Vehicle positioning method, and electronic device and storage medium

Abstract

Disclosed in the present application are a vehicle positioning method, and an electronic device and a storage medium. The method comprises: acquiring first sensing information sent by a vehicle and second sensing information sent by a road-end device, wherein the road-end device is located on a road on which the vehicle is traveling; on the basis of a first collection time of the first sensing information and a second collection time of the second sensing information, performing spatial-temporal alignment on the first sensing information and the second sensing information, so as to obtain aligned first sensing information and aligned second sensing information; and on the basis of the aligned first sensing information and the aligned second sensing information, performing positioning on the vehicle, so as to obtain a positioning result of the vehicle. By means of the present application, the technical problem in the related art of vehicle positioning accuracy being relatively low is solved.

Description

Vehicle positioning method, electronic device and storage medium Technical Field

[0001] The present application relates to the field of intelligent network technology, and more specifically, to a vehicle positioning method, electronic equipment, and storage medium. Background Art

[0002] With the rapid development of intelligent connected technology, the practical application of autonomous vehicles is gradually expanding. Traditional vehicle positioning methods generally rely on the Global Positioning System (GPS). However, in complex environments, GPS signals are easily interfered with and blocked, resulting in low vehicle positioning accuracy.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide a vehicle positioning method, an electronic device, and a storage medium to at least solve the technical problem of low vehicle positioning accuracy in related technologies.

[0006] According to one aspect of an embodiment of the present application, a vehicle positioning method is provided, including: acquiring first perception information sent by a vehicle and second perception information sent by a road-side device, wherein the road-side device is located on a road on which the vehicle is traveling; based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, performing spatiotemporal alignment on the first perception information and the second perception information to obtain aligned first perception information and aligned second perception information; and positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle.

[0007] According to another aspect of an embodiment of the present application, a vehicle positioning method is also provided, including: acquiring a first perception feature sent by a vehicle and a second perception feature sent by a road-side device, wherein the road-side device is located on the road where the vehicle is traveling, the first perception feature is a feature obtained by extracting features from first perception information collected by the vehicle, and the second perception feature is a feature obtained by extracting features from second perception information collected by the road-side device; based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, the first perception feature and the second perception feature are temporally and spatially aligned to obtain an aligned first perception feature and an aligned second perception feature; based on the aligned first perception feature and the aligned second perception feature, the vehicle is positioned to obtain a positioning result of the vehicle.

[0008] According to another aspect of an embodiment of the present application, another vehicle positioning method is provided, including: responding to an input instruction acting on an operation interface, displaying first perception information sent by the vehicle and second perception information sent by a road-side device on the operation interface, wherein the road-side device is located on the road on which the vehicle is traveling; responding to a vehicle instruction acting on the operation interface, displaying a positioning result of the vehicle on the operation interface, wherein the positioning result is a result of positioning the vehicle based on the aligned first perception information and the aligned second perception information, and the aligned first perception information and the aligned second perception information are obtained by spatiotemporally aligning the first perception information and the second perception information based on a first acquisition time of the first perception information and a second acquisition time of the second perception information.

[0009] According to another aspect of an embodiment of the present application, another vehicle positioning method is provided, including: obtaining first perception information and second perception information by calling a first interface, wherein the first interface includes a first parameter, the parameter value of the first parameter is the first perception information and the second perception information, and the road-end equipment is located on the road on which the vehicle is traveling; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle; and the positioning result of the vehicle is output by calling a second interface, wherein the second interface includes a second parameter, the parameter value of the second parameter is the positioning result of the vehicle.

[0010] According to another aspect of an embodiment of the present application, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein any one of the above methods is executed when the program is running.

[0011] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above methods.

[0012] In an embodiment of the present application, first perception information sent by a vehicle and second perception information sent by a roadside device are obtained; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle. It is easy to notice that by temporally and spatially aligning the first perception information and the second perception information and using the aligned first perception information and the aligned second perception information to position the vehicle, compensation for communication delay is achieved, so that in the process of collaborative positioning using the first perception information collected by the vehicle and the second perception information collected by the roadside device, a more accurate positioning result can be obtained, thereby improving the vehicle positioning accuracy, thereby solving the technical problem of low vehicle positioning accuracy in related technologies.

[0013] It is easy to notice that the above general description and the following detailed description are for the purpose of exemplifying and explaining the present application, and do not constitute a limitation to the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] FIG1 is a schematic diagram of an application scenario of a vehicle positioning method according to an embodiment of the present application;

[0016] FIG2 is a flow chart of a vehicle positioning method according to Example 1 of the present application;

[0017] FIG3 is a schematic diagram of vehicle positioning according to an embodiment of the present application;

[0018] FIG4 is a flow chart of a vehicle positioning method according to Example 2 of the present application;

[0019] FIG5 is a flow chart of a vehicle positioning method according to Example 3 of the present application;

[0020] FIG6 is a flow chart of a vehicle positioning method according to Example 4 of the present application;

[0021] FIG7 is a schematic diagram of a vehicle positioning device according to Example 1 of the present application;

[0022] FIG8 is a schematic diagram of a vehicle positioning device according to Example 2 of the present application;

[0023] FIG9 is a schematic diagram of a vehicle positioning device according to Example 3 of the present application;

[0024] FIG10 is a schematic diagram of a vehicle positioning device according to Example 4 of the present application;

[0025] FIG11 is a structural block diagram of a computer terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0029] Roadside equipment: It can be sensors on the intelligent connected road section where the vehicle is currently traveling, for example, cameras, lidar and other equipment on the road section where the vehicle is currently traveling.

[0030] Deep neural network: a machine learning technology with a network structure composed of multiple layers of neurons, used to process and analyze large amounts of data and capable of performing tasks such as feature extraction, classification, and prediction.

[0031] Feature extraction: It can be to extract representative features from raw data to facilitate machine learning algorithms to analyze and process the data.

[0032] Feature fusion: It can be the merging of feature information from different sources, types or forms to obtain a more complete and accurate feature representation.

[0033] RTK (Real Time Kinematic, carrier phase differential technology) positioning: A technology that can use the carrier phase information provided by the global satellite navigation system to perform high-precision positioning.

[0034] Transformer model: It can be a deep learning network based on the self-attention mechanism, which is often used in natural language processing and computer vision tasks.

[0035] Caffe (Convolutional Architecture For Fast Feature Embedding, convolutional neural network framework): can be a commonly used deep learning framework.

[0036] PyTorch: It is an open source Python machine learning library based on Torch, which can be used for natural language processing. Torch is a data structure containing multi-dimensional tensors, and Python is a language.

[0037] Tensorflow: It can be an artificial intelligence learning system used in many machine learning and deep learning fields such as speech recognition and image recognition.

[0038] Spatiotemporal alignment: This refers to predicting feature information at the same timestamp based on perception information at different timestamps.

[0039] Example 1

[0040] According to an embodiment of the present application, a method embodiment of a vehicle positioning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] In an optional embodiment, the vehicle positioning method described above can be applied to the application scenario shown in FIG1 , but is not limited thereto. FIG1 is a schematic diagram of an application scenario of a vehicle positioning method according to an embodiment of the present application. In the application scenario shown in FIG1 , a vehicle 10 and a roadside device 20 can communicate with a server 40 via a network 30 . The vehicle 10 described above can include, but is not limited to, a variety of different types of sensors, such as cameras, lidars, RTK positioning sensors, and communication devices, and the roadside device 20 can include, but is not limited to, a single type of sensor and communication device. The server 40 described above can perform the following steps in the vehicle positioning method described above: obtaining first perception information sent by the vehicle and second perception information sent by the roadside device, wherein the roadside device is located on the road on which the vehicle is traveling; performing spatiotemporal alignment of the first perception information and the second perception information based on a first acquisition time of the first perception information and a second acquisition time of the second perception information to obtain aligned first perception information and aligned second perception information; and locating the vehicle based on the aligned first perception information and aligned second perception information to obtain a vehicle positioning result.

[0042] In the above operating environment, the present application provides a vehicle positioning method as shown in Figure 2. Figure 2 is a flow chart of the vehicle positioning method according to Example 1 of the present application. As shown in Figure 2, the method includes the following steps:

[0043] Step S202: Acquire first perception information sent by the vehicle and second perception information sent by a road-side device, wherein the road-side device is located on the road on which the vehicle is traveling.

[0044] The above-mentioned first perception information may be information perceived by the vehicle. Optionally, environmental information around the vehicle may be obtained through sensors on the vehicle, wherein the sensors on the vehicle may include cameras, lidars, RTK positioning and other sensors. The environmental information around the vehicle may include information such as the position, speed, shape of other vehicles around the vehicle, as well as information such as the position, speed, posture of the vehicle itself.

[0045] The above-mentioned second perception information can be the perception information collected by the road-side equipment. Specifically, it can include environmental information within the road section where the vehicle is currently traveling, such as lane lines, traffic signs, road conditions, etc., as well as information such as the position, speed and behavior of other vehicles and pedestrians on the road section.

[0046] In an optional embodiment, the position, speed, shape of other vehicles around the vehicle, as well as the position, speed, posture and other information of the vehicle itself can be obtained by various sensors such as cameras, lidars, and RTK positioning on the vehicle, which is also known as first perception information. Furthermore, the lane lines, traffic signs, road conditions and other information within the intelligent connected road section where the vehicle is located can be obtained by sensors such as cameras and lidars, as well as the position, speed and behavior of other vehicles and pedestrians on the road section, which is also known as second perception information. Furthermore, the obtained first perception information and the obtained second perception information can be uploaded to the cloud, where the data can be processed to achieve precise positioning of the vehicle.

[0047] Step S204: Based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information.

[0048] The first acquisition time mentioned above may be the time when the vehicle perceives the first perception information. To facilitate uploading the first acquisition time to the cloud, the first acquisition time may be used as the timestamp of the first perception information. Similarly, the second acquisition time may be used as the timestamp of the second perception information.

[0049] In an optional embodiment, a Transformer architecture, or a spatiotemporal prediction deep model of an architecture such as caffe, PyTorch, or Tensorflow can be used to take the first perception information and the timestamp corresponding to the first perception information, and the second perception information and the timestamp corresponding to the second perception information as input, thereby outputting the first perception information and the second perception information aligned to the same timestamp, i.e., the aligned first perception information and the aligned second perception information. Optionally, the above method helps to compensate for communication delays, thereby obtaining more accurate results in the collaborative positioning process using the first perception information collected by the vehicle and the second perception information collected by the roadside device.

[0050] Step S206: Position the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle.

[0051] In an optional embodiment, after obtaining the aligned first perception information and the aligned second perception information, the aligned first perception information and the aligned second perception information can be fused, and the fusion result can be converted into accurate positioning information of the vehicle in the intelligent connected road section, thereby realizing collaborative positioning using the vehicle side and the road side, and improving the accuracy of vehicle positioning, thereby improving the safety and efficiency of autonomous driving vehicles in application scenarios such as path planning and control strategies of vehicle autonomous driving systems.

[0052] In an embodiment of the present application, first perception information sent by a vehicle and second perception information sent by a roadside device are obtained; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle. It is easy to notice that by temporally and spatially aligning the first perception information and the second perception information and using the aligned first perception information and the aligned second perception information to position the vehicle, compensation for communication delay is achieved, so that in the process of collaborative positioning using the first perception information collected by the vehicle and the second perception information collected by the roadside device, a more accurate positioning result can be obtained, thereby improving the vehicle positioning accuracy, thereby solving the technical problem of low vehicle positioning accuracy in related technologies.

[0053] In the above embodiment of the present application, based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain the aligned first perception information and the aligned second perception information, including: respectively extracting the first perception information and the second perception information to obtain the first perception feature of the first perception information and the second perception feature of the perception information; respectively encoding the first acquisition time and the second acquisition time to obtain the first time feature of the first acquisition time and the second time feature of the second acquisition time; performing temporal and spatial alignment based on the first perception feature, the second perception feature, the first time feature and the second time feature to obtain the aligned first perception feature and the aligned second perception feature; determining the aligned first perception information based on the aligned first perception feature, and determining the aligned second perception information based on the aligned second perception feature.

[0054] In an optional embodiment, after acquiring the first and second perception information, a deep neural network, such as a convolutional neural network or a point cloud processing network, may be used to extract features from the first and second perception information. Optionally, the extracted features may include edges, textures, shapes, key points, and other features. Optionally, the first and second perception features may be uploaded to the cloud via vehicle-to-road communication, thereby achieving spatiotemporal alignment and feature fusion.

[0055] Furthermore, the first acquisition time and the second acquisition time can be encoded respectively to obtain the first time feature of the first acquisition time and the second time feature of the second acquisition time. In this way, in the process of performing the spatiotemporal alignment operation using the spatiotemporal prediction depth model, not only the first perception information collected by the vehicle side and the second perception information collected by the road side equipment are considered, but also the first acquisition time corresponding to the first perception information and the second acquisition time corresponding to the second perception information are considered, thereby realizing compensation for communication delay and improving the accuracy of vehicle positioning.

[0056] In the above embodiment of the present application, spatiotemporal alignment is performed based on the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain the aligned first perceptual feature and the aligned second perceptual feature, including: splicing the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain a spliced ​​feature; and predicting the aligned first perceptual feature and the aligned second perceptual feature based on the spliced ​​feature, wherein the aligned first perceptual feature and the aligned second perceptual feature correspond to the same acquisition time.

[0057] In an optional embodiment, after obtaining the first perception feature, the second perception feature, the first time feature, and the second time feature, the obtained first perception feature and the first time feature, and the second time feature and the second perception feature can be spliced ​​together, or the first perception feature and the second time feature, and the second time feature and the first perception feature can be spliced ​​together to generate a corresponding time series, and this time series can be determined as the spliced ​​feature. Furthermore, the aligned first perception feature and the aligned second perception feature can be predicted based on the time feature, thereby compensating for communication delay and improving the accuracy of vehicle positioning.

[0058] In the above embodiment of the present application, the vehicle is positioned based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle, including: fusing the aligned first perception information and the aligned second perception information to obtain fused information; and positioning the vehicle based on the fused information to obtain a positioning result of the vehicle.

[0059] The above-mentioned fusion information may be the first perception information and the second perception information after time alignment.

[0060] The above positioning result may be the current location of the vehicle.

[0061] In an optional embodiment, the aligned first perception information and the aligned second perception information can be spliced ​​together using a related multimodal deep learning model, and the spliced ​​results can be fused to obtain fused information, thereby enhancing the perception capability of the vehicle positioning system, optimizing the positioning results, and improving the accuracy of the positioning results. The multimodal deep learning model herein can be a model provided in the relevant art, and this application does not specifically limit this.

[0062] In the above embodiment of the present application, the aligned first perception information and the aligned second perception information are fused to obtain fused information, including: determining the point cloud data and visual data in the aligned first perception information and the aligned second perception information; and fusing the point cloud data and the visual data to obtain fused information.

[0063] The above-mentioned point cloud data can be the perception information collected by the laser radar on the vehicle in the aligned first perception information, and the perception information collected by the laser radar on the intelligent connected road section where the vehicle is located in the aligned second perception information.

[0064] The above-mentioned visual data can be the perception information collected by the camera on the vehicle in the aligned first perception information, and the perception information collected by the camera on the intelligent connected road section where the vehicle is located in the aligned second perception information.

[0065] In an optional embodiment, when the aligned first perception information and the aligned second perception information are fused, the point cloud data collected by the lidar on the vehicle and the visual data collected by the camera on the vehicle can be determined from the aligned first perception information, and the point cloud data collected by the lidar on the intelligent connected road section where the vehicle is located and the visual data collected by the camera on the intelligent connected road section where the vehicle is located can be determined from the aligned second perception information, so that the point cloud data and visual data collected by different devices can be fused, and ultimately the accuracy of the positioning results can be improved.

[0066] In the above embodiments of the present application, point cloud data and visual data are fused to obtain fusion information, including: converting point cloud data to a preset space to obtain point cloud features, and converting visual data to a preset space to obtain visual features; encoding point cloud features and visual features to obtain fusion features; and determining fusion information based on the fusion features.

[0067] The aforementioned preset space may be a BEV (Bird's Eye View) feature layer.

[0068] In an optional embodiment, the point cloud data and visual data can be converted to the BEV feature layer through a related multimodal deep learning model to obtain point cloud features and visual features. Furthermore, the BEV feature layer can be used to encode the point cloud features and visual features to obtain fusion features. Optionally, after obtaining the fusion features, corresponding fusion information can be determined based on the fusion features, that is, the first perception information and the second perception information after time alignment are obtained, so that the fusion information can be used to determine the positioning result of the vehicle.

[0069] In the above embodiments of the present application, based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain the aligned first perception information and the aligned second perception information, including: preprocessing the first perception information and the second perception information to obtain first preprocessed information and second preprocessed information; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first preprocessed information and the second preprocessed information are temporally and spatially aligned to obtain the aligned first perception information and the aligned second perception information.

[0070] The above-mentioned preprocessing can be used to eliminate interference factors such as noise, illumination changes, motion blur, etc. in the first perception information and the second perception information, thereby obtaining more accurate data through preprocessing.

[0071] In an optional embodiment, before performing spatiotemporal alignment on the first and second perception information, the first and second perception information may be preprocessed using techniques such as image enhancement, filtering, and denoising to remove interference factors such as noise, illumination variations, and motion blur from the first and second perception information, thereby obtaining first and second preprocessed information. Furthermore, the first and second preprocessed information may be spatiotemporally aligned based on the first acquisition time of the first and second acquisition times of the second perception information.

[0072] In the above embodiment of the present application, obtaining the first perception information sent by the vehicle includes: determining at least one vehicle traveling within a preset range of the vehicle; obtaining the perception information sent by at least one vehicle to obtain the first perception information.

[0073] The above-mentioned preset range can be set by those skilled in the art according to their needs, and the size of the preset range is not specifically limited in this application.

[0074] In an optional embodiment, a preset range can be determined first, and then at least one vehicle can be determined within the preset range, and the perception information collected by the laser radar and camera on the vehicle can be obtained, and the perception information collected by the laser radar and camera on the vehicle can be determined as the first perception information.

[0075] In the above embodiment of the present application, after positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain the positioning result of the vehicle, the method also includes: generating control information or a driving path of the vehicle based on the positioning result of the vehicle; and controlling the vehicle based on the control information or the driving path.

[0076] The above control information can be used to indicate the vehicle's forward direction during driving.

[0077] The above-mentioned driving route can represent the travel information of the vehicle.

[0078] In an optional embodiment, after obtaining the positioning results of the vehicle, corresponding control information and driving paths can be generated based on the positioning results, thereby improving the safety and efficiency of the autonomous driving vehicle in application scenarios such as path planning and control strategies of the autonomous driving system.

[0079] Figure 3 is a schematic diagram of vehicle positioning according to an embodiment of the present application. As shown in Figure 3, the first perception information of the vehicle side and the second perception information of the road side can be collected. After the first perception information and the second perception information are collected, the first perception information and the second perception information can be preprocessed respectively. Furthermore, the features of the preprocessed first perception information and the preprocessed second perception information can be extracted respectively, and the results obtained after feature extraction can be used to align the time and space of the vehicle side data and the road side data, and finally feature fusion can be performed to obtain a fusion result.

[0080] By adopting the embodiments of the present application, the communication delay can be compensated by aligning the first perception information and the second perception information in time and space; in the vehicle positioning process, the positioning accuracy and robustness are improved by fusing the vehicle's own perception information and the perception information of the road-side equipment, so that the positioning error can be reduced in complex scenes and dynamic environments; by effectively fusing laser point cloud data and visual data at the feature level, the perception ability of the positioning system is improved, the positioning results are further optimized, and the adaptability of the positioning system in different scenes and environmental conditions is enhanced.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0082] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0084] Example 2

[0085] According to an embodiment of the present application, a vehicle positioning method is also provided. FIG4 is a flow chart of the vehicle positioning method according to embodiment 2 of the present application. As shown in FIG4 , the method includes the following steps:

[0086] Step S402: Acquire a first perception feature sent by the vehicle and a second perception feature sent by the road-side device, wherein the road-side device is located on the road on which the vehicle is traveling, the first perception feature is a feature obtained by extracting features from the first perception information collected by the vehicle, and the second perception feature is a feature obtained by extracting features from the second perception information collected by the road-side device.

[0087] Step S404: Based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception feature and the second perception feature are temporally and spatially aligned to obtain the aligned first perception feature and the aligned second perception feature.

[0088] Step S406: Positioning the vehicle based on the aligned first perception features and the aligned second perception features to obtain a positioning result of the vehicle.

[0089] In an optional embodiment, after the vehicle collects the first perception information, feature extraction can be performed on the first perception information first, and the first perception feature obtained after feature extraction can be uploaded to the cloud. Alternatively, the vehicle can upload the collected first perception information to the cloud, and then feature extraction can be performed on the first perception information in the cloud to obtain the first perception feature. Concurrently, after the road-side device collects the second perception information, feature extraction can be performed on the second perception information first, and the second perception feature obtained after feature extraction can be uploaded to the cloud. Alternatively, the road-side device can upload the collected second perception information to the cloud, and then feature extraction can be performed on the second perception information in the cloud to obtain the second perception feature.

[0090] Furthermore, after obtaining the first perception feature and the second perception feature, the first perception feature and the second perception feature can be aligned in time and space to obtain the aligned first perception feature and the aligned second perception feature, and the aligned first perception feature and the aligned second perception feature can be used to locate the vehicle, thereby compensating for the communication delay and improving the accuracy of vehicle positioning.

[0091] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0092] Example 3

[0093] According to an embodiment of the present application, another vehicle positioning method is also provided. FIG5 is a flow chart of the vehicle positioning method according to embodiment 3 of the present application. As shown in FIG5 , the method includes the following steps:

[0094] Step S502: In response to an input instruction applied to the operation interface, first perception information sent by the vehicle and second perception information sent by a road-side device are displayed on the operation interface, wherein the road-side device is located on the road on which the vehicle is traveling.

[0095] The above-mentioned operation interface can be the display interface of a computer terminal, or the display interface of a mobile terminal, or the central control display screen on a vehicle. Optionally, this application does not impose any specific restrictions on the type of operation interface.

[0096] The above-mentioned input instructions can be used to instruct the operation interface to display the first perception information sent by the vehicle and the second perception information sent by the road-side equipment. Optionally, the above-mentioned input instructions can be expressed in the form of text display or in the form of voice. This application does not impose specific restrictions on the representation method of the input instructions.

[0097] Step S504: In response to the vehicle instruction acting on the operation interface, the positioning result of the vehicle is displayed on the operation interface, wherein the positioning result is a result of positioning the vehicle based on the aligned first perception information and the aligned second perception information, and the aligned first perception information and the aligned second perception information are obtained by temporally and spatially aligning the first perception information and the second perception information based on the first acquisition time of the first perception information and the second acquisition time of the second perception information.

[0098] The above-mentioned vehicle instructions can be used to instruct the operation interface to display the vehicle positioning results. Optionally, the above-mentioned vehicle instructions can be expressed in the form of text display or in the form of voice. This application does not impose specific restrictions on the representation method of the vehicle instructions.

[0099] In an optional embodiment, when the operation interface receives an input command, the first perception information sent by the vehicle and the second perception information sent by the roadside device can be displayed on the operation interface. Furthermore, the first perception information and the second perception information can be spatiotemporally aligned using the first acquisition time of the first perception information and the second acquisition time of the second perception information, thereby obtaining the aligned first perception information and the aligned second perception information. The positioning result obtained by locating the vehicle can then be determined based on the aligned first perception information and the aligned second perception information. Optionally, when the operation interface receives a vehicle command, the positioning result can be displayed on the operation interface.

[0100] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0101] Example 4

[0102] According to an embodiment of the present application, another vehicle positioning method is also provided. FIG6 is a flow chart of the vehicle positioning method according to embodiment 4 of the present application. As shown in FIG6 , the method includes the following steps:

[0103] Step S602: Obtain first perception information and second perception information by calling a first interface, wherein the first interface includes a first parameter, the parameter value of the first parameter is the first perception information and the second perception information, and the road-end device is located on the road on which the vehicle is traveling.

[0104] The first interface may be a Universal Serial Bus (USB) interface, or may be a virtual data transmission interface.

[0105] The above-mentioned first parameter can be used to represent the first perception information and the second perception information.

[0106] Step S604: Based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information.

[0107] Step S606: Position the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle.

[0108] Step S608: Output the positioning result of the vehicle by calling the second interface, wherein the second interface includes a second parameter, and the parameter value of the second parameter is the positioning result of the vehicle.

[0109] The second interface may be a USB interface, and the first interface may be a virtual data transmission interface.

[0110] The second parameter mentioned above can be used to represent the positioning result of the vehicle.

[0111] In an optional embodiment, the first perception information and the second perception information can be obtained through data transmission via the first interface. Optionally, after obtaining the first perception information and the second perception information, the first perception information and the second perception information can be spatiotemporally aligned based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, thereby obtaining the aligned first perception information and the aligned second perception information. Furthermore, the vehicle can be positioned using the aligned first perception information and the aligned second perception information, and the positioning result generated by positioning the vehicle can be output via the second interface.

[0112] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0113] Example 5

[0114] According to an embodiment of the present application, a device for implementing the above-mentioned vehicle positioning method is also provided. Figure 7 is a schematic diagram of the vehicle positioning device according to Example 1 of the present application. As shown in Figure 7, the device includes: an acquisition module 702, a processing module 704 and a positioning module 706.

[0115] The acquisition module 702 is configured to acquire first perception information transmitted by the vehicle and second perception information transmitted by a roadside device located on the road on which the vehicle is traveling. The processing module 704 is configured to perform spatiotemporal alignment of the first perception information and the second perception information based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, thereby obtaining aligned first perception information and aligned second perception information. The positioning module 706 is configured to locate the vehicle based on the aligned first perception information and aligned second perception information, thereby obtaining a vehicle positioning result.

[0116] It should be noted that the acquisition module 702, processing module 704, and positioning module 706 correspond to steps S202 to S206 in Example 1. The examples and application scenarios implemented by the two modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above-mentioned modules can also be part of the device and can be run in the computer terminal 10 provided in Example 1.

[0117] In the above embodiment of the present application, the processing module 704 includes: an extraction unit, configured to extract the first perception information and the second perception information respectively to obtain a first perception feature of the first perception information and a second perception feature of the perception information; an encoding unit, configured to encode the first acquisition time and the second acquisition time respectively to obtain a first time feature of the first acquisition time and a second time feature of the second acquisition time; a processing unit, configured to perform spatiotemporal alignment based on the first perception feature, the second perception feature, the first time feature, and the second time feature to obtain an aligned first perception feature and an aligned second perception feature; and a first determination unit, configured to determine the aligned first perception information based on the aligned first perception feature, and to determine the aligned second perception information based on the aligned second perception feature.

[0118] In the above embodiment of the present application, the processing unit includes: a splicing subunit, configured to splice the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain a splicing feature; a prediction subunit, configured to predict the aligned first perceptual feature and the aligned second perceptual feature based on the splicing feature, wherein the aligned first perceptual feature and the aligned second perceptual feature correspond to the same acquisition time.

[0119] In the above embodiment of the present application, the positioning module 706 includes: a fusion unit, configured to fuse the aligned first perception information and the aligned second perception information to obtain fusion information; a positioning unit, configured to locate the vehicle based on the fusion information to obtain a positioning result of the vehicle.

[0120] In the above embodiment of the present application, the fusion unit includes: a determination subunit, configured to determine the point cloud data and visual data in the aligned first perception information and the aligned second perception information; a fusion subunit, configured to fuse the point cloud data and the visual data to obtain fusion information.

[0121] In the above embodiment of the present application, the fusion subunit is also configured to convert point cloud data into a preset space to obtain point cloud features, and convert visual data into a preset space to obtain visual features; encode point cloud features and visual features to obtain fusion features; and determine fusion information based on the fusion features.

[0122] In the above embodiment of the present application, the processing module 704 includes: a preprocessing unit, configured to preprocess the first perception information and the second perception information to obtain first preprocessed information and second preprocessed information; a spatiotemporal alignment unit, configured to perform spatiotemporal alignment on the first preprocessed information and the second preprocessed information based on a first acquisition time of the first perception information and a second acquisition time of the second perception information to obtain aligned first perception information and aligned second perception information.

[0123] In the above embodiment of the present application, the acquisition module 702 includes: a second determination unit, configured to determine at least one vehicle traveling within a preset range of the vehicle; and an acquisition unit, configured to acquire perception information sent by at least one vehicle to obtain first perception information.

[0124] In the above embodiment of the present application, the device also includes: a generation module, configured to generate control information or a driving path of the vehicle based on the positioning result of the vehicle; and a control module, configured to control the vehicle based on the control information or the driving path.

[0125] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0126] Example 6

[0127] According to an embodiment of the present application, a device for implementing the above-mentioned vehicle positioning method is also provided. Figure 8 is a schematic diagram of a vehicle positioning device according to Example 2 of the present application. As shown in Figure 8, the device includes: an acquisition module 802, a processing module 804 and a positioning module 806.

[0128] Among them, the above-mentioned acquisition module 802 is configured to acquire the first perception feature sent by the vehicle and the second perception feature sent by the road-side equipment, wherein the road-side equipment is located on the road where the vehicle is traveling, and the first perception feature is a feature obtained by feature extraction of the first perception information collected by the vehicle, and the second perception feature is a feature obtained by feature extraction of the second perception information collected by the road-side equipment; the processing module 804 is configured to perform spatiotemporal alignment on the first perception feature and the second perception feature based on the first acquisition time of the first perception information and the second acquisition time of the second perception information to obtain the aligned first perception feature and the aligned second perception feature; the positioning module 806 is configured to position the vehicle based on the aligned first perception feature and the aligned second perception feature to obtain the positioning result of the vehicle.

[0129] It should be noted here that the above-mentioned acquisition module 802, processing module 804 and positioning module 806 correspond to steps S402 to S406 in Example 2. The instances and application scenarios implemented by the modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 2.

[0130] Example 7

[0131] According to an embodiment of the present application, another device for implementing the above-mentioned vehicle positioning method is also provided. Figure 9 is a schematic diagram of a vehicle positioning device according to Example 3 of the present application. As shown in Figure 9, the device includes: a sending module 902 and a display module 904.

[0132] In which, the above-mentioned sending module 902 is configured to respond to input instructions acting on the operation interface, and display the first perception information sent by the vehicle and the second perception information sent by the road-end device on the operation interface, wherein the road-end device is located on the road on which the vehicle is traveling; the above-mentioned display module 904 is configured to respond to vehicle instructions acting on the operation interface, and display the positioning result of the vehicle on the operation interface, wherein the positioning result is the result of positioning the vehicle based on the aligned first perception information and the aligned second perception information, and the aligned first perception information and the aligned second perception information are obtained by temporally and spatially aligning the first perception information and the second perception information based on the first acquisition time of the first perception information and the second acquisition time of the second perception information.

[0133] It should be noted here that the above-mentioned sending module 902 and display module 904 correspond to steps S502 to S504 in Example 3. The instances and application scenarios implemented by the modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 3.

[0134] Example 8

[0135] According to an embodiment of the present application, another device for implementing the above-mentioned vehicle positioning method is also provided. Figure 10 is a schematic diagram of a vehicle positioning device according to Example 4 of the present application. As shown in Figure 10, the device includes: a first calling module 1002, a processing module 1004, a positioning module 1006, and a second calling module 1008.

[0136] The first calling module 1002 is configured to obtain the first perception information and the second perception information by calling the first interface, wherein the first interface includes a first parameter, the parameter value of the first parameter is the first perception information and the second perception information, and the road end device is located on the road on which the vehicle is traveling; the processing module 1004 is configured to perform spatiotemporal alignment on the first perception information and the second perception information based on the first acquisition time of the first perception information and the second acquisition time of the second perception information to obtain the aligned first perception information and the aligned second perception information; the positioning module 1006 is configured to perform positioning on the vehicle based on the aligned first perception information and the aligned second perception information to obtain the positioning result of the vehicle; the second calling module 1008 is configured to output the positioning result of the vehicle by calling the second interface, wherein the second interface includes a second parameter, the parameter value of the second parameter is the positioning result of the vehicle.

[0137] It should be noted here that the above-mentioned first calling module 1002, processing module 1004, positioning module 1006, and second calling module 1008 correspond to steps S602 to S608 in Example 4. The instances and application scenarios implemented by the modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 4.

[0138] Example 9

[0139] The embodiment of the present application can provide a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0140] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of a computer network.

[0141] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the vehicle positioning method: obtaining first perception information sent by the vehicle and second perception information sent by the road-end device, wherein the road-end device is located on the road on which the vehicle is traveling; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, performing spatiotemporal alignment on the first perception information and the second perception information to obtain aligned first perception information and aligned second perception information; based on the aligned first perception information and the aligned second perception information, positioning the vehicle to obtain the vehicle positioning result.

[0142] Optionally, Figure 11 is a block diagram of a computer terminal according to an embodiment of the present application. As shown in Figure 11, the computer terminal A may include: one or more (one is shown in the figure) processors 1102, a memory 1104, a storage controller, and a peripheral interface, wherein the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0143] The memory can be configured to store software programs and modules, such as the program instructions / modules corresponding to the vehicle positioning method and device in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the above-mentioned vehicle positioning method. The memory can include high-speed random access memory and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located relative to the processor, and these remote memories can be connected to terminal A via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the first perception information sent by the vehicle and the second perception information sent by the road-end equipment, wherein the road-end equipment is located on the road on which the vehicle is traveling; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, align the first perception information and the second perception information in time and space to obtain the aligned first perception information and the aligned second perception information; based on the aligned first perception information and the aligned second perception information, locate the vehicle to obtain the positioning result of the vehicle.

[0145] Optionally, the processor may also execute the program code for the following steps: based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, performing spatiotemporal alignment on the first perception information and the second perception information to obtain aligned first perception information and aligned second perception information, including: performing feature extraction on the first perception information and the second perception information respectively to obtain first perception features of the first perception information and second perception features of the perception information; encoding the first acquisition time and the second acquisition time respectively to obtain first time features of the first acquisition time and second time features of the second acquisition time; performing spatiotemporal alignment based on the first perception feature, the second perception feature, the first time feature and the second time feature to obtain aligned first perception features and aligned second perception features; determining the aligned first perception information based on the aligned first perception feature, and determining the aligned second perception information based on the aligned second perception feature.

[0146] Optionally, the processor may also execute the program code of the following steps: performing spatiotemporal alignment based on the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain the aligned first perceptual feature and the aligned second perceptual feature, including: splicing the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain a spliced ​​feature; and predicting the aligned first perceptual feature and the aligned second perceptual feature based on the spliced ​​feature, wherein the aligned first perceptual feature and the aligned second perceptual feature correspond to the same acquisition time.

[0147] Optionally, the above-mentioned processor can also execute the program code of the following steps: positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain the positioning result of the vehicle, including: fusing the aligned first perception information and the aligned second perception information to obtain fused information; positioning the vehicle based on the fused information to obtain the positioning result of the vehicle.

[0148] Optionally, the above-mentioned processor can also execute the program code of the following steps: fusing the aligned first perception information and the aligned second perception information to obtain fusion information, including: determining the point cloud data and visual data in the aligned first perception information and the aligned second perception information; fusing the point cloud data and the visual data to obtain fusion information.

[0149] Optionally, the processor may also execute the program code of the following steps: fusing point cloud data and visual data to obtain fusion information, including: converting point cloud data to a preset space to obtain point cloud features, and converting visual data to a preset space to obtain visual features; encoding point cloud features and visual features to obtain fusion features; and determining fusion information based on the fusion features.

[0150] Optionally, the processor may also execute the program code for the following steps: based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, performing spatiotemporal alignment on the first perception information and the second perception information to obtain aligned first perception information and aligned second perception information, including: preprocessing the first perception information and the second perception information to obtain first preprocessed information and second preprocessed information; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, performing spatiotemporal alignment on the first preprocessed information and the second preprocessed information to obtain aligned first perception information and aligned second perception information.

[0151] Optionally, the processor may also execute the program code of the following steps: obtaining the first perception information sent by the vehicle includes: determining at least one vehicle traveling within a preset range of the vehicle; obtaining the perception information sent by at least one vehicle to obtain the first perception information.

[0152] Optionally, the above-mentioned processor can also execute the program code of the following steps: after positioning the vehicle based on the aligned first perception information and the aligned second perception information and obtaining the positioning result of the vehicle, the method also includes: generating the control information or driving path of the vehicle based on the positioning result of the vehicle; and controlling the vehicle based on the control information or the driving path.

[0153] In an embodiment of the present application, first perception information sent by a vehicle and second perception information sent by a roadside device are obtained; based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle. It is easy to notice that by temporally and spatially aligning the first perception information and the second perception information and using the aligned first perception information and the aligned second perception information to position the vehicle, compensation for communication delay is achieved, so that in the process of collaborative positioning using the first perception information collected by the vehicle and the second perception information collected by the roadside device, a more accurate positioning result can be obtained, thereby improving the vehicle positioning accuracy, thereby solving the technical problem of low vehicle positioning accuracy in related technologies.

[0154] Those skilled in the art will appreciate that the structure shown in the figure is for illustration only, and the computer terminal may also be a smartphone (such as an Android phone, iOS phone, etc.), a tablet computer, a PDA, a mobile internet device (MID), a PAD, or other terminal device. FIG11 does not limit the structure of the above-mentioned electronic devices. For example, the computer terminal A may include more or fewer components (such as a network interface, a display device, etc.) than those shown in FIG11, or may have a configuration different from that shown in FIG11.

[0155] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0156] Example 10

[0157] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be configured to store the program code executed by the vehicle positioning method provided in the first embodiment.

[0158] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0159] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: obtaining first perception information sent by the vehicle and second perception information sent by a road-end device, wherein the road-end device is located on the road on which the vehicle is traveling; based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, performing spatiotemporal alignment on the first perception information and the second perception information to obtain aligned first perception information and aligned second perception information; and positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle.

[0160] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0161] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are schematic. For example, the division of the units is a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0163] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0165] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0166] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A vehicle positioning method, comprising: Acquire first perception information sent by a vehicle and second perception information sent by a road-side device, wherein the road-side device is located on a road on which the vehicle is traveling; Based on a first collection time of the first perception information and a second collection time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; Based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle.

2. The method according to claim 1, wherein: Based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information, including: Extracting features from the first perception information and the second perception information respectively to obtain a first perception feature of the first perception information and a second perception feature of the perception information; Encode the first acquisition time and the second acquisition time respectively to obtain a first time feature of the first acquisition time and a second time feature of the second acquisition time; Performing spatiotemporal alignment based on the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain an aligned first perceptual feature and an aligned second perceptual feature; The aligned first perceptual information is determined based on the aligned first perceptual feature, and the aligned second perceptual information is determined based on the aligned second perceptual feature.

3. The method according to claim 2, wherein: Performing spatiotemporal alignment based on the first perceptual feature, the second perceptual feature, the first time feature, and the second time feature to obtain an aligned first perceptual feature and an aligned second perceptual feature includes: splicing the first perception feature, the second perception feature, the first time feature, and the second time feature to obtain a spliced ​​feature; Based on the splicing feature, the aligned first perceptual feature and the aligned second perceptual feature are predicted, wherein the aligned first perceptual feature and the aligned second perceptual feature correspond to the same acquisition time.

4. The method according to claim 1, wherein: Based on the aligned first perception information and the aligned second perception information, the vehicle is positioned to obtain a positioning result of the vehicle, including: fusing the aligned first perception information and the aligned second perception information to obtain fused information; The vehicle is positioned based on the fusion information to obtain a positioning result of the vehicle.

5. The method according to claim 4, wherein: Fusing the aligned first perception information and the aligned second perception information to obtain fused information includes: Determining point cloud data and visual data in the aligned first perception information and the aligned second perception information; The point cloud data and the visual data are fused to obtain the fusion information.

6. The method according to claim 5, wherein: Fusing the point cloud data and the visual data to obtain the fusion information includes: Converting the point cloud data to a preset space to obtain point cloud features, and converting the visual data to the preset space to obtain visual features; Encoding the point cloud features and the visual features to obtain fusion features; The fusion information is determined based on the fusion feature.

7. The method according to claim 1, wherein: Based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information, including: Preprocessing the first perception information and the second perception information to obtain first preprocessing information and second preprocessing information; Based on a first acquisition time of the first perception information and a second acquisition time of the second perception information, the first preprocessing information and the second preprocessing information are temporally and spatially aligned to obtain the aligned first perception information and the aligned second perception information.

8. The method according to claim 1, wherein: Get the first perception information sent by the vehicle, including: determining at least one vehicle traveling within a preset range of the vehicle; Acquire the perception information sent by the at least one vehicle to obtain the first perception information.

9. The method according to claim 1, wherein: After positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle, the method further includes: Based on the positioning result of the vehicle, generating control information or a driving path of the vehicle; The vehicle is controlled based on the control information or the driving path.

10. A vehicle positioning method, comprising: Acquire a first perception feature sent by a vehicle and a second perception feature sent by a roadside device, wherein the roadside device is located on a road on which the vehicle is traveling, the first perception feature is a feature obtained by extracting first perception information collected by the vehicle, and the second perception feature is a feature obtained by extracting second perception information collected by the roadside device; Based on the first acquisition time of the first perception information and the second acquisition time of the second perception information, the first perception feature and the second perception feature are temporally and spatially aligned to obtain the aligned first perception feature. features and aligned second perceived features; The vehicle is positioned based on the aligned first perception feature and the aligned second perception feature to obtain a positioning result of the vehicle.

11. A vehicle positioning method, comprising: In response to an input instruction acting on an operation interface, displaying on the operation interface first perception information sent by the vehicle and second perception information sent by a road-side device, wherein the road-side device is located on a road on which the vehicle is traveling; In response to a vehicle command applied to the operation interface, a positioning result of the vehicle is displayed on the operation interface, wherein the positioning result is a result of positioning the vehicle based on the aligned first perception information and the aligned second perception information, and the aligned first perception information and the aligned second perception information are obtained by spatiotemporally aligning the first perception information and the second perception information based on a first acquisition time of the first perception information and a second acquisition time of the second perception information.

12. A vehicle positioning method, comprising: Acquiring first perception information and second perception information by calling a first interface, wherein the first interface includes a first parameter, a parameter value of the first parameter is the first perception information and the second perception information, and a road end device is located on a road where the vehicle is traveling; Based on a first collection time of the first perception information and a second collection time of the second perception information, the first perception information and the second perception information are temporally and spatially aligned to obtain aligned first perception information and aligned second perception information; Positioning the vehicle based on the aligned first perception information and the aligned second perception information to obtain a positioning result of the vehicle; The positioning result of the vehicle is output by calling a second interface, wherein the second interface includes a second parameter, and a parameter value of the second parameter is the positioning result of the vehicle.

13. An electronic device comprising: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 12 when running.

14. A computer-readable storage medium comprising a stored executable program, wherein: When the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 12.

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