Target confidence coefficient processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a target confidence coefficient processing method and device, electronic equipment and a storage medium, and is applied to a cloud control platform, and the method comprises the steps: receiving sensing result data reported by a roadside base station and RTK truth value data reported by a vehicle; on the basis of the position of the roadside base station, according to the sensing result data and the RTK truth value data, vehicle track data in the sensing range of the current roadside base station is screened out; screening traffic participant trajectory data sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station; and matching the vehicle trajectory data with traffic participant trajectory data sensed by the current roadside base station, and issuing an error to the roadside base station, so that the roadside base station corrects a confidence index of a sensed traffic participant target. According to the invention, the accuracy of the confidence index is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle-road collaboration technology, and in particular to a target confidence processing method, device, electronic device, and storage medium. Background Art

[0002] With the large-scale promotion and implementation of the vehicle-road-cloud integrated system construction, the perception result target of the roadside base station is an important part of the vehicle-road collaborative perception, and the confidence index of the roadside base station perception result target will participate in the vehicle-road collaborative decision-making process.

[0003] However, most of the current confidence indicators of perception result targets are generated based on the rules of perception nodes, lacking real (true value) evaluation data as a basis, which leads to certain accuracy issues in the confidence indicators of perception result targets themselves. This reduces the quality of roadside perception data and also has certain interference factors in the use of roadside perception data in vehicle-road collaborative decision-making scenarios. Summary of the invention

[0004] The embodiments of the present application provide a target confidence processing method, device, electronic device, and storage medium to improve the accuracy of the confidence index.

[0005] The present application embodiment adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a target confidence processing method, which is applied to a cloud control platform, and the method includes:

[0007] Receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle;

[0008] Based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle;

[0009] According to the time range of the vehicle trajectory data within the sensing range of the current roadside base station, the trajectory data of the traffic participants sensed by the current roadside base station are filtered out;

[0010] Matching the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station;

[0011] When the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, at least one of the position, speed, heading angle and size error between the two trajectory data is calculated according to the two successfully matched trajectory data;

[0012] The error is sent to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0013] In some embodiments, when the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, after calculating at least one of the position, speed, heading angle and size error between the two successfully matched trajectory data, the method further includes:

[0014] Normalize the errors in position, speed, heading angle and size between the two;

[0015] The distance from the current evaluation position to the roadside base station is normalized and used as a distance level, and the distance level range is (0, 10);

[0016] According to the distance level, the error in the current distance level range is normalized and used as the error level, and the error level range is (0, 1).

[0017] In some embodiments, the method further comprises:

[0018] The distance level and the error level corresponding to the distance level are sent to the roadside base station according to the error update frequency or manually sent.

[0019] In some embodiments, the receiving of the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle also includes:

[0020] The vehicle is a vehicle carrying RTK real positioning, traveling within the coverage area of ​​a roadside base station, and reporting any one or more data of its own position, speed, heading angle, and size in real time and at a fixed frequency.

[0021] In some embodiments, the vehicle includes an autonomous driving vehicle, and the position of the roadside base station is based on the perception result data and the RTK true value data, and the vehicle trajectory data within the perception range of the current roadside base station is screened out, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle, including:

[0022] Based on the location of the roadside base station, the trajectories of the autonomous driving vehicles within the sensing range of the current base station are filtered and each autonomous driving vehicle contains one trajectory;

[0023] According to the time range of the filtered autonomous driving vehicle trajectory, the perception result trajectory of the current roadside base station is filtered out, wherein the perception result trajectory of the current roadside base station includes the trajectories of multiple traffic participants.

[0024] In a second aspect, an embodiment of the present application further provides a target confidence processing method, which is applied to a roadside base station sensing node, and the target confidence processing method includes:

[0025] The confidence index of the traffic participant target perceived in the method described in the first aspect is corrected in real time, and the correction method is as follows:

[0026] The perception result confidences of the position confidence, speed confidence, heading angle confidence, and size confidence of the perceived traffic participant target are corrected in real time.

[0027] In some embodiments, the method further comprises:

[0028] The confidence level of the corrected perception result is sent to the preset vehicle-road collaborative decision-making scenario.

[0029] In a third aspect, an embodiment of the present application further provides a target confidence processing device, which is applied to a cloud control platform, and the device includes:

[0030] The receiving module is used to receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle;

[0031] A first screening module is used to screen out vehicle trajectory data within the sensing range of the current roadside base station based on the position of the roadside base station, according to the sensing result data and the RTK true value data, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle;

[0032] The second screening module is used to screen out the trajectory data of traffic participants sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station;

[0033] A matching module, used to match the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station;

[0034] an error calculation module, configured to calculate at least one of the errors of position, speed, heading angle and size between the vehicle trajectory data and the traffic participant trajectory data sensed by the current roadside base station according to the two successfully matched trajectory data when the vehicle trajectory data and the traffic participant trajectory data sensed by the current roadside base station are successfully matched;

[0035] The sending module is used to send the error to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0036] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the above method.

[0037] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the above method.

[0038] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: the cloud control platform receives the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle, and based on the position of the roadside base station, the vehicle trajectory data within the perception range of the current roadside base station is screened out according to the perception result data and the RTK true value data. The time range of the vehicle trajectory within the perception range of the current roadside base station is used to screen out the trajectory data of the traffic participants perceived by the current roadside base station. Furthermore, the vehicle trajectory data is matched with the trajectory data of the traffic participants perceived by the current roadside base station; when the vehicle trajectory data is successfully matched with the trajectory data of the traffic participants perceived by the current roadside base station, the position, speed, heading angle and size errors between the two are calculated according to the two successfully matched trajectory data. Based on the real data reported by the vehicle, the error of the perception result target of the roadside base station is evaluated in real time.

[0039] The errors in position, speed, heading angle and size between the two are sent to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target, and the evaluation results are sent to the corresponding roadside base station perception node in real time. The evaluation results are used to assist in correcting the confidence index of the base station perception result target, thereby improving the accuracy of the confidence index and solving the accuracy problem of the perception result target confidence index itself.

[0040] In addition, the above method indirectly improves the quality of roadside perception data and eliminates the interference factor problem existing in the use of target confidence indicators of roadside perception results in vehicle-road collaborative decision-making scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 Schematic diagram of the process of target confidence processing method in the embodiment of the present application;

[0043] Figure 2 The following is a schematic diagram of the implementation principle of the target confidence processing method in the embodiment of the present application;

[0044] Figure 3Schematic diagram of the structure of the target confidence processing device in the embodiment of the present application;

[0045] Figure 4 This is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0047] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0048] The present application embodiment provides a method for processing target confidence. Figure 1 As shown, a flow chart of a target confidence processing method in an embodiment of the present application is provided, and the method at least includes the following steps S110 to S160:

[0049] Step S110, receiving the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle.

[0050] The cloud control platform communicates and interacts with the roadside base station. The perception result target of the roadside base station is an important part of the vehicle-road cooperative perception system, and the confidence index of the perception result target of the roadside base station will participate in the vehicle-road cooperative decision-making process. The cloud control platform can further process the received data and then send it to the perception node of the roadside base station.

[0051] The cloud control platform receives the perception result data reported by the perception node of the roadside base station. At the same time, the cloud control platform also receives the RTK true value data reported by the vehicle. It should be noted that the RTK true value data reported by the vehicle refers to the positioning result of the high-precision RTK device installed on the vehicle, which is different from the result of the vehicle's own perception and recognition. In other words, the results reported by the high-precision RTK device installed on the vehicle are very accurate, but it can only report the vehicle's own position, not the vehicle's perception results.

[0052] Afterwards, the cloud control platform uses the vehicle's reported RTK true value data to report the vehicle's position to match the road-side perception result of the road-side recognition of the vehicle, so as to obtain the accuracy of the road-side recognition result, and then calculate the confidence in each field in the road-side perception result.

[0053] Step S120, based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle.

[0054] Step S130, filtering out the trajectory data of traffic participants sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station.

[0055] like Figure 2 As shown, on the cloud control platform, based on the location of the roadside base station, the trajectory of the autonomous driving vehicle within the sensing range of the current base station is filtered.

[0056] It should be noted that the cloud control platform selects the autonomous driving vehicle tracks within the current base station sensing range based on the actual location of the roadside base station. Since there are many roadside base stations deployed, the actual location of the roadside base station can be determined based on the latitude and longitude information of the roadside base station, and the autonomous driving vehicle tracks within the current base station sensing range can be further selected.

[0057] Step S140: Match the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station.

[0058] like Figure 2 As shown, the time range of the vehicle trajectory is filtered out from the time range of the vehicle trajectory within the perception range of the current roadside base station, and then the perception result trajectory of the current roadside base station is filtered out.

[0059] Step S150, when the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, at least one of the errors in position, speed, heading angle and size between the two trajectory data are calculated according to the two successfully matched trajectory data.

[0060] like Figure 2 As shown in the figure, the cloud control platform calculates the corresponding position, speed, heading angle and size error based on the successfully matched trajectory. Because according to the standard protocol for uploading road-side perception results published by the China Society of Automotive Engineers, each field in the road-side perception recognition result is equipped with a result confidence field. Therefore, a method is to further enhance the confidence field of the road-side perception result. In other words, the more accurate this confidence field is, the more accurate the road-side provides to other downstream users, which can improve the accuracy of these downstream users in making collaborative decisions or displaying data.

[0061] The downstream user mainly refers to the party that has access to the road-side identification data, including but not limited to sending the road-side data to the vehicle-side for vehicle-road collaboration, filling in the gaps for third-party social vehicles, or providing digital large-screen displays in exhibition halls.

[0062] Step S160: Send the error to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0063] like Figure 2 As shown, the cloud control platform sends the errors of position, speed, heading angle and size between the two to the roadside base station, so that the confidence index of the perceived traffic participant target is corrected in real time at the roadside base station sensing node.

[0064] Through the above method, the target confidence of the roadside base station perception result is corrected and correctly assigned through the cloud control platform, which can improve the accuracy of the perception result confidence index, indirectly improve the quality of roadside perception data, and enable vehicle-road collaborative decision-making scenarios more widely and accurately.

[0065] Through the above method, the cloud control platform uses the real RTK data and base station perception data reported in real time to conduct real-time evaluation of the error of the base station perception results.

[0066] Different from the related technologies, most of the confidence indicators of current perception result targets are generated based on the rules of perception nodes, lacking real evaluation data as a basis. Through the above method, the cloud control platform sends the error processed data to the roadside base station perception node to correct the position confidence, speed confidence, heading angle confidence, size confidence and other perception result confidence of the perception result target in real time.

[0067] In one embodiment of the present application, when the vehicle trajectory data successfully matches the traffic participant trajectory data perceived by the current roadside base station, after calculating at least one of the errors in position, speed, heading angle and size between the two according to the two successfully matched trajectory data, it also includes: normalizing the errors in position, speed, heading angle and size between the two; normalizing the distance from the current evaluation position to the roadside base station and using it as a distance level, and the distance level range is (0, 10); according to the distance level, normalizing the error in the current distance level range and using it as an error level, and the error level range is (0, 1).

[0068] Each indicator of the position, speed, heading angle and size error between the two is normalized on the cloud control platform, and the normalization method uses the maximum and minimum values ​​for normalization. After each evaluation, the cloud control platform sends the currently processed error data to the perception node of the roadside base station in real time. The perception node of the roadside base station uses these error level indicators to correct the confidence of the results of each indicator in real time.

[0069] In the specific implementation, (1) the distance from the measured location to the base station is normalized (distance level): the closer the distance, the smaller the distance level, and the distance level range is [0, 10]. (2) according to the distance level, the error in the current distance level range is normalized to Min-Max (error level): the smaller the error, the larger the level, and the error level range is [0, 1].

[0070] In one embodiment of the present application, the method further includes: sending the distance level and the error level corresponding to the distance level to the roadside base station according to the error update frequency or in a manual sending manner.

[0071] The cloud control platform sends the distance level and the corresponding error level to the perception node of the roadside base station from the cloud control platform using the http protocol according to the error update frequency set by the cloud control platform or the manual sending method.

[0072] In one embodiment of the present application, the receiving of perception result data reported by the roadside base station and the RTK true value data reported by the vehicle also includes: the vehicle is a vehicle carrying RTK real positioning, traveling within the coverage area of ​​the roadside base station, and reporting any one or more of its own position, speed, heading angle, and size in real time and at a fixed frequency.

[0073] Vehicles with RTK real-time positioning, driving within the scope of smart roads equipped with roadside base stations, report their own position, speed, heading angle, size and other status information to the cloud control platform in real time and at a fixed frequency. Similarly, the perception nodes of the roadside base stations report the position, speed, heading angle and size of all traffic participants identified by the perception results to the cloud control platform in real time and at a fixed frequency;

[0074] In one embodiment of the present application, the vehicle includes an autonomous driving vehicle, and the vehicle trajectory data within the perception range of the current roadside base station is filtered out based on the position of the roadside base station and the perception result data and the RTK true value data, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle, including: based on the position of the roadside base station, the trajectory of the autonomous driving vehicles within the perception range of the current base station is filtered out, and each autonomous driving vehicle contains a trajectory; according to the time range of the filtered autonomous driving vehicle trajectory, the perception result trajectory of the current roadside base station is filtered out, wherein the perception result trajectory of the current roadside base station includes the trajectories of multiple traffic participants.

[0075] like Figure 2As shown in the figure, based on the roadside base station position, the cloud control platform selects the autonomous driving vehicle trajectories within the current base station perception range according to the RTK true value data reported by the vehicle, and each autonomous driving vehicle has only one trajectory. The time range of the autonomous driving vehicle trajectory is selected according to the perception result data reported by the perception node of the roadside base station, and then the perception result trajectory of the current roadside base station is selected to contain the trajectories of many traffic participants.

[0076] In an embodiment of the present application, a target confidence processing method is also provided, which is applied to a roadside base station sensing node, and the target confidence processing method includes:

[0077] The roadside base station perception node makes real-time corrections to the confidence index of the traffic participant target perceived in the method, and the correction method is as follows: the confidence of the perception result target after correction = the original perception result target confidence * the error level corresponding to the distance level of the current target; the perception result confidence of the position confidence, speed confidence, heading angle confidence, and size confidence of the perceived traffic participant target is corrected in real time.

[0078] The roadside base station perception node performs real-time correction of the perception result confidence, including the position confidence, speed confidence, heading angle confidence, size confidence, etc. of the perception result target.

[0079] In one embodiment of the present application, the method further includes: using the confidence level of the corrected perception result to send to a preset vehicle-road collaborative decision-making scenario.

[0080] The confidence level of the perception results of the roadside base station is sent to each vehicle-road collaborative decision-making scenario using the confidence level of the corrected perception results, and a decision is provided to the autonomous driving vehicle based on the preset vehicle-road collaborative decision-making scenario.

[0081] It can be understood that the embodiments of the present application do not limit the specific vehicle-road collaborative decision-making scenarios, as long as they conform to the general scenarios that are well known and recognized by technical personnel in this field.

[0082] The present application embodiment also provides a target confidence processing device 300, such as Figure 3 As shown, a schematic diagram of the structure of a target confidence processing device in an embodiment of the present application is provided, wherein the target confidence processing device 300 at least includes: a receiving module 310, a first screening module 320, a second screening module 330, a matching module 340, an error calculation module 350 and a sending module 360, wherein:

[0083] In one embodiment of the present application, the receiving module 310 is specifically used to: receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle.

[0084] The cloud control platform communicates and interacts with the roadside base station. The perception result target of the roadside base station is an important part of the vehicle-road cooperative perception system, and the confidence index of the perception result target of the roadside base station will participate in the vehicle-road cooperative decision-making process. The cloud control platform can further process the received data before sending it to the roadside base station.

[0085] The cloud control platform receives the perception result data reported by the perception node of the roadside base station. At the same time, the cloud control platform also receives the RTK true value data reported by the vehicle. It should be noted that the RTK true value data reported by the vehicle refers to the positioning result of the high-precision RTK device installed on the vehicle, which is different from the result of the vehicle's own perception and recognition. In other words, the results reported by the high-precision RTK device installed on the vehicle are very accurate, but it can only report the vehicle's own position, not the vehicle's perception results.

[0086] Afterwards, the cloud control platform uses the vehicle's reported RTK true value data to report the vehicle's position to match the road-side perception result of the road-side recognition of the vehicle, so as to obtain the accuracy of the road-side recognition result, and then calculate the confidence in each field in the road-side perception result.

[0087] In one embodiment of the present application, the first screening module 320 is specifically used to: based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle.

[0088] The second screening module 330 is further used to screen out the trajectory data of traffic participants sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station.

[0089] like Figure 2 As shown, on the cloud control platform, based on the location of the roadside base station, the trajectory of the autonomous driving vehicle within the sensing range of the current base station is filtered.

[0090] It should be noted that the cloud control platform selects the autonomous driving vehicle tracks within the current base station sensing range based on the actual location of the roadside base station. Since there are many roadside base stations deployed, the actual location of the roadside base station can be determined based on the latitude and longitude information of the roadside base station, and the autonomous driving vehicle tracks within the current base station sensing range can be further selected.

[0091] In one embodiment of the present application, the matching module 340 is specifically used to match the vehicle trajectory data with the traffic participant trajectory data perceived by the current roadside base station.

[0092] like Figure 2As shown, the time range of the vehicle trajectory is filtered out from the time range of the vehicle trajectory within the perception range of the current roadside base station, and then the perception result trajectory of the current roadside base station is filtered out.

[0093] In one embodiment of the present application, the error calculation module 350 is specifically used to: when the vehicle trajectory data successfully matches the traffic participant trajectory data perceived by the current roadside base station, calculate at least one of the errors in position, speed, heading angle and size between the two according to the two successfully matched trajectory data.

[0094] The cloud control platform calculates the corresponding position, speed, heading angle and size error based on the matched trajectory. According to the standard protocol for uploading road-side perception results published by the China Society of Automotive Engineers, each field in the road-side perception recognition result is equipped with a field for result confidence. Therefore, a method is to further enhance the field of road-side perception result confidence. In other words, the more accurate this confidence field is, the more accurate the road-side can provide to other downstream users, and the more accurate the accuracy of collaborative decision-making or data display of these downstream users can be.

[0095] The downstream user mainly refers to the party that has access to the road-side identification data, including but not limited to sending the road-side data to the vehicle-side for vehicle-road collaboration, filling in the gaps for third-party social vehicles, or providing digital large-screen displays in exhibition halls.

[0096] In one embodiment of the present application, the sending module 360 ​​is specifically used to send the error to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0097] The cloud control platform sends the errors in position, speed, heading angle and size between the two to the roadside base station, so that the confidence index of the perceived traffic participant target is corrected in real time at the roadside base station sensing node.

[0098] In one embodiment of the present application, it further includes: a normalization module for

[0099] Normalize the errors in position, speed, heading angle and size between the two;

[0100] The distance from the current evaluation position to the roadside base station is normalized and used as a distance level, and the distance level range is (0, 10);

[0101] According to the distance level, the error in the current distance level range is normalized and used as the error level, and the error level range is (0, 1).

[0102] In one embodiment of the present application, it also includes: an update module for

[0103] The distance level and the error level corresponding to the distance level are sent to the roadside base station according to the error update frequency or manually sent.

[0104] In one embodiment of the present application, it also includes: an initialization module for

[0105] The vehicle is a vehicle carrying RTK real positioning, traveling within the coverage area of ​​a roadside base station, and reporting any one or more data of its own position, speed, heading angle, and size in real time and at a fixed frequency.

[0106] In one embodiment of the present application, the screening module 320 is also used to

[0107] Based on the location of the roadside base station, the trajectories of the autonomous driving vehicles within the sensing range of the current base station are filtered and each autonomous driving vehicle contains one trajectory;

[0108] According to the time range of the filtered autonomous driving vehicle trajectory, the perception result trajectory of the current roadside base station is filtered out and includes the trajectories of multiple traffic participants.

[0109] It can be understood that the above-mentioned target confidence processing device can implement each step of the target confidence processing method provided in the above-mentioned embodiment, and the relevant explanations about the target confidence processing method are applicable to the target confidence processing device, which will not be repeated here.

[0110] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0111] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0112] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0113] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a target confidence processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0114] Receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle;

[0115] Based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle;

[0116] According to the time range of the vehicle trajectory data within the sensing range of the current roadside base station, the trajectory data of the traffic participants sensed by the current roadside base station are filtered out;

[0117] Matching the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station;

[0118] When the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, the position, speed, heading angle and size errors between the two trajectory data are calculated according to the two successfully matched trajectory data;

[0119] The errors in position, speed, heading angle and size between the two are sent to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0120] The above application Figure 1The method performed by the target confidence processing device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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 gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0121] The electronic device may also perform Figure 1 The method is executed by the target confidence processing device in Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0122] The present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 1 The method performed by the target confidence processing device in the illustrated embodiment is specifically used to perform:

[0123] Receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle;

[0124] Based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle;

[0125] According to the time range of the vehicle trajectory data within the sensing range of the current roadside base station, the trajectory data of the traffic participants sensed by the current roadside base station are filtered out;

[0126] Matching the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station;

[0127] When the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, at least one of the position, speed, heading angle and size error between the two trajectory data is calculated according to the two successfully matched trajectory data;

[0128] The error is sent to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

[0129] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0135] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0136] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0137] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A target confidence processing method, wherein: Applied to the cloud control platform, the method includes: Receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle; Based on the position of the roadside base station, according to the perception result data and the RTK true value data, filter out the vehicle trajectory data within the perception range of the current roadside base station, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle; Filtering out the trajectory data of traffic participants sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station; Matching the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station; When the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, at least one of the position, speed, heading angle and size error between the two trajectory data is calculated according to the two successfully matched trajectory data; The error is sent to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

2. The method of claim 1, wherein: In the case where the vehicle trajectory data successfully matches the traffic participant trajectory data sensed by the current roadside base station, after calculating at least one of the position, speed, heading angle and size error between the two successfully matched trajectory data, the method further includes: Normalize the errors in position, speed, heading angle and size between the two; The distance from the current evaluation position to the roadside base station is normalized and used as a distance level, and the distance level range is (0, 10); According to the distance level, the error in the current distance level range is normalized and used as the error level, and the error level range is (0, 1).

3. The method of claim 2, wherein: The method further comprises: The distance level and the error level corresponding to the distance level are sent to the roadside base station according to the error update frequency or manually sent.

4. The method of claim 1, wherein: The receiving of the sensing result data reported by the roadside base station and the RTK true value data reported by the vehicle also includes: The vehicle is a vehicle carrying RTK real positioning, traveling within the coverage area of ​​a roadside base station, and reporting any one or more data of its own position, speed, heading angle, and size in real time and at a fixed frequency.

5. The method of claim 1, wherein: The vehicle includes an autonomous driving vehicle, and the position of the roadside base station is based on the perception result data and the RTK true value data, and the vehicle trajectory data within the perception range of the current roadside base station is screened out, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle, including: Based on the location of the roadside base station, the trajectories of the autonomous driving vehicles within the sensing range of the current base station are filtered and each autonomous driving vehicle contains one trajectory; According to the time range of the filtered autonomous driving vehicle trajectory, the perception result trajectory of the current roadside base station is filtered out, wherein the perception result trajectory of the current roadside base station includes the trajectories of multiple traffic participants.

6. A target confidence processing method, wherein: Applied to a roadside base station sensing node, the target confidence processing method includes: The confidence index of the traffic participant target perceived in the method according to any one of claims 1 to 5 is corrected in real time, and the correction method is as follows: The confidence of the corrected perception result target = the confidence of the original perception result target * the error level corresponding to the distance level of the current target; The perception result confidences of the position confidence, speed confidence, heading angle confidence, and size confidence of the perceived traffic participant target are corrected in real time.

7. The method of claim 6, wherein: The method further comprises: The confidence level of the corrected perception result is sent to the preset vehicle-road collaborative decision-making scenario.

8. A target confidence processing device, wherein: Applied to the cloud control platform, the device includes: The receiving module is used to receive the perception result data reported by the roadside base station and the RTK true value data reported by the vehicle; A first screening module is used to screen out vehicle trajectory data within the sensing range of the current roadside base station based on the position of the roadside base station, according to the sensing result data and the RTK true value data, wherein the vehicle trajectory data is obtained based on the RTK true value data reported by the vehicle; The second screening module is used to screen out the trajectory data of traffic participants sensed by the current roadside base station according to the time range of the vehicle trajectory data within the sensing range of the current roadside base station; A matching module, used to match the vehicle trajectory data with the traffic participant trajectory data sensed by the current roadside base station; an error calculation module, configured to calculate at least one of the errors of position, speed, heading angle and size between the vehicle trajectory data and the traffic participant trajectory data sensed by the current roadside base station according to the two successfully matched trajectory data when the vehicle trajectory data and the traffic participant trajectory data sensed by the current roadside base station are successfully matched; The sending module is used to send the error to the roadside base station so that the roadside base station can correct the confidence index of the perceived traffic participant target.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute any one of the methods of claims 1 to 7.

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