including information indicating the first moment; A second time determination module is configured to determine a second time that matches the first time from the multiple different times; wherein the second time is a historical time that is no later than the first time and has the smallest time difference with the first time; and a position determination module configured to determine fused positioning information of the vehicle at the first moment based on the on-board positioning information corresponding to the second moment and the external positioning information corresponding to the first moment; and determine the position of the vehicle at a current moment based on the fused positioning information; Wherein, the location determination module includes: A first prediction module is configured to predict the predicted positioning information of the vehicle at the first moment based on the onboard positioning information of the vehicle at the second moment; and A fusion module is configured to fuse the predicted positioning information and the external positioning information to determine the fused positioning information of the vehicle at the first moment.

9. The device according to claim 8, wherein the on-board positioning information of the vehicle is determined based on sensor information of the vehicle, and the sensor information includes at least one of the following: global navigation satellite system GNSS information, inertial navigation information, visual sensor information and lidar information. 10 . The apparatus according to claim 8 , wherein the sensing device is a roadside device; or the sensing device is located in another vehicle different from the vehicle.

11. The apparatus according to claim 8, wherein the second time determination module comprises: a time group determining module, configured to determine a group of time moments that are no later than the first time moment from the multiple time moments; as well as The second time selection module is configured to select the second time from the group of time instants so that the difference between the second time instant and the first time instant is smaller than a first threshold.

12. The apparatus according to claim 8, wherein the acquisition module comprises: A reading module is configured to read the vehicle positioning information at the plurality of different moments from the vehicle position status list, and The device further includes: an updating module configured to update the vehicle location status list.

13. The apparatus according to claim 12, wherein the update module comprises: A third time determination module is configured to determine a third time later than the second time from the different multiple time points; as well as The first removing module is configured to remove the vehicle positioning information corresponding to the third moment from the vehicle position status list.

14. The apparatus according to claim 12, wherein the update module comprises: a fourth time determination module, configured to determine a fourth time from the multiple different time periods, wherein a difference between the fourth time period and the current time period is less than a second threshold; as well as The second removing module is configured to remove the vehicle positioning information corresponding to the fourth moment from the vehicle position status list.

15. An electronic device, comprising: one or more processors; as well as The memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the method according to any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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