Auxiliary positioning method, device, equipment and medium

Abstract

The invention discloses an auxiliary positioning method and device, equipment and a medium. The method comprises the steps that when satellite signals are lost, a curtail monkey decision-making mechanism is simulated, and a leader predicts a target area where a vehicle is located according to real-time feedback information of the vehicle; and the follower carries out dense search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is recovered. According to the embodiment of the invention, accurate positioning under the condition that satellite signals are lost can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and in particular to an auxiliary positioning method, device, equipment and medium. Background Art

[0002] At present, in complex environments such as urban high-rise areas, tunnels and underground parking lots where GPS signals are limited or completely blocked, traditional positioning technologies often find it difficult to guarantee high-precision positioning results, and may even lead to positioning failure due to signal loss.

[0003] Traditional positioning technologies often rely on external GPS signals or fixed infrastructure, and are unable to cope with complex and changing driving environments. Especially in extreme weather or signal interference conditions, their positioning performance will be severely affected. Summary of the invention

[0004] The present invention provides an auxiliary positioning method, device, equipment and medium to achieve accurate positioning when satellite signals are lost.

[0005] According to one aspect of the present invention, there is provided an auxiliary positioning method, comprising:

[0006] Simulating the capuchin monkey decision-making mechanism when satellite signals are lost, the leader predicts the target area where the vehicle is located based on the real-time feedback information from the vehicle;

[0007] The follower performs an intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

[0008] According to another aspect of the present invention, there is provided an auxiliary positioning device, comprising:

[0009] A search start module, used to simulate the capuchin monkey decision-making mechanism when the satellite signal is lost, where the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle;

[0010] The search end module is used for the follower to perform an intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the auxiliary positioning method according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, 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 a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the assisted positioning method described in any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the auxiliary positioning method described in any embodiment of the present invention when executed.

[0014] The embodiment of the present invention introduces the capuchin algorithm to simulate the capuchin's behavior of efficiently selecting paths during foraging. In car positioning, when the GPS signal is blocked by complex environments such as tunnels and underground parking lots, it can integrate data from multiple car sensors, and through intelligent analysis and optimization, reduce the impact of signal loss on car positioning, thereby achieving more accurate and stable vehicle positioning.

[0015] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of an auxiliary positioning method provided according to an embodiment of the present invention;

[0018] Figure 2 is a flow chart of an auxiliary positioning method provided according to another embodiment of the present invention;

[0019] Figure 3 is a structural schematic diagram of an auxiliary positioning method provided according to another embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Figure 1 This is a flow chart of an auxiliary positioning method provided by an embodiment of the present invention. This embodiment is applicable to the situation where a vehicle cannot accurately position itself due to loss of satellite signals during movement. The method can be performed by an auxiliary positioning device, which can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities, such as a vehicle-mounted positioning system. Figure 1 As shown, the method includes:

[0024] S110, simulating the capuchin monkey decision-making mechanism when the satellite signal is lost, the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle.

[0025] S120: The follower performs an intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

[0026] Among them, the capuchin search algorithm (CapSA) is a new type of intelligent optimization algorithm. The algorithm models the behavior of capuchins wandering in the forest and designs the basic optimization characteristics of the algorithm. It has the characteristics of strong optimization ability and fast convergence speed. In this algorithm, the capuchin population includes two roles. One is the leader, which refers to the individual with the best fitness value in the current population. It represents the best solution found so far and guides the followers to search in a better direction; the other is the follower, which refers to other individuals except the leader. They adjust their positions according to the guidance of the leader in order to find a better solution. Real-time feedback information is used to characterize the state change information of the vehicle in continuous time.

[0027] Specifically, the vehicle's movement process is preferentially positioned based on the received satellite signals. When the vehicle loses the satellite signal due to entering a tunnel, underground garage, or encountering extreme weather, real-time feedback information from the vehicle when the satellite signal is lost is obtained, and the leader predicts the target area where the vehicle is located at this time based on the real-time feedback information.

[0028] The follower conducts an intensive search in the target area. If the candidate position found meets the output requirements (mainly the positioning accuracy requirements), the candidate position is output as the final target position of the vehicle in the target area. If the candidate position does not meet the output requirements, feedback is given to the leader, requiring the leader to give a new target area, and the follower conducts an intensive search again in the new target area.

[0029] This iterative process will be repeated many times until the number of iterations reaches the upper limit, in which case the candidate position obtained from the last iteration is determined as the target position, the candidate position meets the output requirements, in which case the candidate position that meets the output requirements is determined as the target position, or the satellite signal is restored and positioning is performed again relying on the satellite signal.

[0030] The embodiment of the present invention introduces the capuchin algorithm to simulate the capuchin's behavior of efficiently selecting paths during foraging. In car positioning, when the GPS signal is blocked by complex environments such as tunnels and underground parking lots, it can integrate data from multiple car sensors, and through intelligent analysis and optimization, reduce the impact of signal loss on car positioning, thereby achieving more accurate and stable vehicle positioning.

[0031] Based on the above embodiment, optionally, when the satellite signal is not lost, the capuchin monkey decision-making mechanism is simulated, and the leader predicts the target area where the vehicle is located based on the vehicle's historical data and global information; the global information includes the satellite signal; and the follower performs an intensive search in the target area to determine the target position of the vehicle in the target area.

[0032] Specifically, when the satellite signal is not lost, the capuchin decision mechanism can also be simulated. The leader uses the vehicle's historical data and global information to conduct a large-scale search, evaluate the positioning accuracy of different areas, and determine the target area, that is, the area where the vehicle is most likely to be located. The follower conducts a local search in the target area, quickly converges to the optimal solution, and determines the target position of the vehicle in the target area. Compared with the capuchin decision mechanism when the satellite signal is lost, the leader now has the satellite signal as a positioning reference data. When the satellite signal is not lost, the capuchin decision mechanism is simulated, and there is no need to load the capuchin decision mechanism from scratch when the satellite signal is not lost, which can improve the positioning efficiency.

[0033] Figure 2 This is a flow chart of an auxiliary positioning method provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0034] S210. Construct a historical data set based on the vehicle's motion information and environmental information; based on the historical data set, simulate the decision-making process and search process of the capuchin monkey when faced with multiple choices to obtain a first machine learning model and a second machine learning model, respectively.

[0035] S220. Determine the first machine learning model as a leader in the capuchin monkey decision-making mechanism, and determine the second machine learning model as a follower in the capuchin monkey decision-making mechanism.

[0036] Specifically, in the early data collection stage, the vehicle's motion information and environmental information are obtained to build a complete historical data set. The historical data set is preprocessed to facilitate the subsequent extraction of key features such as road type and environmental changes from the processed data.

[0037] Data related to the leader's decision-making task is extracted from the historical data set, and the basic machine learning model is trained accordingly to obtain the first machine learning model as the leader. Similarly, data related to the follower's search task is extracted from the historical data set, and the basic machine learning model is trained accordingly to obtain the second machine learning model as the follower. Since the tasks of the leader and the follower are relatively fixed, the decision-making process of the capuchin monkey when facing multiple choices can be simulated by training the neural network model, and learn how it chooses the optimal path in a complex environment.

[0038] S230, simulating the capuchin monkey decision-making mechanism when the satellite signal is lost, the leader determines the possible position and movement trajectory of the vehicle according to the real-time feedback information of the vehicle; the leader predicts the target area where the vehicle is located according to the possible position and movement trajectory.

[0039] Specifically, the leader recursively infers the possible position and movement trajectory of the vehicle at the current moment through real-time feedback. According to the possible position and movement trajectory of the vehicle at the current moment, the leader sets a range (within xx square meters) and determines it as the possible movement area of ​​the vehicle in the next time period, that is, the target area where the vehicle is located.

[0040] S240, the follower performs intensive search in the target area to obtain candidate positions;

[0041] S250. If the candidate position meets the preset positioning accuracy, the candidate position is determined as the target position of the vehicle in the predicted area; if the candidate position does not meet the preset positioning accuracy, the search results of the intensive search are supplemented to the real-time feedback information, and the execution leader is returned to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle until the candidate position meets the preset positioning accuracy or the satellite signal is restored.

[0042] Specifically, the follower conducts intensive search in the target area determined by the leader, and can further divide the target area through the grid division method, and gradually narrow the search range to focus on precise positioning in a smaller area. When the follower searches for a vehicle position that meets the preset positioning accuracy (for example, the positioning accuracy is less than xx meters) in the target area, this candidate position is determined as the target position of the vehicle in the prediction area.

[0043] If the follower does not find a candidate location with the preset expected positioning accuracy in the current area, it will add the search results of this intensive search (including candidate locations and search parameters) to the real-time feedback information to feed back to the leader, requiring the leader to search for a new target area again.

[0044] In addition, during the adjustment and optimization process, both the leader and the leader can continuously adjust the search strategy, such as adjusting the search step size, adjusting the search direction, or changing the grid division area accuracy to adapt to environmental changes and improve positioning accuracy.

[0045] Based on the above embodiment, optionally, the historical data includes the vehicle's motion information, environmental information and the user's vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent the changes in the vehicle's motion parameters in space.

[0046] Specifically, historical data includes vehicle motion information (such as position, speed, acceleration, driving direction, etc.), environmental information (road type, surrounding building information, obstacle information, satellite signal strength, etc.), and vehicle owner’s vehicle usage habits (such as driving route preferences, frequent stop areas, etc.). The vehicle dynamics model can be constructed based on the vehicle’s kinematics to represent the changes in the vehicle’s position, speed, acceleration and other motion parameters in space over time, thereby providing a basis for predicting vehicle movement, so that a reasonable prediction of the vehicle’s position can be made when the satellite signal is lost.

[0047] The embodiment of the present invention optimizes the iteration termination condition of the capuchin decision mechanism to avoid excessive occupation of computing resources due to unlimited operation of the capuchin decision mechanism.

[0048] Figure 3 FIG. 1 is a schematic diagram of a structure of an auxiliary positioning device provided by another embodiment of the present invention. Figure 3 As shown, the device comprises:

[0049] A search start module 310, for simulating the capuchin monkey decision-making mechanism when the satellite signal is lost, where the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle;

[0050] The search end module 320 is used for the follower to perform intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

[0051] The auxiliary positioning device provided in the embodiment of the present invention can execute the auxiliary positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0052] Optionally, the real-time feedback information includes historical data and a vehicle dynamic model, and the search start module 310 includes:

[0053] A position trajectory estimation unit, used for the leader to determine the possible position and movement trajectory of the vehicle according to the real-time feedback information of the vehicle;

[0054] The area division unit is used for the leader to predict the target area where the vehicle is located based on the possible position and movement trajectory.

[0055] Optionally, the search end module 320 includes:

[0056] A search unit, configured to follow the target area to perform an intensive search to obtain a candidate position;

[0057] A first ending unit, configured to determine the candidate position as a target position of the vehicle in a prediction area if the candidate position meets a preset positioning accuracy;

[0058] The second ending unit is used to add the search results of the intensive search to the real-time feedback information if the candidate position does not meet the preset positioning accuracy, and return to the execution leader to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle until the candidate position meets the preset positioning accuracy or the satellite signal is restored.

[0059] Optionally, the historical data includes vehicle motion information, environmental information and user's vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent changes in the vehicle's motion parameters in space.

[0060] Optionally, the device further comprises:

[0061] A data set construction module, used to construct a historical data set based on the vehicle's motion information and environmental information;

[0062] A decision learning module, for simulating the decision-making process and search process of the capuchin monkey when facing multiple choices based on the historical data set to obtain a first machine learning model and a second machine learning model respectively;

[0063] A role determination module is used to determine the first machine learning model as a leader in the capuchin decision-making mechanism, and determine the second machine learning model as a follower in the capuchin decision-making mechanism.

[0064] Optionally, the device further comprises:

[0065] A region determination module, for simulating the capuchin monkey decision-making mechanism when the satellite signal is not lost, the leader predicts the target region where the vehicle is located based on the historical data of the vehicle and global information; the global information includes the satellite signal;

[0066] The position determination module is used for the follower to perform intensive search in the target area to determine the target position of the vehicle in the target area.

[0067] The auxiliary positioning device further described can also execute the auxiliary positioning method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0068] Figure 4A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0069] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0070] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0071] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as an auxiliary positioning method.

[0072] In some embodiments, the assisted positioning method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the assisted positioning method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to perform the assisted positioning method in any other appropriate manner (for example, by means of firmware).

[0073] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0075] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A 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. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0076] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0077] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0078] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0079] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0080] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An auxiliary positioning method, characterized in that: The method comprises: Simulating the capuchin monkey decision-making mechanism when satellite signals are lost, the leader predicts the target area where the vehicle is located based on the real-time feedback information from the vehicle; The follower performs an intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

2. The method according to claim 1, characterized in that The real-time feedback information includes historical data and a vehicle dynamic model. The leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle, including: The leader determines the possible position and movement trajectory of the vehicle based on the real-time feedback information of the vehicle; The leader predicts a target area where the vehicle is located based on the possible positions and movement trajectories.

3. The method according to claim 2, characterized in that The follower performs intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored, including: The follower performs intensive search in the target area to obtain candidate locations; If the candidate position meets the preset positioning accuracy, the candidate position is determined as the target position of the vehicle in the prediction area; If the candidate position does not meet the preset positioning accuracy, the search results of the intensive search are added to the real-time feedback information and returned to the execution leader to predict the target area where the vehicle is located based on the real-time feedback information of the vehicle until the candidate position meets the preset positioning accuracy or the satellite signal is restored.

4. The method according to claim 2, characterized in that: The historical data includes the vehicle's motion information, environmental information and the user's vehicle usage habits; the vehicle dynamic model is constructed based on the vehicle's kinematics and is used to represent the changes in the vehicle's motion parameters in space.

5. The method according to claim 1, characterized in that Before the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle, the method further includes: Construct a historical data set based on the vehicle's motion information and environmental information; Based on the historical data set, the decision-making process and the search process of the capuchin monkey when facing multiple choices are simulated to obtain a first machine learning model and a second machine learning model respectively; The first machine learning model is determined as a leader in a capuchin decision-making mechanism, and the second machine learning model is determined as a follower in a capuchin decision-making mechanism.

6. The method according to claim 1, characterized in that The method further comprises: Simulating the capuchin monkey decision-making mechanism when the satellite signal is not lost, the leader predicts the target area where the vehicle is located based on the historical data of the vehicle and global information; the global information includes the satellite signal; The follower performs an intensive search in the target area to determine the target position of the vehicle in the target area.

7. An auxiliary positioning device, characterized in that: The device comprises: A search start module, used to simulate the capuchin monkey decision-making mechanism when the satellite signal is lost, where the leader predicts the target area where the vehicle is located based on the real-time feedback information of the vehicle; The search end module is used for the follower to perform an intensive search in the target area until the target position of the vehicle in the target area is determined or the satellite signal is restored.

8. The device according to claim 7, characterized in that The real-time feedback information includes historical data and a vehicle dynamic model, and the search start module includes: A position trajectory estimation unit, used for the leader to determine the possible position and movement trajectory of the vehicle according to the real-time feedback information of the vehicle; The area division unit is used for the leader to predict the target area where the vehicle is located based on the possible position and movement trajectory.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the assisted positioning method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the auxiliary positioning method according to any one of claims 1 to 6 when executed.

Images