Vehicle cross-scene path planning method, device and equipment based on UWB and V2X

Abstract

This invention discloses a method, apparatus, and device for cross-scene vehicle path planning based on UWB and V2X, comprising: acquiring satellite positioning data, UWB positioning data, and INS pose data of the current vehicle; determining the current scene of the current vehicle based on the satellite positioning data and UWB positioning data; fusing the satellite positioning data, UWB positioning data, and INS pose data using an extended Kalman filter algorithm based on the current scene to obtain the fused pose information of the current vehicle; acquiring high-precision map data and real-time traffic information of the area where the current vehicle is located based on V2X; and performing path planning based on the high-precision map data, real-time traffic information, and fused pose information to obtain the optimal driving path of the current vehicle in the current scene. This invention improves the accuracy and reliability of vehicle path planning and enhances the user's driving experience, and can be applied to the field of path planning technology.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method, apparatus and equipment for cross-scenario path planning of vehicles based on UWB and V2X. Background Technology

[0002] Vehicle cross-scenario path planning refers to a continuous path planning technology that covers multiple scenarios such as outdoor open roads and indoor enclosed parking lots. The goal is to achieve seamless connection of path planning when switching scenarios without the need for manual intervention to replan.

[0003] Current vehicle cross-scenario path planning solutions have the following drawbacks: breakpoints are prone to occur when switching between indoor and outdoor positioning, and positioning jumps affect the accuracy of vehicle cross-scenario path planning; existing solutions mostly only cover a single indoor or outdoor scenario, lacking seamless integration throughout the entire process, requiring manual triggering by the user after scenario switching, which is inefficient; and the lack of sufficient integration of multi-source data to optimize path and positioning accuracy makes it difficult to meet the needs of seamless travel in all scenarios, thus restricting the scenario coverage capability of intelligent driving.

[0004] The above problems urgently need to be addressed. Summary of the Invention

[0005] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0006] Therefore, one objective of this invention is to provide a vehicle cross-scene path planning method based on UWB and V2X. This method determines the current scene based on satellite positioning data and UWB positioning data. Based on the current scene, it fuses satellite positioning data, UWB positioning data, and INS pose data using an extended Kalman filter algorithm to obtain fused pose information. Combined with high-precision map data and real-time traffic information obtained based on V2X communication, path planning is performed to obtain the optimal driving path in the current scene. This realizes vehicle cross-scene path planning under indoor and outdoor scene switching, improves the accuracy and reliability of vehicle path planning, and also improves the user's driving experience.

[0007] Another objective of this invention is to provide a vehicle cross-scenario path planning device based on UWB and V2X.

[0008] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a vehicle cross-scenario path planning method based on UWB and V2X, including the following steps: Acquire the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; The current scene of the vehicle is determined based on the satellite positioning data and the UWB positioning data. Based on the current scene, the satellite positioning data, the UWB positioning data and the INS pose data are fused using an extended Kalman filter algorithm to obtain the fused pose information of the vehicle. High-precision map data and real-time traffic information of the area where the vehicle is currently located are obtained based on V2X; Based on the high-precision map data, the real-time traffic information, and the fused pose information, path planning is performed to obtain the optimal driving path for the current vehicle in the current scenario; The current scene is one of the following: an indoor scene, an outdoor scene, and an indoor-outdoor transition scene.

[0009] Furthermore, in one embodiment of the present invention, the acquisition of the current vehicle's satellite positioning data, UWB positioning data, and INS pose data specifically includes: The satellite positioning data of the current vehicle is obtained through the vehicle-mounted GNSS module; The vehicle communicates with UWB base stations deployed in indoor areas via the vehicle-mounted UWB module to obtain the UWB positioning data of the current vehicle. The angular velocity and linear acceleration data of the current vehicle are collected by the inertial measurement unit, and the INS pose data of the current vehicle is calculated based on the angular velocity data, the linear acceleration data and the initial position information of the current vehicle.

[0010] Furthermore, in one embodiment of the present invention, determining the current scene of the current vehicle based on the satellite positioning data and the UWB positioning data specifically includes: Determine the positioning accuracy factor of the satellite positioning data, and determine the ranging response signal strength corresponding to the UWB positioning data; When the positioning accuracy factor is greater than or equal to a preset first threshold and the ranging response signal strength is greater than or equal to a preset second threshold, it is determined that the current vehicle is in an indoor scene; When the positioning accuracy factor is less than or equal to a preset third threshold and the ranging response signal strength is less than or equal to a preset fourth threshold, it is determined that the current vehicle is in an outdoor scene. When the positioning accuracy factor is less than the first threshold and the ranging response signal strength is greater than the fourth threshold, or when the positioning accuracy factor is greater than the third threshold and the ranging response signal strength is less than the second threshold, it is determined that the current vehicle is in an indoor-outdoor transition scenario.

[0011] Furthermore, in one embodiment of the present invention, the step of fusing the satellite positioning data, the UWB positioning data, and the INS pose data according to the current scene using an extended Kalman filter algorithm to obtain the fused pose information of the current vehicle specifically includes: The data fusion weights of the satellite positioning data, the UWB positioning data, and the INS pose data are determined based on the current scenario, and the corresponding observation noise covariance matrix is ​​determined based on the data fusion weights. Based on the observation noise covariance matrix, the satellite positioning data, the UWB positioning data, and the INS pose data are fused using the extended Kalman filter algorithm to obtain the fused pose information of the current vehicle.

[0012] Furthermore, in one embodiment of the present invention, determining the data fusion weights of the satellite positioning data, the UWB positioning data, and the INS pose data based on the current scene specifically includes: The data fusion weights of the INS pose data are determined according to preset fixed values; When the current scene is an indoor scene, the data fusion weight of the satellite positioning data is determined to be a preset first lower limit value, and the data fusion weight of the UWB positioning data is determined to be a preset second upper limit value; When the current scene is an outdoor scene, the data fusion weight of the satellite positioning data is determined to be a preset first upper limit value, and the data fusion weight of the UWB positioning data is determined to be a preset second lower limit value; When the current scenario is an indoor-outdoor transition scenario, the data fusion weight of the satellite positioning data is dynamically determined based on the positioning accuracy factor between the first lower limit and the first upper limit, and the data fusion weight of the UWB positioning data is dynamically determined based on the ranging response signal strength between the second lower limit and the second upper limit.

[0013] Furthermore, in one embodiment of the present invention, the step of acquiring high-precision map data and real-time traffic information of the area where the current vehicle is located based on V2X specifically includes: The vehicle communicates with roadside equipment in the area where it is located via its V2X communication module to obtain the high-precision map data and the real-time traffic information.

[0014] Furthermore, in one embodiment of the present invention, the step of performing path planning based on the high-precision map data, the real-time traffic information, and the fused pose information to obtain the optimal driving path for the current vehicle in the current scenario specifically includes: The starting point is determined based on the fused pose information, and the destination, as well as lane information and obstacle information, are determined based on the high-precision map data. Based on the vehicle lane information, the obstacle information, and the real-time traffic information, path planning is performed to obtain the optimal driving route between the starting point and the destination.

[0015] On the other hand, embodiments of the present invention provide a vehicle cross-scenario path planning device based on UWB and V2X, comprising: The positioning data acquisition module is used to acquire the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; The positioning data fusion module is used to determine the current scene of the current vehicle based on the satellite positioning data and the UWB positioning data, and to fuse the satellite positioning data, the UWB positioning data and the INS pose data based on the current scene using an extended Kalman filter algorithm to obtain the fused pose information of the current vehicle. The V2X communication module is used to obtain high-precision map data and real-time traffic information of the area where the vehicle is currently located based on V2X. The path planning module is used to perform path planning based on the high-precision map data, the real-time traffic information, and the fused pose information to obtain the optimal driving path of the current vehicle in the current scenario. The current scene is one of the following: an indoor scene, an outdoor scene, and an indoor-outdoor transition scene.

[0016] On the other hand, embodiments of the present invention provide an electronic device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-described vehicle cross-scene path planning method based on UWB and V2X.

[0017] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described vehicle cross-scenario path planning method based on UWB and V2X.

[0018] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle cross-scenario path planning method based on UWB and V2X.

[0019] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention acquires satellite positioning data, UWB positioning data, and INS pose data of the current vehicle. Based on the satellite and UWB positioning data, the current scene of the vehicle is determined. Then, using an extended Kalman filter algorithm, the satellite positioning data, UWB positioning data, and INS pose data are fused to obtain the fused pose information of the current vehicle. High-precision map data and real-time traffic information of the area where the vehicle is located are acquired via V2X. Path planning is then performed based on the high-precision map data, real-time traffic information, and fused pose information to obtain the optimal driving path for the current vehicle in the current scene. This invention achieves cross-scene path planning for vehicles switching between indoor and outdoor scenes, improving the accuracy and reliability of vehicle path planning and enhancing the user's driving experience. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the steps of a vehicle cross-scenario path planning method based on UWB and V2X provided in an embodiment of the present invention; Figure 2 A structural block diagram of a vehicle cross-scene path planning device based on UWB and V2X provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0024] The vehicle cross-scenario path planning method based on UWB and V2X provided in this invention can be applied to terminals, servers, or software running on either terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle cross-scenario path planning method based on UWB and V2X, but is not limited to the above forms.

[0025] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] It should be noted that in various specific embodiments of the present invention, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user parking space location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of the present invention require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of the embodiments of the present invention acquired.

[0027] Reference Figure 1 This invention provides a vehicle cross-scenario path planning method based on UWB and V2X, specifically including the following steps: S101. Obtain the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; S102. Determine the current scene of the vehicle based on satellite positioning data and UWB positioning data. Based on the current scene, fuse the satellite positioning data, UWB positioning data and INS pose data using an extended Kalman filter algorithm to obtain the fused pose information of the vehicle. S103. Obtain high-precision map data and real-time traffic information of the area where the vehicle is currently located based on V2X; S104. Based on high-precision map data, real-time traffic information, and fused pose information, perform path planning to obtain the optimal driving path for the current vehicle in the current scenario. The current scene is one of the following: an indoor scene, an outdoor scene, or an indoor-outdoor transition scene.

[0028] This invention determines the current scene based on satellite positioning data and UWB positioning data. Based on the current scene, the extended Kalman filter algorithm is used to fuse the satellite positioning data, UWB positioning data, and INS pose data to obtain fused pose information. Combined with high-precision map data and real-time traffic information obtained based on V2X communication, path planning is performed to obtain the optimal driving path in the current scene. This realizes cross-scene path planning for vehicles when switching between indoor and outdoor scenes, improves the accuracy and reliability of vehicle path planning, and also enhances the user's driving experience.

[0029] As a further optional implementation, the system acquires the current vehicle's satellite positioning data, UWB positioning data, and INS pose data, specifically including: S1011. Obtain the current vehicle's satellite positioning data through the vehicle-mounted GNSS module; S1012. Communicate with the UWB base station deployed in the indoor area through the vehicle-mounted UWB module to obtain the current vehicle's UWB positioning data; S1013. The angular velocity and linear acceleration data of the current vehicle are collected by the inertial measurement unit, and the INS pose data of the current vehicle is calculated based on the angular velocity data, linear acceleration data and the initial position information of the current vehicle.

[0030] Specifically, embodiments of the present invention acquire multi-source positioning data from three types of hardware modules installed in the vehicle: 1) Satellite positioning data: acquired through a vehicle-mounted GNSS receiver, including latitude and longitude, altitude, positioning accuracy factor (PDOP), number of satellites, and other information in the WGS-84 coordinate system; 2) UWB positioning data: By communicating with UWB base stations deployed in indoor scenes through vehicle-mounted UWB tags, the distance or coordinate data of the tag relative to the base station can be obtained; 3) INS pose data: Angular velocity and linear acceleration data are collected by the inertial measurement unit (IMU), and the real-time attitude (roll angle, pitch angle, yaw angle) and position changes of the vehicle are calculated by combining the initial position information.

[0031] As a further optional implementation, the current scene of the current vehicle is determined based on satellite positioning data and UWB positioning data, specifically including: S1021. Determine the positioning accuracy factor of the satellite positioning data and determine the ranging response signal strength corresponding to the UWB positioning data; S1022. When the positioning accuracy factor is greater than or equal to the preset first threshold and the ranging response signal strength is greater than or equal to the preset second threshold, it is determined that the current vehicle is in an indoor scene. S1023. When the positioning accuracy factor is less than or equal to the preset third threshold and the ranging response signal strength is less than or equal to the preset fourth threshold, it is determined that the current vehicle is in an outdoor scene. S1024. When the positioning accuracy factor is less than the first threshold and the ranging response signal strength is greater than the fourth threshold, or when the positioning accuracy factor is greater than the third threshold and the ranging response signal strength is less than the second threshold, it is determined that the current vehicle is in an indoor-outdoor transition scenario.

[0032] Specifically, this embodiment of the invention completes scene recognition based on the characteristics of UWB positioning data and satellite positioning data, and achieves more accurate scene judgment by combining the signal states of the two types of data: 1) When the positioning accuracy factor is greater than or equal to 6 and the ranging response signal strength is greater than or equal to -80dBm, it is determined that the current vehicle is in an indoor scene; 2) When the positioning accuracy factor is less than or equal to 3 and the ranging response signal strength is less than or equal to -100dBm, it is determined that the current vehicle is in an outdoor scene; 3) When the positioning accuracy factor is less than 6 and the ranging response signal strength is greater than -100dBm, or when the positioning accuracy factor is greater than 3 and the ranging response signal strength is less than -80dBm, it is determined that the current vehicle is in an indoor-outdoor transition scenario.

[0033] As a further optional implementation, satellite positioning data, UWB positioning data, and INS pose data are fused using an extended Kalman filter algorithm based on the current scenario to obtain the fused pose information of the current vehicle, specifically including: S1025. Determine the data fusion weights of satellite positioning data, UWB positioning data, and INS pose data based on the current scenario, and determine the corresponding observation noise covariance matrix based on the data fusion weights. S1026. Based on the observation noise covariance matrix, the satellite positioning data, UWB positioning data, and INS pose data are fused using the extended Kalman filter algorithm to obtain the fused pose information of the current vehicle.

[0034] Specifically, the observation noise covariance matrix of the extended Kalman filter algorithm is set according to different scenarios, thereby adjusting the weights of each data source: 1) Indoor scenario: Reduce the data fusion weight of satellite positioning data and increase the noise weight of satellite positioning data in the observation noise covariance matrix. This mainly relies on the high-precision position data of UWB and the high-frequency attitude data of INS to suppress the drift error of INS. 2) Outdoor scenarios: Reduce the data fusion weight of UWB positioning data, increase the noise weight of UWB positioning data in the observation noise covariance matrix, fuse high-frequency dynamic data of INS, and correct the signal delay problem of satellite positioning data; 3) Indoor-outdoor transition scenario: Dynamically adjust the weight of each data source. For example, as the signal of satellite positioning data gradually strengthens, gradually reduce the data fusion weight of UWB positioning data.

[0035] Based on the observation noise covariance matrix, the extended Kalman filter algorithm is used to fuse satellite positioning data, UWB positioning data, and INS pose data to output vehicle fused pose information in a unified coordinate system, including three-dimensional position, three-dimensional attitude, and corresponding accuracy estimates.

[0036] As an optional implementation, the data fusion weights for satellite positioning data, UWB positioning data, and INS pose data are determined based on the current scenario, specifically including: S10251. Determine the data fusion weights of the INS pose data according to preset fixed values; S10252. When the current scene is an indoor scene, determine the data fusion weight of the satellite positioning data to a preset first lower limit value, and determine the data fusion weight of the UWB positioning data to a preset second upper limit value; S10253. When the current scene is an outdoor scene, determine the data fusion weight of the satellite positioning data to a preset first upper limit value, and determine the data fusion weight of the UWB positioning data to a preset second lower limit value; S10254. When the current scenario is an indoor-outdoor transition scenario, the data fusion weight of satellite positioning data is dynamically determined based on the positioning accuracy factor between the first lower limit and the first upper limit, and the data fusion weight of UWB positioning data is dynamically determined based on the ranging response signal strength between the second lower limit and the second upper limit.

[0037] Specifically, the observation noise covariance matrix is ​​a parameter matrix used in the extended Kalman filter (EKF) to measure the observation error of each data source. It is inversely related to the weight of the data source: the smaller the element value of the corresponding data source in the matrix, the higher the weight of that data source in the fusion process; the larger the element value, the lower the weight.

[0038] In this embodiment of the invention, the observation noise covariance matrix is ​​a 3×3 diagonal matrix, and the elements on the diagonal correspond to the observation noise variance of the three types of data sources, respectively. The specific adjustment logic is as follows: 1) Indoor Scene If satellite positioning data signals are lost or have large errors, the corresponding observation noise variance is set to a larger value, and the corresponding data fusion weight is set to the preset first lower limit value. UWB positioning data signals have small stability errors, corresponding to a small observation noise variance, and the corresponding data fusion weight is a preset second upper limit value. The INS attitude data is high-frequency and stable, the corresponding observation noise variance is set to a moderate value, and the corresponding data fusion weight is a preset fixed value.

[0039] 2) Outdoor scenes The satellite positioning data signal has a small stability error, so the corresponding observation noise variance is set to a small value, and the corresponding data fusion weight is set to a preset first upper limit value. If the UWB positioning data signal is lost or the error is large, the corresponding observation noise variance is set to a larger value, and the corresponding data fusion weight is set to the preset second lower limit value. The INS attitude data is high-frequency and stable, the corresponding observation noise variance is set to a moderate value, and the corresponding data fusion weight is a preset fixed value.

[0040] 3) Indoor-outdoor transition scenarios The fluctuation of satellite positioning data signal corresponds to dynamic adjustment of observation noise variance, and the data fusion weight of satellite positioning data is dynamically determined based on the positioning accuracy factor between the first lower limit and the first upper limit. UWB positioning data signal fluctuations correspond to dynamic adjustment of observation noise variance. The data fusion weight of UWB positioning data is dynamically determined based on the ranging response signal strength between the second lower limit and the second upper limit. The INS attitude data remained stable, the corresponding observation noise variance remained at a moderate value, and the corresponding data fusion weights were preset fixed values.

[0041] In the update step of the extended Kalman filter, the system calculates the Kalman gain matrix based on the observation noise covariance matrix and the prediction error covariance matrix. The elements of the Kalman gain matrix represent the fusion weights of the various data sources. Kalman gain matrix = prediction error covariance matrix × inverse of observation noise covariance matrix × (inverse of prediction error covariance matrix × inverse of observation noise covariance matrix + identity matrix); The final fusion result = prediction result + Kalman gain matrix × (observation result - prediction result).

[0042] The larger the element value in the Kalman gain matrix, the greater the contribution (higher weight) of the corresponding data source to the fusion result. By adjusting the observation noise covariance matrix, the weight of each data source in the fusion process can be flexibly controlled, thereby achieving the optimal fusion effect in different scenarios.

[0043] As a further optional implementation, high-precision map data and real-time traffic information of the area where the vehicle is currently located can be obtained based on V2X, specifically as follows: The vehicle communicates with roadside equipment in the area where it is located via its V2X communication module to obtain high-precision map data and real-time traffic information.

[0044] Specifically, the vehicle communicates with roadside equipment in the area where it is located via its V2X communication module to obtain the following two types of key data: High-precision map data includes static road information with centimeter-level accuracy, such as lane line positions, road curvature, traffic signs, and intersection structures, as well as indoor scene data such as parking space layout and passageway orientation within parking lots. Real-time traffic information includes the location, speed, and direction of travel of surrounding vehicles. In outdoor scenarios, it also includes dynamic environmental information such as traffic light status, road construction areas, and congested road sections.

[0045] As an optional implementation, path planning is performed based on high-precision map data, real-time traffic information, and fused pose information to obtain the optimal driving path for the current vehicle in the current scenario. This specifically includes: S1041. Determine the starting point based on the fused pose information, and determine the destination, as well as the information on motor vehicle lanes and obstacles, based on the high-precision map data; S1042. Based on the information of motor vehicle lanes, obstacles, and real-time traffic information, perform route planning to obtain the optimal driving route between the origin and the destination.

[0046] Specifically, starting with fused pose information, and combining high-precision maps and real-time traffic information, an optimal path is generated through a path planning algorithm: Indoor scenarios (such as parking lots): Prioritize the shortest path while avoiding parking spaces and obstacles, and plan the optimal driving route from the current location to the target parking space or exit, taking into account both driving safety and efficiency. Outdoor scenario: Taking into account factors such as road speed limits, traffic congestion, and traffic light cycles, the optimal route that complies with traffic rules is planned with the goal of minimizing travel time, travel distance, or energy consumption. Indoor-outdoor transition scenario: Achieve seamless path connection. When about to leave the parking lot, plan the connection route with the outdoor road in advance to avoid parking or congestion at the entrance and exit.

[0047] After path planning is completed, the optimal driving path containing lane-level navigation information is output to provide a basis for vehicle decision-making and control.

[0048] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention determine the current scene based on satellite positioning data and UWB positioning data. Based on the current scene, the satellite positioning data, UWB positioning data, and INS pose data are fused using an extended Kalman filter algorithm to obtain fused pose information. Combined with high-precision map data obtained based on V2X communication and real-time traffic information, path planning is performed to obtain the optimal driving path for the current scene. This achieves cross-scene path planning for vehicles switching between indoor and outdoor scenes, improving the accuracy and reliability of vehicle path planning and enhancing the user's driving experience.

[0049] Reference Figure 2 This invention provides a vehicle cross-scenario path planning device based on UWB and V2X, comprising: The positioning data acquisition module is used to acquire the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; The positioning data fusion module is used to determine the current scene of the vehicle based on satellite positioning data and UWB positioning data. Based on the current scene, the extended Kalman filter algorithm is used to fuse the satellite positioning data, UWB positioning data and INS pose data to obtain the fused pose information of the current vehicle. The V2X communication module is used to obtain high-precision map data and real-time traffic information of the area where the vehicle is currently located based on V2X. The path planning module is used to plan the path based on high-precision map data, real-time traffic information and fused pose information to obtain the optimal driving path for the current vehicle in the current scenario. The current scene is one of the following: an indoor scene, an outdoor scene, or an indoor-outdoor transition scene.

[0050] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0051] Reference Figure 3 This invention provides an electronic device, comprising: At least one processor; At least one memory for storing at least one program; When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned vehicle cross-scene path planning method based on UWB and V2X.

[0052] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0053] This invention also provides a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the aforementioned vehicle cross-scenario path planning method based on UWB and V2X.

[0054] This invention provides a computer-readable storage medium that can execute a vehicle cross-scenario path planning method based on UWB and V2X provided in the method embodiments of this invention. It can execute any combination of implementation steps of the method embodiments and has the corresponding functions and beneficial effects of the method.

[0055] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle cross-scenario path planning method based on UWB and V2X.

[0056] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0057] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0058] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0059] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention 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 embodiments of the invention 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 a 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.

[0060] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0061] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0062] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0064] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0065] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0066] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0067] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0068] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for vehicle cross-scenario path planning based on UWB and V2X, characterized in that, Includes the following steps: Acquire the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; The current scene of the vehicle is determined based on the satellite positioning data and the UWB positioning data. Based on the current scene, the satellite positioning data, the UWB positioning data and the INS pose data are fused using an extended Kalman filter algorithm to obtain the fused pose information of the vehicle. High-precision map data and real-time traffic information of the area where the vehicle is currently located are obtained based on V2X; Based on the high-precision map data, the real-time traffic information, and the fused pose information, path planning is performed to obtain the optimal driving path for the current vehicle in the current scenario; The current scene is one of the following: an indoor scene, an outdoor scene, and an indoor-outdoor transition scene.

2. The vehicle cross-scene path planning method based on UWB and V2X according to claim 1, characterized in that, The acquisition of the current vehicle's satellite positioning data, UWB positioning data, and INS pose data specifically includes: The satellite positioning data of the current vehicle is obtained through the vehicle-mounted GNSS module; The vehicle communicates with UWB base stations deployed in indoor areas via an onboard UWB module to obtain the UWB positioning data of the current vehicle. The angular velocity and linear acceleration data of the current vehicle are collected by the inertial measurement unit, and the INS pose data of the current vehicle is calculated based on the angular velocity data, the linear acceleration data and the initial position information of the current vehicle.

3. The vehicle cross-scene path planning method based on UWB and V2X according to claim 1, characterized in that, Determining the current scene of the current vehicle based on the satellite positioning data and the UWB positioning data specifically includes: Determine the positioning accuracy factor of the satellite positioning data, and determine the ranging response signal strength corresponding to the UWB positioning data; When the positioning accuracy factor is greater than or equal to a preset first threshold and the ranging response signal strength is greater than or equal to a preset second threshold, it is determined that the current vehicle is in an indoor scene; When the positioning accuracy factor is less than or equal to a preset third threshold and the ranging response signal strength is less than or equal to a preset fourth threshold, it is determined that the current vehicle is in an outdoor scene. When the positioning accuracy factor is less than the first threshold and the ranging response signal strength is greater than the fourth threshold, or when the positioning accuracy factor is greater than the third threshold and the ranging response signal strength is less than the second threshold, it is determined that the current vehicle is in an indoor-outdoor transition scenario.

4. The vehicle cross-scene path planning method based on UWB and V2X according to claim 3, characterized in that, The step of fusing the satellite positioning data, the UWB positioning data, and the INS pose data using an extended Kalman filter algorithm based on the current scene to obtain the fused pose information of the current vehicle specifically includes: The data fusion weights of the satellite positioning data, the UWB positioning data, and the INS pose data are determined based on the current scenario, and the corresponding observation noise covariance matrix is ​​determined based on the data fusion weights. Based on the observation noise covariance matrix, the satellite positioning data, the UWB positioning data, and the INS pose data are fused using the extended Kalman filter algorithm to obtain the fused pose information of the current vehicle.

5. The UWB and V2X based vehicle cross-scene path planning method according to claim 4, characterized in that, The step of determining the data fusion weights of the satellite positioning data, the UWB positioning data, and the INS pose data based on the current scenario specifically includes: The data fusion weights of the INS pose data are determined according to preset fixed values; When the current scene is an indoor scene, the data fusion weight of the satellite positioning data is determined to be a preset first lower limit value, and the data fusion weight of the UWB positioning data is determined to be a preset second upper limit value; When the current scene is an outdoor scene, the data fusion weight of the satellite positioning data is determined to be a preset first upper limit value, and the data fusion weight of the UWB positioning data is determined to be a preset second lower limit value; When the current scenario is an indoor-outdoor transition scenario, the data fusion weight of the satellite positioning data is dynamically determined based on the positioning accuracy factor between the first lower limit and the first upper limit, and the data fusion weight of the UWB positioning data is dynamically determined based on the ranging response signal strength between the second lower limit and the second upper limit.

6. The vehicle cross-scenario path planning method based on UWB and V2X according to claim 1, characterized in that, The specific steps of acquiring high-precision map data and real-time traffic information of the current vehicle's location based on V2X are as follows: The vehicle communicates with roadside equipment in the area where it is located via its V2X communication module to obtain the high-precision map data and the real-time traffic information.

7. The UWB and V2X based vehicle cross-scenario path planning method according to any one of claims 1 to 6, characterized in that, The step of performing path planning based on the high-precision map data, the real-time traffic information, and the fused pose information to obtain the optimal driving path for the current vehicle in the current scenario specifically includes: The starting point is determined based on the fused pose information, and the destination, as well as lane information and obstacle information, are determined based on the high-precision map data. Based on the vehicle lane information, the obstacle information, and the real-time traffic information, path planning is performed to obtain the optimal driving route between the starting point and the destination.

8. A vehicle cross-scene path planning device based on UWB and V2X, characterized in that, include: The positioning data acquisition module is used to acquire the current vehicle's satellite positioning data, UWB positioning data, and INS pose data; The positioning data fusion module is used to determine the current scene of the current vehicle based on the satellite positioning data and the UWB positioning data, and to fuse the satellite positioning data, the UWB positioning data and the INS pose data based on the current scene using an extended Kalman filter algorithm to obtain the fused pose information of the current vehicle. The V2X communication module is used to obtain high-precision map data and real-time traffic information of the area where the vehicle is currently located based on V2X. The path planning module is used to perform path planning based on the high-precision map data, the real-time traffic information, and the fused pose information to obtain the optimal driving path of the current vehicle in the current scenario. The current scene is one of the following: an indoor scene, an outdoor scene, and an indoor-outdoor transition scene.

9. An electronic device, comprising: include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a vehicle cross-scene path planning method based on UWB and V2X as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a vehicle cross-scenario path planning method based on UWB and V2X as described in any one of claims 1 to 7.

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