Road condition map construction method and system, electronic equipment and storage medium

Abstract

The invention provides a road condition map construction method and system, electronic equipment and a storage medium, and is applied to a vehicle-mounted unit, and the method comprises the steps: obtaining a basic static map of a current road section; acquiring sensing data acquired by a sensor of the current vehicle; the sensing data comprises current vehicle running state data and current vehicle position environment data; obtaining dynamic data in a target range; the dynamic data at least comprises one of data sent by other vehicles and data sent by a road side unit; according to the perception data, the dynamic data and the basic static map, drawing a road condition map corresponding to the current road section; and sending the sensing data and the dynamic data to an edge computing platform, so that the edge computing platform constructs a dynamic map corresponding to the current road section. The construction efficiency and precision of the local road condition map and the overall dynamic map can be effectively improved, and more accurate road condition information and driving safety guarantee are provided for drivers.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to a road condition map construction method, system, electronic device, and storage medium. Background Technology

[0002] Currently, V2X information sharing is largely limited to "vehicle-roadside unit" communication, lacking self-organizing and collaborative mechanisms between vehicles, and overly reliant on roadside unit infrastructure, placing excessively high demands on communication quality. In the current mainstream V2X framework, the Mobile Edge Computing Platform (MEP) generally provides the most core application services, while Roadside Units (RSUs) and On-Board Units (OBUs) are mainly responsible for providing road data and receiving service messages from the MEP. This means that the communication quality between vehicles and the cloud is crucial. Although the MEP can provide powerful computing capabilities and service capacity, over-reliance on cloud computing for core services will negate the advantages of the Internet of Things (IoT), ultimately leading to technical bottlenecks and an inability to provide more diverse services. However, if some core service computing is moved to the RSUs and OBUs within the current framework, allowing vehicles and infrastructure to be less reliant on core network services and possessing only basic computing and service capabilities, the flexibility of V2X functionality will be greatly improved. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: A road condition map construction method, applied to an in-vehicle unit, the method comprising: Obtain the basic static map of the current road segment; Acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data; Acquire dynamic data within the target area; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; Based on the perceived data, the dynamic data, and the basic static map, a road condition map corresponding to the current road segment is drawn. The sensed data and the dynamic data are sent to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

[0004] Furthermore, after acquiring the dynamic data within the target range, the method further includes: Call the road business calculation model; The dynamic data is filtered using the road business calculation model to obtain target data; the filtering is performed based on the perceived data. The road operation calculation model is used to calculate the perceived data and target data to obtain early warning information; the early warning information includes at least one of the following: congestion warning, oncoming traffic warning, pedestrian warning, and red light warning.

[0005] Furthermore, after filtering the dynamic data using the road business calculation model to obtain the target data, the method further includes: The complexity of calculating the perceived data and target data using the road business calculation model is obtained. If the complexity exceeds a preset scale, the perceived data and target data are sent to the edge computing platform so that the edge computing platform can calculate the perceived data and target data and obtain the warning information sent by the edge computing platform. If the complexity does not exceed the preset scale, the perceived data and target data are calculated using the road business calculation model to obtain the warning information.

[0006] Furthermore, the method of invoking the road business calculation model includes: An algorithm model update request is sent to the edge computing platform so that the edge computing platform matches a road business calculation model of a target mode based on the road condition map; the target mode includes: highway mode and urban road mode. The vehicle unit obtains the road service calculation model application interface sent by the edge computing platform, so that the vehicle unit can call the road service calculation model through the road service calculation model application interface.

[0007] Further, the system acquires the road condition map corresponding to the current road segment according to a first preset period, and sends the perception data and the dynamic data to the edge computing platform; requests the acquisition of the dynamic map from the edge computing platform according to a second preset period; the dynamic data is the dynamic data within the target range in the dynamic map; and calls the road business calculation model according to a third preset period. The first preset period, the second preset period, and the third preset period are obtained based on the target mode; the first preset period, the second preset period, and the third preset period corresponding to the highway mode are greater than the first preset period, the second preset period, and the third preset period corresponding to the urban road mode; the first preset period is less than or equal to the second preset period, and the second preset period is less than or equal to the third preset period.

[0008] Furthermore, the calculation of the perceived data and target data includes: Determine each piece of data to be calculated contained in the perception data and target data, and the data type of each piece of data to be calculated; the data types include: radar data, visual data, and road data; Obtain the data weight corresponding to each data type, perform weighted aggregation calculation on the data to be calculated for each data type, and obtain the target calculation result; Based on preset feature types, feature extraction is performed on the target calculation results to obtain at least one warning message corresponding to each preset feature type; the preset feature types include: road obstacles, road risks, road conditions, and driving conditions; The warning message is obtained based on the warning information.

[0009] Furthermore, the method also includes: Obtain basic security information within the target area; The basic security information is verified based on the acquired dynamic data.

[0010] A road condition map building system, applied to an in-vehicle unit, includes: The basic map acquisition module is used to acquire the basic static map of the current road segment; The perception data acquisition module is used to acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data. The dynamic data acquisition module is used to acquire dynamic data within the target range; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; The road condition map construction module is used to draw a road condition map corresponding to the current road segment based on the perceived data, the dynamic data and the basic static map; The road condition map upload module is used to send the perceived data and the dynamic data to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

[0011] A non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, the at least one instruction or the at least one program segment being loaded and executed by a processor to implement the aforementioned method.

[0012] An electronic device includes a processor and the aforementioned non-transitory computer-readable storage medium.

[0013] The present invention has at least the following beneficial effects: This invention employs a decentralized architecture, breaking away from the reliance on traditional roadside units for high-precision road condition map creation. It segments the map and uses OBUs and RSUs to construct parts of it, alleviating computational pressure. By transmitting individual vehicle behaviors, the response latency for map creation is shortened, and blind spots of individual vehicles can be filled by the visibility of other vehicles. This invention effectively improves the efficiency and accuracy of constructing local road condition maps and overall dynamic maps, providing drivers with more precise road condition information and enhanced driving safety. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart of a road condition map construction method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the business logic for constructing a traffic map, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating a distributed group algorithm for road condition map construction provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of a weighted calculation use case for road condition map construction provided by an embodiment of the present invention; Figure 5 This is a structural diagram of a road condition map construction system provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] This invention provides an embodiment of a road condition map construction method, applied to an in-vehicle unit, such as... Figure 1 As shown: S01: Obtain the basic static map of the current road segment; S02: Acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data, mainly including: vehicle latitude and longitude, heading angle, speed and other driving parameters; S03: Acquire dynamic data within the target range; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; the data sent by the roadside units mainly includes: dynamic data such as road surface conditions, vehicle speed, and pedestrian positions collected in real time by sensors such as microwave radar and video capture. S04: Based on the perceived data, the dynamic data, and the basic static map, a road condition map corresponding to the current road segment is drawn; S05: Send the sensing data and the dynamic data to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

[0018] In V2X services, map building is often accomplished by MEP collecting information and building in real time. The basis for OBU and RSU to have business capabilities is the availability of maps, but building a complete map requires a lot of computation, and OBU and RSU may not be able to meet the required performance. Figure 1 The aforementioned embodiment divides the map into segments that are constructed by OBU and RSU to alleviate computational pressure. Figure 1 The embodiment described above uses an On-Board Unit (OBU) as an example. It constructs a road condition map for the current road segment using data sensed by vehicle sensors and received local dynamic data. The Management Epidemic Provider (MEP) then merges these multiple road condition maps to generate a real-time dynamic map. When needed, the OBU can directly download the constructed dynamic map from the MEP to obtain a high-precision road condition map. The business logic is as follows: Figure 2 As shown.

[0019] In one embodiment of the present invention, after acquiring the dynamic data within the target range, the method further includes: Call the road business calculation model; The dynamic data is filtered through the road business calculation model to obtain target data; the filtering is based on the perception data to filter the dynamic data; this process can accurately filter out the data that is useful to the current vehicle, and then directly calculate the useful data. While effectively reducing the amount of calculation and ensuring the performance of the calculation, it can make the calculation results more accurate and obtain more precise warning information to guide the driver to drive safely. The road operation calculation model is used to calculate the perceived data and target data to obtain early warning information; the early warning information includes at least one of the following: congestion warning, oncoming traffic warning, pedestrian warning, and red light warning.

[0020] In one embodiment of the present invention, after filtering the dynamic data through the road business calculation model to obtain target data, the method further includes: Obtain the complexity of calculating the perceived data and target data using the road business calculation model; When the complexity exceeds a preset scale, the perceived data and target data are sent to the edge computing platform so that the edge computing platform can perform calculations on the perceived data and target data and obtain the warning information sent by the edge computing platform. This step is used to report the task to the MEP for cloud computing to obtain the final business data when complex business logic is involved, so as to ensure the performance of the current vehicle, improve the calculation speed, and ensure the accuracy of the calculation results. If the complexity is not greater than the preset scale, the perceived data and target data are calculated using the road business calculation model to obtain the early warning information; for example, if the complexity is higher than quadratic order (O(n^2)), it is considered to be greater than the preset scale. The preset scale is determined based on the current vehicle's hardware performance and computing power. If the hardware performance is low and the computing power is insufficient, the current vehicle may only be able to handle constant order (O(1)) and linear order O(n) complexity calculations. If the calculation is larger than this scale, it will cause system lag, unresponsiveness and other situations that affect the driving experience. If the hardware performance is high and the computing power is sufficient, the current vehicle can handle high complexity calculations such as linear logarithmic order O(nlogn) and quadratic order O(n^2).

[0021] In one embodiment of the present invention, the method of invoking the road business calculation model includes: An algorithm model update request is sent to the edge computing platform so that the edge computing platform matches a road business calculation model of a target mode based on the road condition map; the target mode includes: highway mode and urban road mode. The vehicle unit obtains the road service calculation model application interface sent by the edge computing platform, so that the vehicle unit can call the road service calculation model through the road service calculation model application interface.

[0022] In one embodiment of the present invention, a road condition map corresponding to the current road segment is acquired according to a first preset period, and the perceived data and the dynamic data are sent to an edge computing platform to regularly update or construct a local road condition map and a dynamic map in real time, thereby regularly or in real time acquiring the latest road condition information, obtaining more accurate early warning information, and ensuring driving safety; the dynamic map is requested from the edge computing platform according to a second preset period; the dynamic data is the dynamic data within the target range of the dynamic map, and the dynamic data acquired based on the dynamic map can ensure the accuracy of the constructed local road condition map, further ensuring the accuracy of the dynamic map; the road business calculation model is invoked according to a third preset period. This process is used to dynamically update the road business calculation model according to the current road conditions (highway or urban road) to better calculate business data and obtain more accurate calculation results; The first, second, and third preset periods are obtained based on the target mode. Since road conditions and environments are more complex in urban areas compared to relatively simpler on highways, a higher frequency of business data updates is required in urban areas. Therefore, the first, second, and third preset periods corresponding to the highway mode are longer than those corresponding to the urban road mode. The first, second, and third preset periods can be the same, but considering the driver's need for real-time accuracy of the dynamic map of current road conditions, the first preset period can be shorter than the second preset period. Considering the low switching frequency between urban and highway sides, and the driver's reliance on the invoked road business calculation model before the target mode switch, the second preset period can be shorter than the third preset period. Those skilled in the art will realize that in practical applications, updates to other business data or algorithm models may also be required, potentially involving fourth, fifth, or other preset periods, which can be set according to specific application scenarios and needs.

[0023] With a dynamic map as a reference, the vehicle-mounted unit (VBU) has the foundation to integrate road algorithms. The above embodiment illustrates the business computation flow of a single VBU. In practical applications, there are many vehicles on the road, and the situation is more complex and changeable. This involves business computations from multiple VBUs. Simultaneously, the MEP (Mobile Execution Program) needs to integrate the computation scheduling and information aggregation of multiple VBUs, thus presenting a distributed collective learning mode for VBUs. Taking the algorithm logic flow of a single VBU as an example, it breaks down the collected complex external perception data into output data. Based on the current road business computation model, it selects useful road data for simple road business computations specific to itself. When complex business logic is involved, the task is reported to the MEP for cloud computing to obtain the final business data. It is conceivable that the VBU can also pass on unnecessary computation tasks beyond its own to other OBUs or RSUs. Due to the limitations of the VBU's local algorithm, to ensure that its algorithm results do not deviate too much from the MEP's algorithm results, it is necessary to periodically request complete map data from the MEP and synchronize the algorithm model to guarantee the algorithm's correctness. The distributed collective algorithm is used, for example... Figure 3 As shown.

[0024] To achieve better computational results for vehicles, the above embodiments need to adapt to different road environments. This is done through expert mode, matching different road computation models to achieve better computational performance with fewer computational resources. Examples are as follows: Highway mode: i. Prioritize road data for preceding and following OBUs and RSUs; ii. The MEP data update interval has been increased to 10 seconds; iii. The range of updated perception data has been expanded to 1km.

[0025] City road mode: i. Prioritize vehicle-centric circular road data; ii. The MEP data update interval is shortened to 3 seconds; iii. Add a road prediction model to determine the relevance of road perception data that exceeds the specified range. If the data is determined to be useful for future paths, the area of ​​the perception data will be dynamically changed.

[0026] In one embodiment of the present invention, the calculation of the perceived data and the target data includes: Determine each piece of data to be calculated contained in the perception data and target data, and the data type of each piece of data to be calculated; the data types include: radar data, visual data, and road data; Obtain the data weight corresponding to each data type, perform weighted aggregation calculation on the data to be calculated for each data type, and obtain the target calculation result; Feature extraction is performed on the target calculation results to obtain at least one target feature corresponding to each preset feature type; the preset feature types include: road obstacles, road risks, road conditions, and driving conditions; The target features are analyzed to obtain at least one warning message; The warning message is obtained based on the warning information.

[0027] To address the issue of unbalanced data confidence caused by existing swarm algorithms failing to consider the heterogeneous sensor characteristics of vehicles—namely, the varying data quality provided by different vehicles and roadside units due to hardware differences in real-world environments—the aforementioned embodiment employs weighted calculations for different data types. More accurate and reliable data is prioritized in the calculation, while unreliable or irrelevant data is weighted down or discarded to further ensure calculation accuracy and obtain more precise maps and warning information. In practical applications, the data weights for each data type differ, and the combination of weights also varies across different scenarios. OBU data is weighted differently based on its temporal and spatial hardware performance characteristics. RSU data serves as the primary reliable data source; the weight of OBU data only increases when RSU data is lacking on the road. Taking an intersection as an example, when a vehicle approaches an intersection, the weighted calculation uses, for example… Figure 4As shown, the input to the road traffic management calculation model is first obtained, including RSU radar data, RSU visual analysis data, RSU traffic equipment data, OBU vehicle status data, OBU radar data, and OBU visual analysis data. Then, the input data is categorized according to data type; for example, RSU radar data and OBU radar data are categorized as radar data. Next, weighted aggregation calculations are performed on the data to be calculated for each data type. When a vehicle approaches an intersection, the road environment is more complex, and there are more elements to focus on within the field of vision; therefore, the data weights for visual data and road data should be higher. Figure 4 The example shown uses radar data with a weight of 0.2, visual data with a weight of 0.5, and road data with a weight of 0.3. In practical applications, the weights of each data type will be set differently depending on the scenario and driving environment to ensure the applicability of the calculation results and provide accurate reference for safe driving. After weighted aggregation calculation of the data to be calculated for each data type, the target calculation result is obtained. Then, feature extraction is performed on the target calculation result according to the preset feature type, that is, the corresponding features for warning are collected. For example, if the features of the current vehicle turning right at a speed of 20 km / h (a driving situation) and a pedestrian crossing the road on the right side of the current vehicle (a road obstacle) are extracted, and it can be determined that if the current vehicle does not slow down, it will hit the pedestrian crossing the road, then a pedestrian warning information is obtained. If no other features are extracted, the pedestrian warning information is determined as a warning prompt information. If other features are extracted, such as the presence of an oncoming vehicle in the current vehicle's direction of travel (a road risk), then a meeting vehicle warning information is obtained. At this time, the pedestrian warning and the meeting vehicle warning are determined as warning prompt information.

[0028] In one embodiment of the present invention, the method further includes: acquiring basic safety information within a target range; and verifying the basic safety information based on the acquired dynamic data. This embodiment demonstrates that the accident vehicle automatically sends a Basic Safety Message (BSM), containing the accident location and vehicle attitude, and surrounding vehicles verify the message through their own calculations.

[0029] This invention provides an embodiment of a road condition map construction system, applied to an in-vehicle unit, such as... Figure 5 As shown, it includes: The basic map acquisition module 51 is used to acquire the basic static map of the current road segment; The perception data acquisition module 52 is used to acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data. The dynamic data acquisition module 53 is used to acquire dynamic data within the target range; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; The road condition map construction module 54 is used to draw a road condition map corresponding to the current road segment based on the perceived data, the dynamic data and the basic static map. The road condition map upload module 55 is used to send the perceived data and the dynamic data to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

[0030] In one embodiment of the present invention, after acquiring the dynamic data within the target range, the dynamic data acquisition module 53 is further configured to: Call the road business calculation model; The dynamic data is filtered using the road business calculation model to obtain target data; the filtering is performed based on the perceived data. The road operation calculation model is used to calculate the perceived data and target data to obtain early warning information; the early warning information includes at least one of the following: congestion warning, oncoming traffic warning, pedestrian warning, and red light warning.

[0031] In one embodiment of the present invention, the dynamic data acquisition module 53 is further configured to: The complexity of calculating the perceived data and target data using the road business calculation model is obtained. If the complexity exceeds a preset scale, the perceived data and target data are sent to the edge computing platform so that the edge computing platform can calculate the perceived data and target data and obtain the warning information sent by the edge computing platform. If the complexity does not exceed the preset scale, the perceived data and target data are calculated using the road business calculation model to obtain the warning information.

[0032] In one embodiment of the present invention, the method of invoking the road business calculation model includes: An algorithm model update request is sent to the edge computing platform so that the edge computing platform matches a road business calculation model of a target mode based on the road condition map; the target mode includes: highway mode and urban road mode. The vehicle unit obtains the road service calculation model application interface sent by the edge computing platform, so that the vehicle unit can call the road service calculation model through the road service calculation model application interface.

[0033] In one embodiment of the present invention, a road condition map corresponding to the current road segment is obtained according to a first preset period, and the sensing data and the dynamic data are sent to the edge computing platform; the dynamic map is requested from the edge computing platform according to a second preset period; the dynamic data is dynamic data within the target range in the dynamic map; and the road business calculation model is invoked according to a third preset period. The first preset period, the second preset period, and the third preset period are obtained based on the target mode; the first preset period, the second preset period, and the third preset period corresponding to the highway mode are greater than the first preset period, the second preset period, and the third preset period corresponding to the urban road mode; the first preset period is less than or equal to the second preset period, and the second preset period is less than or equal to the third preset period.

[0034] In one embodiment of the present invention, the calculation of the perceived data and the target data includes: Determine each piece of data to be calculated contained in the perception data and target data, and the data type of each piece of data to be calculated; the data types include: radar data, visual data, and road data; Obtain the data weight corresponding to each data type, perform weighted aggregation calculation on the data to be calculated for each data type, and obtain the target calculation result; Feature extraction is performed on the target calculation results to obtain at least one target feature corresponding to each preset feature type; the preset feature types include: road obstacles, road risks, road conditions, and driving conditions; The target features are analyzed to obtain at least one warning message; The warning message is obtained based on the warning information.

[0035] In one embodiment of the present invention, the dynamic data acquisition module 53 is further configured to: Obtain basic security information within the target area; The basic security information is verified based on the acquired dynamic data.

[0036] Figure 5 The aforementioned embodiment segments the map into sections that are constructed using OBU and RSU to alleviate computational pressure. Figure 5 The embodiment described above uses an On-Board Unit (OBU) as an example. It constructs a road condition map for the current road segment using data sensed by vehicle sensors and received local dynamic data. The Vehicle Information Processing Unit (MEP) then merges these multiple road condition maps to generate a real-time dynamic map. When needed, the OBU can directly download the constructed dynamic map from the MEP to obtain a high-precision road condition map.

[0037] Figure 5 The embodiment is as follows: Figure 1 The system implementation corresponding to the method implementation is described above. Figure 5 The implementation process and technical effects of the embodiments are as follows: Figure 1 The embodiments described are similar, therefore, for Figure 5 The description of the embodiments is relatively simple; please refer to relevant details. Figure 1 The embodiment described above.

[0038] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0039] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0040] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0041] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0042] An electronic device according to this embodiment of the present application. The electronic device is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0043] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0044] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this application.

[0045] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0046] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0047] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0048] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0049] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0050] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this application may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the "Exemplary Methods" section above.

[0051] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0052] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0053] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0054] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0055] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0056] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0057] 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. A road condition map construction method, applied to an on-board unit, characterized in that, The method includes: Obtain the basic static map of the current road segment; Acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data; Acquire dynamic data within the target area; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; Based on the perceived data, the dynamic data, and the basic static map, a road condition map corresponding to the current road segment is drawn. The sensed data and the dynamic data are sent to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

2. The method according to claim 1, characterized in that, After acquiring the dynamic data within the target range, the method further includes: Call the road business calculation model; The dynamic data is filtered using the road business calculation model to obtain target data; the filtering is performed based on the perceived data. The road operation calculation model is used to calculate the perceived data and target data to obtain early warning information; the early warning information includes at least one of the following: congestion warning, oncoming traffic warning, pedestrian warning, and red light warning.

3. The method according to claim 2, characterized in that, After filtering the dynamic data using the road business calculation model to obtain the target data, the method further includes: The complexity of calculating the perceived data and target data using the road business calculation model is obtained. If the complexity exceeds a preset scale, the perceived data and target data are sent to the edge computing platform so that the edge computing platform can calculate the perceived data and target data and obtain the warning information sent by the edge computing platform. If the complexity does not exceed the preset scale, the perceived data and target data are calculated using the road business calculation model to obtain the warning information.

4. The method according to claim 2, characterized in that, The methods for invoking the road business calculation model include: An algorithm model update request is sent to the edge computing platform so that the edge computing platform matches a road business calculation model of a target mode based on the road condition map; the target mode includes: highway mode and urban road mode. The vehicle unit obtains the road service calculation model application interface sent by the edge computing platform, so that the vehicle unit can call the road service calculation model through the road service calculation model application interface.

5. The method according to claim 4, characterized in that, According to a first preset period, the road condition map corresponding to the current road segment is obtained, and the sensing data and the dynamic data are sent to the edge computing platform; according to a second preset period, the dynamic map is requested from the edge computing platform. The dynamic data refers to the dynamic data within the target area of ​​the dynamic map; The road service calculation model is invoked according to the third preset cycle; The first preset period, the second preset period, and the third preset period are obtained based on the target pattern; The first preset period, the second preset period, and the third preset period corresponding to the highway mode are longer than the first preset period, the second preset period, and the third preset period corresponding to the urban road mode; the first preset period is less than or equal to the second preset period, and the second preset period is less than or equal to the third preset period.

6. The method according to claim 5, characterized in that, The calculation of the perceived data and target data includes: Determine each piece of data to be calculated contained in the perception data and target data, and the data type of each piece of data to be calculated; the data types include: radar data, visual data, and road data; Obtain the data weight corresponding to each data type, perform weighted aggregation calculation on the data to be calculated for each data type, and obtain the target calculation result; Feature extraction is performed on the target calculation results to obtain at least one target feature corresponding to each preset feature type; the preset feature types include: road obstacles, road risks, road conditions, and driving conditions; The target features are analyzed to obtain at least one warning message; The warning message is obtained based on the warning information.

7. The method according to claim 6, characterized in that, The method further includes: Obtain basic security information within the target area; The basic security information is verified based on the acquired dynamic data.

8. A road condition map construction system, applied to an in-vehicle unit, characterized in that, include: The basic map acquisition module is used to acquire the basic static map of the current road segment; The perception data acquisition module is used to acquire perception data collected by the sensors of the current vehicle; the perception data includes current vehicle operating status data and current vehicle location environment data. The dynamic data acquisition module is used to acquire dynamic data within the target range; the dynamic data includes at least one of the following: data sent by other vehicles, data sent by roadside units; The road condition map construction module is used to draw a road condition map corresponding to the current road segment based on the perceived data, the dynamic data and the basic static map; The road condition map upload module is used to send the perceived data and the dynamic data to the edge computing platform so that the edge computing platform can construct a dynamic map corresponding to the current road segment.

9. A non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, said at least one instruction or said at least one program segment being loaded and executed by a processor to implement the method of any one of claims 1-8.

10. An electronic device, characterized in that, Includes a processor and a non-transitory computer-readable storage medium as described in claim 9.

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