Method, apparatus, device, medium and product for testing perceived target number

Abstract

The present disclosure provides methods, devices, equipment, media, and products for testing the number of perceived targets, relating to the field of intelligent transportation technology, specifically the field of system testing technology. A specific implementation scheme comprises: receiving perception data transmitted by a roadside perception device; determining the maximum number of perceived targets based on the perception data; and generating a perception target number test result based on the maximum number of perceived targets. This implementation method can test the maximum number of perceived targets of a roadside perception device, thereby measuring the performance of the roadside perception device based on the test results and improving the accuracy of the performance evaluation of the roadside perception device.

Description

[0001] This application is a divisional application of "Methods, devices, equipment, media and products for testing the number of perceived targets". The application date of the original application is December 15, 2021, and the application number of the original application is CN202111536694.7. The name of the invention of the original application is: Methods, devices, equipment, media and products for testing the number of perceived targets. Technical Field

[0002] The present disclosure relates to the field of intelligent transportation technology, and in particular to the field of system testing technology. Background Art

[0003] In the application scenarios of intelligent transportation, the vehicle-road cooperative roadside perception system can realize the coordinated scheduling of people, vehicles and roads through multiple components such as the traveler subsystem, vehicle-mounted subsystem, roadside subsystem, and central subsystem.

[0004] The roadside sensing equipment in the roadside subsystem detects and identifies obstacles on the road, generating perception data. The maximum number of obstacles a roadside sensing equipment can identify is a key indicator of the roadside sensing system's performance. Summary of the Invention

[0005] The present disclosure provides a method, apparatus, device, medium, and product for testing the number of perceived targets.

[0006] According to one aspect of the present disclosure, a method for testing the number of perception targets is provided, comprising: receiving perception data sent by a roadside perception device; determining a maximum number of perception targets based on the perception data; and generating a perception target number test result based on the maximum number of perception targets.

[0007] According to another aspect of the present disclosure, a device for testing the number of perception targets is provided, including: a data receiving unit, configured to receive perception data sent by a roadside perception device; a maximum perception target number determination unit, configured to determine the maximum perception target number based on the perception data; and a test result generation unit, configured to generate a perception target number test result based on the maximum perception target number.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above methods for testing the number of perceived targets.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any one of the above methods for testing the number of perceived targets.

[0010] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements any one of the above methods for testing the number of perceived targets when executed by a processor.

[0011] According to the technology disclosed in the present invention, a method for testing the number of perception targets is provided, which can test the maximum number of perception targets of roadside perception equipment and improve the accuracy of performance evaluation of roadside perception equipment.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0014] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0015] Figure 2 is a flow chart of one embodiment of a method for testing the number of perceived targets according to the present disclosure;

[0016] Figure 3 is a schematic diagram of an application scenario of the method for testing the number of perceived targets according to the present disclosure;

[0017] Figure 4 is a flow chart of another embodiment of a method for testing a perceived target number according to the present disclosure;

[0018] Figure 5 is a structural diagram of an embodiment of a device for testing the number of perceived targets according to the present disclosure;

[0019] Figure 6 It is a block diagram of an electronic device used to implement the method for testing the number of perceived targets according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] like Figure 1 As shown, system architecture 100 may include a roadside sensing device 101, a network 102, and a target vehicle 103. Network 102 is used as a medium for providing a communication link between roadside sensing device 101 and target vehicle 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables, and the wireless communication connection may include PC5 direct communication (a wireless short-range communication connection) and cellular network communication.

[0023] The roadside sensing device 101 interacts with the target vehicle 103 through the network 102 to receive or send messages, etc. Among them, the roadside sensing device 101 can be used in the roadside sensing and positioning system of vehicle-road collaborative automatic driving to detect and identify road traffic conditions, traffic participants, traffic events, etc. Specifically, the roadside sensing device 101 may include cameras, millimeter wave radars, lidars and other devices. When the roadside sensing device 101 is software, it can be installed in the devices listed above. It can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0024] The target vehicle 103 can be an autonomous driving vehicle or an ordinary vehicle. The target vehicle 103 can be equipped with electronic devices for establishing a communication connection with the roadside sensing device 101 through the network 102, such as an on-board control terminal, an on-board tablet, an on-board mobile phone, etc.

[0025] Furthermore, the solution in the embodiment of the present disclosure can be applied to a real driving environment for testing, and can also be applied to a test environment for testing, which is not limited in this embodiment.

[0026] In order to test the maximum number of perception targets that the roadside perception device 101 can perceive, a target vehicle 103 can be used to travel on a road equipped with the roadside perception device 101, and the target vehicle 103 can be used to receive perception data sent by the roadside perception device 101 through the network 102. The perception data here is at least one data. In order to improve the test accuracy of the maximum number of perception targets, multiple perception data are usually used. The target vehicle 103 can perform data analysis on the multiple perception data received, determine the number of perception targets corresponding to each perception data, and determine the maximum number of perception targets in each perception data as the maximum number of perception targets, and generate a perception target number test result based on the maximum number of perception targets. The perception target number test result here can indicate whether the maximum number of perception targets of the roadside perception device has passed the test or whether the maximum number of perception targets has failed the test.

[0027] It should be noted that the method for testing the number of perceived targets provided in the embodiment of the present disclosure can be executed by the target vehicle 103 , and the device for testing the number of perceived targets can be set in the target vehicle 103 .

[0028] It should be understood that Figure 1 The number of target vehicles, networks, and roadside perception devices in the figure is merely illustrative. Any number of target vehicles, networks, and roadside perception devices may be used depending on the implementation requirements.

[0029] Continue to refer Figure 2 , shows a process 200 of an embodiment of a method for testing the number of perceived targets according to the present disclosure. The method for testing the number of perceived targets in this embodiment includes the following steps:

[0030] Step 201: Receive perception data sent by a roadside perception device.

[0031] In this embodiment, the execution subject (such as Figure 1 The target vehicle 103 or other electronic device in the roadside sensing device can detect whether the target vehicle receives the perception data sent by the roadside sensing device. Among them, the roadside sensing device can be a component of the roadside sensing system. The roadside sensing system is used to realize the vehicle-road cooperative system in the intelligent traffic scene, and can detect and identify the road traffic operation status, traffic participants, traffic events, etc., and store, fuse and analyze the detected and identified perception data to obtain higher-precision perception result information. Among them, the roadside sensing device is used to detect and identify the road traffic operation status, traffic participants, traffic events, etc. to obtain perception data. The roadside sensing device can include but is not limited to perception cameras, millimeter-wave radars, laser radars, etc., and this embodiment does not limit this. In addition, the perception data corresponding to the perception camera can be image data, and the perception data corresponding to the millimeter-wave radar and laser radar can be radar data.

[0032] Furthermore, the perception data may include identification data of each traffic participant on the road. The types of traffic participants may include but are not limited to motor vehicles, non-motor vehicles, pedestrians, spilled or low obstacles and special targets. Specifically, motor vehicles may include but are not limited to cars, trucks, buses, emergency or special vehicles; non-motor vehicles may include but are not limited to bicycles, motorcycles, tricycles, bicycles; spilled or low obstacles may include but are not limited to cones, triangular warning signs, animals, cartons, tires; special targets may include but are not limited to stone piers, water barriers, and pillars. Among them, the classification of motor vehicles may meet the GA802 standard (a road traffic management specification, including specifications for motor vehicle types).

[0033] To test the upper limit of the number of traffic participant targets that a roadside perception system can stably detect at the same time, a target vehicle can be used to receive at least one perception data set transmitted by a roadside perception device and analyze the perception data to determine the number of traffic participants corresponding to the perception data. Based on the number of traffic participants, a maximum number of perception targets is determined, and a perception target number test result is generated based on the maximum number of perception targets. Preferably, a target vehicle can be used to receive multiple perception data sets transmitted by a roadside perception device and determine the number of traffic participants corresponding to each perception data set. The maximum number of traffic participants is then determined as the maximum number of perception targets.

[0034] Optionally, the target vehicle includes an autonomous vehicle or a conventional vehicle. If the target vehicle is an autonomous vehicle, the execution entity may be the autonomous vehicle's onboard control device. If the target vehicle is a conventional vehicle, the execution entity may be a conventional vehicle's onboard tablet, mobile phone, or other device.

[0035] Optionally, the target vehicle receives the perception data set transmitted by the roadside perception device based on a pre-established communication connection with the roadside perception device; the communication connection includes a wired communication connection or a wireless communication connection. Further, optionally, the wireless communication connection may include a wireless short-range communication connection or a cellular network communication connection.

[0036] In some optional implementations of this embodiment, receiving the perception data sent by the roadside perception device may include: receiving the perception data sent by the roadside perception device within at least one preset time period.

[0037] In this implementation, the execution entity may obtain at least one preset time period. For each time period, if a target vehicle is detected receiving perception data transmitted by a roadside perception device during that time period, the execution entity may analyze the perception data to determine the maximum number of perceived targets. The at least one preset time period may be pre-set or randomly selected based on the time the perception data was received, which is not a limitation in this embodiment.

[0038] Step 202: Determine the maximum number of perceived targets based on the perception data.

[0039] In this embodiment, after obtaining multiple perception data, the execution entity can determine the number of perception targets corresponding to each perception data, and determine the maximum number of perception targets as the maximum number of perception targets. Alternatively, if the perception data is image data captured by a perception camera, the number of perception targets corresponding to the image data can be determined based on existing image analysis techniques. If the perception data is radar data collected by a millimeter-wave radar or lidar, the number of perception targets corresponding to the radar data can be determined based on existing data analysis techniques.

[0040] Among them, the maximum number of perception targets refers to the upper limit of traffic participant targets that can be detected by roadside perception equipment.

[0041] Step 203: Generate a perception target number test result based on the maximum perception target number.

[0042] In this embodiment, the execution entity may pre-set a reasonable range or minimum threshold corresponding to the maximum number of perceived targets. After determining the maximum number of perceived targets, it may be determined whether the maximum number of perceived targets is within a reasonable range. If so, the maximum number of perceived targets passes the test; otherwise, the maximum number of perceived targets fails the test, and a perceived target number test result is obtained. Alternatively, after determining the maximum number of perceived targets, it may be determined whether the maximum number of perceived targets is greater than the minimum threshold. If so, the maximum number of perceived targets passes the test; otherwise, the maximum number of perceived targets fails the test, and a perceived target number test result is obtained.

[0043] In some optional implementations of this embodiment, a perception target number test result is generated based on the maximum perception target number, including: in response to determining that the maximum perception target number meets the preset target number condition, generating a perception target number test result indicating that the roadside perception device has passed the test.

[0044] In this implementation, the preset target number condition may be that the maximum number of perceived targets is greater than a preset threshold, or the preset target number condition may be that the maximum number of perceived targets is within a preset target number range. This embodiment does not limit the specific delay condition.

[0045] Continue to see Figure 3 , which shows a schematic diagram of an application scenario of the method for testing the number of perceived targets according to the present disclosure. Figure 3In an application scenario, a target vehicle 301 can travel on a road equipped with a roadside sensing device 302, and the target vehicle 301 can establish a communication connection with the roadside sensing device 302. The roadside sensing device 302 can obtain sensing data and send the sensing data to the target vehicle 301. The sensing data may include identification of traffic participants, such as the number of identified traffic participants. The target vehicle 301 can parse the sensing data to obtain the maximum number of sensing targets, and generate a sensing target number test result based on the maximum number of sensing targets. For example, the roadside sensing device 301 can send three sensing data to the target vehicle 301 within a preset time period. The first sensing data can identify the target vehicle 301 and other vehicles 303 and 304. The second sensing data can identify the target vehicle 301, a bicycle 305, and other vehicles 303 and 304. The third sensing data can identify the target vehicle 301, a bicycle 305, four pedestrians 306, and other vehicles 303 and 304. At this point, the target vehicle 301 can determine the number of perceived targets corresponding to the third perception data as the maximum number of perceived targets, that is, the maximum number of perceived targets is 8. If the maximum number of perceived targets is greater than the preset threshold, it is considered that the roadside perception device can perceive a large number of targets, and the perception target number test has passed. If the maximum number of perceived targets is less than or equal to the preset threshold, it is considered that the roadside perception device can perceive a small number of targets, and the perception target number test has failed.

[0046] The method for testing the number of perception targets provided in the above embodiments of the present disclosure can test the maximum number of perception targets of roadside perception equipment and improve the accuracy of performance evaluation of roadside perception equipment.

[0047] Continue to see Figure 4 , which shows a process 400 of another embodiment of the method for testing the number of perceived targets according to the present disclosure. Figure 4 As shown, the method for testing the number of perceived targets in this embodiment may include the following steps:

[0048] Step 401: receiving perception data sent by a roadside perception device in a test environment; wherein the test environment includes an open test environment, a closed test environment, or a semi-closed test environment.

[0049] In this embodiment, the execution subject (such as Figure 1 The target vehicle 103 or other electronic device in the test environment can control the target vehicle to travel along a preset road and receive perception data sent by the roadside perception device to the target vehicle.

[0050] Among them, the test environment can meet the following conditions: the test road environment is open, unobstructed, and free of interference; there is no severe weather conditions such as snowfall, hail, and dust; the ambient temperature is -20℃ to 60℃; the relative humidity is 25% to 75%; the air pressure is 86kPa to 106kPa; the horizontal visibility should be greater than 500m; the electromagnetic environment of the test field will not affect the network communication test; the length of the test road should be greater than 500m, the longitudinal slope should be less than 0.5%, and the transverse slope should be less than 3%; the test environment must ensure RSU (RoadSide Unit) signal coverage.

[0051] Among them, the target vehicle can meet the following conditions: it has wireless communication capabilities, and the communication distance is greater than or equal to 300m under open, unobstructed and interference-free conditions; the sending of V2X (Vehicle to Everything, communication between the vehicle-mounted unit and other devices) messages shall comply with the YD / T 3340-2018, YD / T 3707-2020, YD / T 3709-2020 and T / CSAE 53-2020 standards and specifications; it should support obtaining data information such as vehicle speed, gear information, vehicle steering wheel angle, status of lights around the vehicle body, vehicle event signs, vehicle four-axis acceleration, and vehicle braking system status from the vehicle data bus or other data sources.

[0052] For the detailed description of step 401 , please refer to the detailed description of step 201 , which will not be repeated here.

[0053] Step 402: In response to determining that the number of perception data received from the roadside perception device is multiple, determine the number of perception targets corresponding to each perception data.

[0054] In this embodiment, the execution entity may perform data analysis on each perception data to determine the number of traffic participants identified by the perception data, that is, to obtain the number of perception targets corresponding to the perception data.

[0055] Step 403: Determine the maximum number of perception targets from the number of perception targets corresponding to each perception data.

[0056] In this embodiment, the execution subject may determine the maximum number of perception targets with the largest number of perception targets from the number of perception targets corresponding to each perception data.

[0057] For the detailed description of steps 402 - 403 , please refer to the detailed description of step 202 , which will not be repeated here.

[0058] Step 404 : In response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment, output information indicating that additional targets are to be added.

[0059] In this embodiment, when testing is performed in a test environment, test target substitutes may be used to replace real targets such as pedestrians, non-motor vehicles, and motor vehicles.

[0060] Among them, the total number of targets in the test environment can be the number of all traffic participants set in the test environment. If the maximum number of perceived targets is the same as the total number of targets in the test environment, it means that the total number of targets currently set in the test environment may not meet the upper limit of the perception of traffic participants by the roadside perception equipment. For example, the total number of targets in the test environment is 8, and the maximum number of perceived targets obtained in the test is 8. It is possible that the maximum number of perceived targets can originally identify 9 or more traffic participants, but due to the limitations of the test environment, only 8 traffic participants can be identified. In this case, the execution entity can output information for indicating the addition of targets.

[0061] In some optional implementations of this embodiment, in response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment, outputting information indicating that additional targets are to be added may include: in response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment, determining a target number to be added based on the maximum number of perceived targets and a preset target addition ratio, and outputting information indicating that the target number of additional targets is to be added. By implementing this optional implementation, the number of targets to be added may also be determined based on the preset target addition ratio and the maximum number of perceived targets, thereby improving the accuracy of the output information.

[0062] Step 405 : In response to determining that the maximum number of perceived targets is different from the total number of targets in the test environment, generating a perceived target number test result based on the maximum number of perceived targets.

[0063] In this embodiment, if the maximum number of perceived targets is different from the total number of targets in the test environment and the maximum number of perceived targets is less than the total number of targets, a perceived target number test result is generated based on the maximum number of perceived targets.

[0064] Among them, for the specific implementation steps of generating the perception target number test results based on the maximum perception target number, please refer to the detailed description of step 203, which will not be repeated here.

[0065] In some optional implementations of this embodiment, in response to determining that the maximum number of perceived targets is different from the total number of targets in the test environment and the maximum number of perceived targets is greater than the total number of targets, a prompt for reporting an error is output.

[0066] In some optional implementations of this embodiment, a perception target number test result is generated based on the maximum perception target number, including: generating a perception target number test result within a preset time period based on the maximum perception target number, wherein the data reception time corresponding to each perception data is within the preset time period.

[0067] In this implementation, the execution entity can obtain the perception data sent by the roadside perception device to the target vehicle over a period of time, and for each perception data received during this period, determine the number of perception targets corresponding to the perception data. The maximum number of perception targets is determined from each perception target number, and based on the maximum number of perception targets, a perception target number test result for a period of time is generated. Specifically, if the number of perception targets is greater than a preset threshold, it is determined that the roadside perception device has passed the test within the preset time period. If the number of perception targets is less than or equal to the preset threshold, it is determined that the roadside perception device has failed the test within the preset time period.

[0068] Step 406: Based on the perception target number test result, output prompt information for the roadside perception device.

[0069] In this embodiment, the prompt information may include but is not limited to performance evaluation prompts for roadside perception equipment, maintenance prompts for roadside perception equipment, early warning prompts for roadside perception equipment, etc. This embodiment does not limit this.

[0070] For example, if the prompt information is a performance evaluation prompt for the roadside perception device, the maximum number of perception targets provided by the manufacturer channel or other channels of the roadside perception device can be obtained in advance, and based on the maximum number of perception targets, the corresponding target number conditions can be generated, such as greater than or equal to the maximum number of perception targets. By performing the above-mentioned maximum perception target number test steps for the roadside perception device in a test environment or a real driving environment, the perception target number test results can be obtained. If the perception target number test result indicates that the test has not passed, specifically, the maximum perception target number obtained by the test is less than the maximum perception target number initially provided, then a prompt information can be generated to indicate that the performance of the roadside perception device does not conform to the pre-marking.

[0071] Alternatively, if the prompt message is for a roadside sensing device maintenance reminder, a target number condition can be set based on the maintenance standard. In this case, the maximum threshold corresponding to the target number condition can be set to a smaller range of target numbers. If the perceived target number test result indicates a failure, specifically because the maximum perceived target number obtained in the test is less than the smaller range of target numbers, a prompt message can be generated to indicate that the roadside sensing device requires maintenance.

[0072] In some optional implementations of this embodiment, outputting prompt information for the roadside sensing device based on the perceived target number test results may include outputting prompt information for the roadside sensing device based on the perceived target number test results corresponding to each round of testing. This optional implementation can improve the accuracy of the prompt information based on multiple rounds of testing.

[0073] The method for testing the number of perception targets provided by the above-mentioned embodiments of the present disclosure can also, in a test environment, determine the number of perception targets corresponding to each perception data item based on the perception data received by the target vehicle from the roadside perception device, determine the maximum number of perception targets as the maximum number of perception targets, and output information indicating the addition of additional targets when the maximum number of perception targets is the same as the total number of targets in the test environment. This can reduce test errors caused by unreasonable setting of the total number of targets in the test environment and improve test accuracy. Furthermore, prompt information can be output based on the perception target number test results to facilitate dynamic performance evaluation and timely maintenance of the roadside perception device.

[0074] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for testing the number of perceived targets. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to electronic devices such as vehicle-mounted control equipment, vehicle-mounted mobile phones, and vehicle-mounted tablets in the target vehicle.

[0075] like Figure 5 As shown, the apparatus 500 for testing the number of perception targets of this embodiment includes: a data receiving unit 501 , a maximum perception target number determining unit 502 , and a test result generating unit 503 .

[0076] The data receiving unit 501 is configured to receive the sensing data sent by the roadside sensing device.

[0077] The maximum perception target number determining unit 502 is configured to determine the maximum perception target number based on the perception data.

[0078] The test result generating unit 503 is configured to generate a perception target number test result based on the maximum perception target number.

[0079] In some optional implementations of this embodiment, the above-mentioned device is applied to a target vehicle, which includes an autonomous driving vehicle or an ordinary vehicle.

[0080] In some optional implementations of this embodiment, the target vehicle receives the perception data sent by the roadside perception device based on a communication connection pre-established with the roadside perception device; the communication connection includes at least a wireless short-range communication connection.

[0081] In some optional implementations of this embodiment, the data receiving unit 501 is further configured to: receive the perception data sent by the roadside perception device within at least one preset time period.

[0082] In some optional implementations of this embodiment, the maximum perception target number determination unit 502 is further configured to: in response to determining that the number of perception data received from the roadside perception device is multiple, determine the number of perception targets corresponding to each perception data; and determine the maximum perception target number from the perception target numbers corresponding to each perception data.

[0083] In some optional implementations of this embodiment, the test result generation unit 503 is further configured to: generate a perception target number test result within a preset time period based on the maximum perception target number, wherein the data reception time corresponding to each perception data is within the preset time period.

[0084] In some optional implementations of this embodiment, the method further includes: an information output unit configured to output information indicating the addition of additional targets in response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment.

[0085] In some optional implementations of this embodiment, the test result generation unit 503 is further configured to: in response to determining that the maximum number of perception targets is different from the total number of targets in the test environment, generate a perception target number test result based on the maximum number of perception targets.

[0086] In some optional implementations of this embodiment, the test result generation unit 503 is further configured to: in response to determining that the maximum number of perception targets meets the preset target number condition, generate a perception target number test result indicating that the roadside perception device has passed the test.

[0087] In some optional implementations of this embodiment, it further includes: a prompt output unit configured to output prompt information for the roadside perception device based on the perception target number test result.

[0088] It should be understood that the units 501 to 503 described in the apparatus 500 for testing the number of perceived targets are respectively the same as those in the reference Figure 2 Therefore, the operations and features described above for the method for testing the number of perceived targets are also applicable to the device 500 and the units contained therein, and will not be repeated here.

[0089] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0090] Figure 6A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0092] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method for testing the number of perceived targets. For example, in some embodiments, the method for testing the number of perceived targets can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for testing the number of perceived targets described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the method for testing the number of perceived targets by any other suitable means (e.g., via firmware).

[0094] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0099] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0100] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0101] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for testing the number of perceived targets, comprising: Receive perception data sent by roadside perception equipment; Determining a maximum number of perceived targets based on the perception data includes: in response to determining that a plurality of perception data are received from the roadside perception device, determining a number of perception targets corresponding to each perception data; and determining the maximum number of perception targets from the numbers of perception targets corresponding to each perception data, wherein the maximum number of perception targets is used to represent an upper limit of traffic participant targets detected by the roadside perception device; In response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment, outputting information for instructing to add targets, including: determining a number of additional targets based on the maximum number of perceived targets and a preset target addition ratio, and outputting information for instructing to add targets by the number of additional targets; Based on the maximum number of perceived targets, a perception target number test result is generated.

2. The method according to claim 1 is applied to a target vehicle, wherein the target vehicle includes an autonomous driving vehicle or an ordinary vehicle.

3. The method according to claim 2, wherein: The target vehicle receives the perception data sent by the roadside perception device based on a communication connection established in advance with the roadside perception device; the communication connection at least includes a wireless short-range communication connection.

4. The method according to claim 1, wherein The generating a perception target number test result based on the maximum perception target number includes: Based on the maximum number of perception targets, a perception target number test result within a preset time period is generated, wherein the data reception time corresponding to each of the perception data is within the preset time period.

5. The method according to claim 1, wherein The receiving of the sensing data sent by the roadside sensing device includes: In a test environment, the perception data sent by the roadside perception device is received; wherein the test environment includes an open test environment, a closed test environment, or a semi-closed test environment.

6. The method according to claim 5, wherein: The generating a perception target number test result based on the maximum perception target number includes: In response to determining that the maximum number of perceived targets is different from the total number of targets in the test environment, the perceived target number test result is generated based on the maximum number of perceived targets.

7. The method according to claim 1, wherein The generating a perception target number test result based on the maximum perception target number includes: In response to determining that the maximum number of perceived targets meets a preset target number condition, a perception target number test result is generated to indicate that the roadside perception device has passed the test.

8. The method according to claim 1, further comprising: Based on the perception target number test result, prompt information for the roadside perception device is output.

9. A device for testing the number of perceived targets, comprising: a data receiving unit, configured to receive sensing data sent by a roadside sensing device; a maximum perception target number determining unit configured to determine a maximum perception target number based on the perception data, wherein the maximum perception target number is used to represent an upper limit of traffic participant targets detected by the roadside perception device; an information output unit configured to output information indicating that additional targets are to be added in response to determining that the maximum number of perceived targets is the same as the total number of targets in the test environment; a test result generating unit configured to generate a perception target number test result based on the maximum perception target number; The maximum perception target number determination unit is further configured to: In response to determining that a plurality of the sensing data are received from the roadside sensing device, determining a number of sensing targets corresponding to each sensing data; Determining the maximum number of perception targets from the number of perception targets corresponding to each of the perception data; The information output unit is further configured to: Based on the maximum number of perceived targets and a preset target addition ratio, the number of additional targets is determined, and information indicating that the number of additional targets is to be added is output.

10. The device according to claim 9, applied to a target vehicle, wherein the target vehicle includes an autonomous driving vehicle or an ordinary vehicle.

11. The device according to claim 10, wherein The target vehicle receives the perception data sent by the roadside perception device based on a communication connection established in advance with the roadside perception device; the communication connection at least includes a wireless short-range communication connection.

12. The device according to claim 9, wherein The test result generating unit is further configured to: Based on the maximum number of perception targets, a perception target number test result within a preset time period is generated, wherein the data reception time corresponding to each of the perception data is within the preset time period.

13. The device according to claim 9, wherein The data receiving unit is further configured to: In a test environment, the perception data sent by the roadside perception device is received; wherein the test environment includes an open test environment, a closed test environment, or a semi-closed test environment.

14. The device according to claim 13, wherein The test result generating unit is further configured to: In response to determining that the maximum number of perceived targets is different from the total number of targets in the test environment, the perceived target number test result is generated based on the maximum number of perceived targets.

15. The device according to claim 9, wherein The test result generating unit is further configured to: In response to determining that the maximum number of perceived targets meets a preset target number condition, a perception target number test result is generated to indicate that the roadside perception device has passed the test.

16. The apparatus according to claim 9, further comprising: The prompt output unit is configured to output prompt information for the roadside perception device based on the perception target number test result.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

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