Information sensing method, device, system, server and readable storage medium

Abstract

The application provides an information sensing method, device, system server and readable storage medium, comprising: acquiring first target data, the first target data being data of a target vehicle driving on a target road; acquiring second target data, the second target data being target road data; and processing the first target data and the second target data to obtain driving data. The application acquires the first target data and the second target data through a server, processes the first target data and the second target data to obtain driving data, processes the obtained data directly through the server to obtain driving data, reduces data transmission to a cloud server, the transmission distance of the first target data and the second target data is shortened due to the server being arranged at the network edge of a local device, the efficiency of information transmission is improved, and driving data can be obtained in time and accurately to assist in analyzing road safety conditions.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and more specifically, to an information sensing method, device, system, server, and readable storage medium. Background Technology

[0002] In recent years, intelligent vehicles have made great progress in the field of autonomous driving. Intelligent vehicles use various sensors such as radar and cameras to perceive the surrounding environment through advanced sensor fusion technology, providing drivers with more information and realizing autonomous driving functions.

[0003] Due to the limited computing power of vehicle-mounted terminals and roadside units, handling the ever-increasing volume of data from both vehicle-mounted and roadside units during data fusion has become a key issue hindering the development of intelligent transportation. The traditional solution is to upload tasks to a remote cloud, leveraging its abundant computing resources to meet data processing needs. However, in practical applications, if all computationally intensive tasks are handled by cloud servers, the excessive data volume will inevitably overload the cloud servers. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an information sensing method, apparatus, system, server, and readable storage medium. By processing information at the network edge close to the local device, cloud computing services are localized to enable a more comprehensive analysis of road safety conditions.

[0005] In a first aspect, embodiments of this application provide an information perception method applied to a server, comprising: acquiring first target data, wherein the first target data is data of a target vehicle traveling on a target road; acquiring second target data, wherein the second target data is target road data; and processing the first target data and the second target data to obtain driving data.

[0006] This application embodiment obtains first target data and second target data respectively. Through the first target data and second target data, data on the target vehicle's travel on the target road and target road data can be obtained. Then, the first target data and second target data can be combined with the target road data to obtain real-time traffic data of the target road. Based on the real-time traffic data, driving data can be further obtained. The driving data can be used as auxiliary data for vehicle driving to help the vehicle obtain road information in a timely and accurate manner.

[0007] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein: processing the first target data and the second target data to obtain driving data includes: removing abnormal data from the first target data and the second target data to obtain a first dataset, wherein the first target data and the second target data are data collected by multiple devices; classifying and fusing the first dataset to obtain driving data for one or more targets.

[0008] This application embodiment removes abnormal data from the first target data and the second target data to ensure the accuracy of the data used in the calculation, preventing the data from being affected by abnormal data. Furthermore, the first target data and the second target data collected from multiple devices are classified and fused to obtain driving data for one or more targets. By classifying and fusing the first and second target data, driving data for multiple targets can be obtained separately, improving the accuracy of the driving data.

[0009] In conjunction with the first possible implementation of the first aspect, this application provides a second possible implementation of the first aspect, wherein: the driving data includes: driving data of a first target; the classification and fusion of the first dataset to obtain driving data of one or more targets includes: dividing the first dataset into a first dynamic dataset; determining a second dynamic dataset of the first target based on the first dynamic dataset, wherein the second dynamic dataset is the dataset belonging to the first target in the first dynamic dataset; and fusing the second dynamic dataset to obtain driving data of the first target.

[0010] This application embodiment divides the first dataset into a first dynamic dataset and a first static dataset. Since dynamic data varies significantly, and different devices may have different first dynamic datasets for the same target depending on the angle and time of acquisition, the dataset belonging to the first target within the first dynamic dataset is designated as the second dynamic dataset. By classifying and filtering the first dataset before determining the second dataset, the processing of the dataset is reduced. The second dataset, specifically targeting the first target, is then processed, reducing the amount and difficulty of processing and improving efficiency and accuracy.

[0011] In conjunction with the second possible implementation of the first aspect, this application provides a third possible implementation of the first aspect, wherein fusing the second dynamic dataset to obtain the driving data of the first target includes: filtering out a third dynamic dataset that has the same motion trajectory from the second dynamic dataset; and performing calculations on the third dynamic dataset to obtain the driving data of the first target.

[0012] This application embodiment further filters the second dataset to determine the third dataset of the motion trajectory of the first target, and then calculates the driving data of the first target by calculating the third dataset. Based on the second dataset, the true trajectory of the first target is further calculated and determined. Motions in the second dataset that do not belong to the first target are removed to filter out the third dynamic dataset that truly belongs to the first target, thereby further improving the accuracy of the driving data of the first target.

[0013] In conjunction with the third possible implementation of the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the driving data includes road three-dimensional model data, and the processing of the first target data and the second target data further includes: forming a first three-dimensional model based on the first target data; forming a second three-dimensional model based on the second target data; and fusing the first three-dimensional model and the second three-dimensional model to obtain road three-dimensional model data.

[0014] The embodiments of this application form a three-dimensional model by combining the first target data, the second target data, or the combination thereof, making the obtained data more realistic, three-dimensional, and easy to use directly.

[0015] In conjunction with the fourth possible implementation of the first aspect, this application provides a fifth possible implementation of the first aspect, wherein the method further includes: acquiring third target data, wherein the third target data is weather data of the target road; and processing the first target data and the second target data to obtain driving data, comprising: processing the first target data, the second target data and the third target data to obtain driving data.

[0016] This application embodiment increases the acquisition and processing of weather data, making the obtained driving data more complete. This allows vehicles to select the optimal driving route based on the road conditions and weather conditions, making the intelligent transportation system more user-friendly and intelligent.

[0017] Secondly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the information sensing method described in the first aspect or any possible implementation of the first aspect.

[0018] Thirdly, embodiments of this application also provide an information sensing device, comprising: a first acquisition module for acquiring first target data, wherein the first target data is real-time three-dimensional model data of a target vehicle traveling on a target road; a second acquisition module for acquiring second target data, wherein the second target data is three-dimensional model data of a target road; and a processing module for processing the first target data and the second target data to obtain driving data.

[0019] Fourthly, embodiments of this application also provide a server, including: a memory; and a processor including the information sensing device described in the third aspect; the memory is connected to the processor, the processor and the memory interact with each other, and the memory is used to store data in the processor.

[0020] Fifthly, embodiments of this application also provide an information sensing system, comprising: a first sensing device for acquiring first target data and sending the first target data to the server; a second sensing device for acquiring second target data and sending the second target data to the server; and the server described in the fourth aspect for receiving the first target data and the second target data, and processing the first target data and the second target data to obtain driving data.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of an information sensing system provided in an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of the deployment of the second sensing device in the information sensing system provided in the embodiments of this application;

[0025] Figure 3 A schematic diagram of the edge server of the information sensing system provided in the embodiments of this application;

[0026] Figure 4 This is a schematic diagram illustrating the application of the information sensing system provided in the embodiments of this application;

[0027] Figure 5A flowchart of an information perception method provided in an embodiment of this application;

[0028] Figure 6 A flowchart of step 503 of an information sensing method provided in an embodiment of this application;

[0029] Figure 7 A flowchart of step 5032 of an information perception method provided in an embodiment of this application;

[0030] Figure 8 This is a schematic diagram of the functional modules of an information sensing device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] To facilitate understanding of this embodiment, a detailed description of an information perception system disclosed in this application embodiment will be provided first.

[0034] like Figure 1 As shown, Figure 1 This is a schematic diagram of an information sensing system. For example... Figure 1 As shown, the information sensing system 10 includes: a server 100, a second sensing device 200, and a first sensing device 300.

[0035] The server 100 is connected to the second sensing device 200 and the first sensing device 300 respectively. The server 100 and the second sensing device 200 can be connected by wired or wireless means, and the server 100 and the first sensing device 300 can also be connected by wired or wireless means.

[0036] Optionally, the wired connection method can be Ethernet, fiber optic, etc., and the wireless connection method can be WIFI, Bluetooth, etc.

[0037] Optionally, the first sensing device 300 may be a vehicle-mounted sensing device, the second sensing device 200 may be a roadside sensing device, and the server 100 may be an edge computing module.

[0038] Optionally, the first sensing device 300 is used to collect vehicle driving data and vehicle surrounding environment data, and send them to the server 100. The second sensing device 200 is used to sense road traffic environment information and send it to the server 100.

[0039] Optionally, the server 100 is used to receive data sent by the second sensing device 200 and the first sensing device 300, and process it to obtain driving data.

[0040] Optionally, the second sensing device 200 can be installed on a lamppost at a road intersection or on a pole specifically designed for installing sensing devices. The first sensing device 300 can be installed on the target vehicle.

[0041] The second sensing device 200 includes a first lidar module 210, a first millimeter-wave radar module 220, a first imaging module 230, a weather and environmental monitoring sensor 240, and a first communication unit 250. The first communication unit 250 is connected to the server 100 and is used for data transmission. The first lidar module 210 and the first millimeter-wave radar module 220 collect road data for the target road segment and transmit this road data to the server 100 via the first communication unit 250. The first imaging module 230 captures road condition data and transmits this road condition data to the server 100. The weather and environmental monitoring sensor 240 monitors weather data and transmits this weather data to the server 100 via the first communication unit 250.

[0042] Optionally, the positions of the first lidar module 210, the first millimeter-wave radar module 220, and the first imaging module 230 can be selected according to actual conditions.

[0043] For example, such as Figure 2 As shown, the first imaging module 230 can be set above the stop line of the road intersection in the same traffic flow direction, the first millimeter-wave radar module 220 can be set above the road in the traffic flow direction, and the first imaging module 230 set on the road in the same traffic flow direction is located on both sides of the intersection, respectively. The first lidar module 210 is set on the side of the road and near the two intersecting roads.

[0044] Optionally, the first lidar module 210 can also be used to measure distance, and the first millimeter-wave radar module 220 can be used to sense information such as target distance and speed.

[0045] Optionally, the first lidar module 210, the first millimeter-wave radar module 220, the first imaging module 230, and the weather and environment monitoring sensor 240 operate independently and do not affect each other.

[0046] Optionally, the road data may include: potholes, road surface water accumulation, waterlogging locations, and road obstruction locations. The road condition data may include: traffic flow, pedestrian flow, and other data. The weather data may include: wind speed, wind direction, rainfall, temperature, and humidity.

[0047] The first sensing device 300 is equipped with a second millimeter-wave radar module 330, a second imaging module 320, a second lidar module 310, a surround-view camera module 340, and a second communication unit 360.

[0048] The second communication unit 360 is connected to the server 100 and is used for data transmission. The second millimeter-wave radar module 330 is used to identify obstacles and extract depth and speed information of targets. The second millimeter-wave radar module 330 is also used to send this information to the server 100 via the second communication unit 360. The second imaging module 320 is used to identify lane lines, traffic signs, obstacles, pedestrians, etc., and is also used to send the acquired data to the server 100 via the second communication unit 360. The second lidar module 310 is used to acquire target speed, distance, and angle data, and is also used to send the acquired data to the server 100 via the second communication unit 360. The surround-view camera module 340 is used for short-range scene recognition, such as data on obstacles around vehicles, pedestrians around vehicles, and animals around vehicles. The surround-view camera module 340 is also used to send the acquired data to the server 100 via the second communication unit 360.

[0049] Optionally, the second millimeter-wave radar module 330, the second imaging module 320, the second lidar module 310, the surround-view camera module 340, and the second communication unit 360 can be set in different positions according to actual conditions.

[0050] For example, the second millimeter-wave radar module 330 may be set at the lower edge of the front and rear license plates of the target vehicle, the second shooting module 320 may be set at the rearview mirror of the target vehicle, the second lidar module 310 may be set at the roof of the target vehicle, and the surround-view camera module 340 may be set at the front, rear and side rearview mirrors.

[0051] Optionally, the second millimeter-wave radar module 330, the second imaging module 320, the second lidar module 310, and the surround-view camera module 340 operate independently of each other and do not affect each other.

[0052] The server 100 includes an edge server 110, an application program 120, and a third communication unit 130. The edge server 110 processes data received from the second sensing device 200 and the first sensing device 300. The application program 120 packages the edge server data into an application and makes it available to third-party business applications or software developers. The third communication unit 130 interacts with the second sensing device 200, the first sensing device 300, the smart terminal 30, the information board 20, and the traffic management cloud computing center 40.

[0053] Optionally, the server 100 can be set up at base stations on both sides of the road.

[0054] Optionally, the edge server 110 may further include a memory 111, a storage controller 112, a processor 113, a peripheral interface 114, an input / output unit 115, and a display unit 116. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of server 100. For example, server 100 may also include components that are more complex than those shown in the diagram. Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0055] The aforementioned memory 111, memory controller 112, processor 113, peripheral interface 114, input / output unit 115, and display unit 116 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 113 is used to execute executable modules stored in the memory.

[0056] The memory 111 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 111 stores programs, and the processor 113 executes these programs upon receiving execution instructions. The methods executed by the server 100 as defined in any embodiment of this application can be applied to the processor 113, or implemented by the processor 113.

[0057] The aforementioned processor 113 may be an integrated circuit chip with signal processing capabilities. The processor 113 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0058] The peripheral interface 114 described above couples various input / output devices to the processor 113 and the memory 111. In some embodiments, the peripheral interface 114, the processor 113, and the memory controller 112 can be implemented on a single chip. In other instances, they can be implemented on separate chips.

[0059] The input / output unit 115 described above is used to provide user input data. The input / output unit 115 may be, but is not limited to, a mouse and keyboard, etc.

[0060] The aforementioned display unit 116 provides an interactive interface (e.g., a user interface) between the server 100 and the user, or displays image data of the surrounding rock for the user's reference. In this embodiment, the display unit can be a liquid crystal display or a touch screen. If it is a touch screen, it can be a capacitive touch screen or a resistive touch screen that supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch screen can sense touch operations generated simultaneously from one or more locations on the touch screen and pass the sensed touch operations to the processor for calculation and processing.

[0061] For example, such as Figure 4 As shown, the first sensing device 300 collects vehicle driving data and surrounding environment data, and sends them to the server 100. The second sensing device 200 senses road traffic environment information and sends it to the edge server 110. The server 100 receives data from the second sensing device 200 and the first sensing device 300, performs multi-source heterogeneous fusion of the low-level data from the first sensing device 300 and the data from the second sensing device 200, and finally sends the fused data to the first sensing device 300 for assisted driving, as well as to the second sensing device 200 and the smart terminal 30. The server 100 also sends the fused data to the traffic management cloud computing center 40, which permanently stores the data.

[0062] Through this system, the first sensing device can receive a general road environment model distributed by the edge server and perceive surrounding environmental information through onboard sensors. Through a unified interaction interface, the server fuses the received general environment model and the data perceived by the onboard sensors for collaborative perception, expanding the perceived objects and supplementing the perception field of view. In this system, the server defines a unified interaction interface to realize the transmission of data between the first and second sensing devices. Additionally, smart terminals can receive the general road environment model distributed by the edge server, providing it to drivers and passengers and effectively enhancing driving safety.

[0063] Please see Figure 5 This is a flowchart of the information perception method provided in the embodiments of this application. The following will describe... Figure 5 The specific process shown will be explained in detail.

[0064] Step 501: Obtain first target data, which is the data of the target vehicle traveling on the target road.

[0065] Optionally, the first target data is data sent by the first sensing device, and the first data includes: the target vehicle's driving trajectory, the target vehicle's driving speed, the driving trajectories of vehicles around the target vehicle, the driving speeds of vehicles around the target vehicle, the road conditions around the target vehicle, and road obstacles around the target vehicle.

[0066] Step 502: Obtain the second target data, which is the target road data.

[0067] Optionally, the second target data is data sent by the second sensing device, and the first data includes: traffic congestion conditions of the target road segment, road segment signage, weather conditions of the target road segment, and construction conditions of the target road segment.

[0068] Step 503: Process the first target data and the second target data to obtain driving data.

[0069] Optionally, the driving data may be navigation data, or it may be auxiliary driving data obtained by processing real-time traffic conditions during the journey.

[0070] For example, if current lane congestion information is obtained by analyzing first target data and second target data, then this driving data could indicate that the current lane is congested while the left or right lane is clear. In this case, the driver can choose to change lanes based on the information provided by this driving data, which provides assistance to the driver during driving.

[0071] For example, if the weather condition of the target road segment is determined to be heavy fog by analyzing the first target data and the second target data, and if the target road segment is the first road segment, then the driving data could be: heavy fog in the first road segment, clear skies in the second or third road segment, with the second road segment being 20km long and the estimated driving time approximately 30 minutes, and the third road segment being 30km long and the estimated driving time approximately 40 minutes. In this case, the driver can reselect the target road segment as the second road segment based on the information provided by this driving data and replan the route. This driving data includes navigation data and driving assistance data.

[0072] In one implementation, such as Figure 6 As stated, step 503 includes:

[0073] Step 5031: Remove abnormal data from the first target data and the second target data to obtain the first dataset, which is data collected by multiple devices.

[0074] Optionally, the first target data may be data collected by one or more devices such as a second millimeter-wave radar module, a second imaging module, a second lidar module, or a surround-view camera module.

[0075] Optionally, the abnormal data can be fuzzy data, incomplete data, etc.

[0076] Optionally, the first dataset may be one or more datasets, and the first dataset may be categorized according to the acquisition device.

[0077] Step 5032: Classify and fuse the first dataset to obtain driving data for one or more targets.

[0078] Optionally, the one or more targets may be a first target vehicle, a second target vehicle, a third target vehicle, a first target pedestrian, a second target pedestrian, a first obstacle, a second obstacle, a first target animal, a second target animal, etc.

[0079] Optionally, the driving data includes: driving data of the first target, driving data of the second target, driving data of the third target, etc.

[0080] In one implementation, such as Figure 7 As shown, step 5032 includes:

[0081] Step 50321: Divide the first dataset into a first dynamic dataset.

[0082] Optionally, the first dynamic dataset includes: an environmental dataset, a vehicle trajectory dataset, a pedestrian trajectory dataset, an obstacle trajectory dataset, etc.

[0083] Optionally, the first dataset can also be divided into a first static dataset, which includes: roads, buildings, numbers, traffic signs, traffic lights, weather conditions, etc.

[0084] Step 50322: Based on the first dynamic dataset, determine the second dynamic dataset of the first target. The second dynamic dataset is the dataset belonging to the first target in the first dynamic dataset.

[0085] Optionally, the second dynamic dataset includes multiple datasets, and the first target is multiple first targets.

[0086] For example, if there are 5 datasets of first target A in the first dynamic dataset, then these 5 datasets of first target A constitute the second dynamic dataset of first target A. If there are 10 datasets of first target B in the first dynamic dataset, then these 10 datasets of first target B constitute the second dynamic dataset of first target A. If there are 15 datasets of first target C in the first dynamic dataset, then these 15 datasets of first target C constitute the second dynamic dataset of first target C.

[0087] Step 50323: The second dynamic dataset is fused to obtain the driving data of the first target.

[0088] For example, if the second dynamic dataset of the first target A includes 5 datasets, then these 5 datasets are merged to obtain the driving data of the first target A. If the second dynamic dataset of the first target B includes 10 datasets, then these 10 datasets are merged to obtain the driving data of the first target B. If the second dynamic dataset of the first target C includes 15 datasets, then these 15 datasets are merged to obtain the driving data of the first target C.

[0089] In one implementation, step 50323 includes: filtering a third dynamic dataset from the second dynamic dataset that shares the same motion trajectory; and performing calculations on the third dynamic dataset to obtain the driving data of the first target.

[0090] For example, if the second dynamic dataset of the first target A includes 5 datasets, and after calculation by a specific algorithm it is determined that only 3 of these datasets belong to the second dynamic dataset of the first target A, then the other 2 datasets are removed, leaving only 3 second dynamic datasets belonging to the first target A, which are determined as the third dynamic dataset. The driving data of the first target A is then calculated from the third dynamic dataset.

[0091] For example, if the second dynamic dataset of the first target B includes 10 datasets, and after calculation by a specific algorithm it is determined that only 5 datasets belong to the second dynamic dataset of the first target B, then the other 5 datasets are removed, leaving only 5 second dynamic datasets belonging to the first target B, which are determined as the third dynamic dataset. The driving data of the first target B is then calculated from the third dynamic dataset.

[0092] For example, if the second dynamic dataset of the first target C includes 15 datasets, and after calculation by a specific algorithm it is determined that these 15 datasets all belong to the second dynamic dataset of the first target C, then the driving data of the first target C can be obtained by directly calculating these 15 second dynamic datasets of the first target C.

[0093] In one embodiment, the driving data includes road three-dimensional model data. Step 503 further includes: forming a first three-dimensional model based on the first target data, forming a second three-dimensional model based on the second target data, and fusing the first three-dimensional model and the second three-dimensional model to obtain a road three-dimensional model.

[0094] Optionally, the first three-dimensional model may be a three-dimensional model of the road surface around the vehicle where the first sensing device is located, or a three-dimensional model of the vehicles around the vehicle where the first sensing device is located, or a three-dimensional model of the pedestrians around the vehicle where the first sensing device is located, or a three-dimensional model of the entire driving environment of the vehicle where the first sensing device is located.

[0095] Optionally, the first three-dimensional model can be a real-time three-dimensional model that is updated in real time as the vehicle containing the first sensing device moves.

[0096] Optionally, the second three-dimensional model may be a three-dimensional model of the ground obstacles on the target road where the second sensing device is located, a three-dimensional model of the terrain of the target road where the second sensing device is located, a three-dimensional model of the driving conditions on the target road where the second sensing device is located, or a three-dimensional model of the overall road surface traffic environment of the target road where the second sensing device is located.

[0097] Optionally, the second three-dimensional model can be a real-time three-dimensional model that is updated in real time.

[0098] Optionally, fusing the first three-dimensional model with the second three-dimensional model may include fusing the first three-dimensional model with the second three-dimensional model. Fusing the first three-dimensional model with the second three-dimensional model may also include fusing the first target data with the second target data, and forming a three-dimensional model based on the fused data.

[0099] Optionally, the road 3D model includes, but is not limited to, a road terrain 3D model, a road condition model, a road vehicle 3D model, a road pedestrian 3D environment, a target vehicle surrounding environment model, and a target vehicle surrounding vehicle environment. The road 3D model can be one or more of the above 3D models in combination.

[0100] In one embodiment, the information perception method provided in this application further includes: acquiring third target data, wherein the third target data is weather data of the target road.

[0101] Optionally, the weather data may include: dense fog, heavy rain, snow, mudslides, and icing on the target road.

[0102] Optionally, the third target data is obtained by the second sensing device.

[0103] Optionally, step 503 further includes: processing the first target data, the second target data, and the third target data to obtain driving data.

[0104] Optionally, the driving data can be stored directly on the server, or it can be sent to the traffic management cloud computing center for storage.

[0105] This application embodiment acquires first target data collected by a first sensing device and second target data collected by a second sensing device, and calculates and fuses this data. This allows the vehicle to obtain not only the vehicle's surrounding information captured by the first sensing device, but also road information over a wider range and greater distance captured by the second sensing device. This fused auxiliary information helps the vehicle to analyze road safety conditions more comprehensively, thereby making better driving choices.

[0106] Based on the same application concept, this application also provides an information sensing device corresponding to the information sensing method. Since the principle of the device in this application is similar to that of the aforementioned information sensing method embodiment, the implementation of the device in this application can refer to the description in the above method embodiment, and the repeated parts will not be repeated.

[0107] Please see Figure 8 This is a functional module diagram of the information sensing device provided in this application embodiment. Each module in the information sensing device in this embodiment is used to execute the steps in the above method embodiments. The information sensing device includes a first acquisition module 601, a second acquisition module 602, and a processing module 603.

[0108] The first acquisition module 601 is used to acquire first target data, which is the data of the target vehicle traveling on the target road.

[0109] The second acquisition module 602 is used to acquire second target data, which is target road data.

[0110] The processing module 603 is used to process the first target data and the second target data to obtain driving data.

[0111] In one possible implementation, the processing module 603 is further configured to: remove abnormal data from the first target data and the second target data to obtain a first dataset, wherein the first target data and the second target data are data collected by multiple devices, and classify and fuse the first dataset to obtain driving data of one or more targets.

[0112] In one possible implementation, the processing module 603 is further configured to: divide the first dataset into a first dynamic dataset, determine a second dynamic dataset of the first target based on the first dynamic dataset, wherein the second dynamic dataset is the dataset belonging to the first target in the first dynamic dataset, and fuse the second dynamic dataset to obtain the driving data of the first target.

[0113] In one possible implementation, the processing module 603 is further configured to: filter out a third dynamic dataset that has the same motion trajectory from the second dynamic dataset, and perform calculations on the third dynamic dataset to obtain the driving data of the first target.

[0114] In one possible implementation, the processing module 603 is further configured to: form a first three-dimensional model based on the first target data, form a second three-dimensional model based on the second target data, and fuse the first three-dimensional model with the second three-dimensional model to obtain road three-dimensional model data.

[0115] In one possible implementation, the second acquisition module 602 is further configured to: acquire third target data, which is weather data of the target road.

[0116] The processing module 603 is also used to process the first target data, the second target data and the third target data to obtain driving data.

[0117] Based on the same application concept, this application also provides a server corresponding to the information sensing device. Since the principle of the server in this application is similar to that of the aforementioned information sensing device embodiment, the implementation of the server in this application can refer to the description in the above-mentioned device embodiment, and the repeated parts will not be repeated.

[0118] This application also provides a server, including: a memory and a processor including the above-described information sensing device.

[0119] The memory is connected to the processor, and the processor and memory exchange information. The memory is used to store data in the processor.

[0120] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the information perception method described in the above method embodiments.

[0121] The above description is merely an optional embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0122] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An information sensing method, characterized in that, Applied to servers, including: Acquire first target data, which is the data of the target vehicle traveling on the target road; Acquire the second target data, which is the target road data; The first target data and the second target data are processed to obtain driving data; The step of processing the first target data and the second target data to obtain driving data includes: Abnormal data is removed from the first target data and the second target data to obtain a first dataset. The first target data and the second target data are data collected by multiple devices. The first dataset is classified according to the collection device. The first dataset is classified and fused to obtain driving data for one or more targets; Driving data includes: driving data of a first target; the classification and fusion of the first dataset to obtain driving data of one or more targets includes: The first dataset is divided into a first dynamic dataset; Based on the first dynamic dataset, a second dynamic dataset for the first target is determined, wherein the second dynamic dataset is the dataset belonging to the first target in the first dynamic dataset; The second dynamic dataset is fused to obtain the driving data of the first target; The process of fusing the second dynamic dataset to obtain the driving data for the first target includes: A third dynamic dataset with the same motion trajectory is selected from the second dynamic dataset; The third dynamic dataset is used to calculate the driving data of the first target; Acquire third target data, which is weather data for the target road; The step of processing the first target data and the second target data to obtain driving data includes: The first target data, the second target data, and the third target data are processed to obtain driving data.

2. The method according to claim 1, characterized in that, The driving data includes road 3D model data, and the processing of the first target data and the second target data further includes: A first three-dimensional model is formed based on the first target data; A second three-dimensional model is formed based on the second target data; The first 3D model and the second 3D model are fused together to obtain the 3D model data of the road.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 2.

4. An information sensing device, characterized in that, include: The first acquisition module is used to acquire first target data, which is the data of the target vehicle traveling on the target road. The second acquisition module is used to acquire second target data, which is target road data. The processing module is used to process the first target data and the second target data to obtain driving data; The processing module is further configured to remove abnormal data from the first target data and the second target data to obtain a first dataset, wherein the first target data and the second target data are data collected by multiple devices; wherein the first dataset is classified according to the collecting device; the first dataset is classified and fused to obtain driving data of one or more targets; The processing module is further configured to divide the first dataset into a first dynamic dataset; determine a second dynamic dataset of the first target based on the first dynamic dataset, wherein the second dynamic dataset is the dataset belonging to the first target in the first dynamic dataset; and fuse the second dynamic dataset to obtain the driving data of the first target. The processing module is further configured to filter out a third dynamic dataset that has the same motion trajectory from the second dynamic dataset; and to perform calculations on the third dynamic dataset to obtain the driving data of the first target; The second acquisition module is further configured to: acquire third target data, wherein the third target data is weather data of the target road; The processing module is further configured to: process the first target data, the second target data and the third target data to obtain driving data.

5. A server, characterized in that, include: Memory; With a processor including the information sensing device of claim 4; The memory is connected to the processor, and the processor and the memory exchange information. The memory is used to store data in the processor.

6. An information sensing system, characterized in that, include: The first sensing device, the second sensing device, and the server as described in claim 5; The first sensing device is used to acquire first target data and send the first target data to the server; The second sensing device is used to acquire second target data and send the second target data to the server; The server according to claim 5 is used to receive the first target data and the second target data, and process the first target data and the second target data to obtain driving data.

Images