Service control methods, devices, electronic equipment, and storage media for roadside equipment

Abstract

This application discloses a service control method, device, electronic equipment, and storage medium for roadside equipment. The method includes: acquiring operational data of the roadside equipment and driving data of an autonomous vehicle; determining the service status of the roadside equipment's perception data based on the operational data and driving data of the autonomous vehicle, whereby the service status includes an available status and an unavailable status, and the available status includes a reliable status and a questionable status; and controlling the service range of the roadside equipment based on the service status of the roadside equipment's perception data. This application evaluates the service quality of roadside equipment by acquiring and analyzing the operational data of the roadside equipment and the driving data of the autonomous vehicle in real time. Based on the evaluation results, problems with the roadside equipment can be identified in a timely manner, and the service range can be adjusted accordingly. This avoids the roadside equipment sending abnormal data to the autonomous vehicle for processing, thereby improving the safety of the roadside equipment's service to the autonomous vehicle.

Description

Technical Field

[0001] This application relates to the field of service monitoring technology for roadside equipment, and in particular to a service control method, device, electronic equipment, and storage medium for roadside equipment. Background Technology

[0002] As single-vehicle autonomous driving gradually reaches its bottleneck, V2X (Vehicle-to-Everything) vehicle-to-everything (V2X) solutions are increasingly considered an essential path to achieving fully autonomous driving. However, due to factors such as weather, electrical circuits, and network issues, V2X roadside equipment may experience service quality fluctuations and degradation. In such cases, the data provided by V2X roadside equipment to autonomous vehicles may have quality problems, which could significantly impact the autonomous driving system's ability to make sound decisions and potentially lead to accidents.

[0003] The availability of existing roadside equipment is mainly verified through manual testing by testers and maintenance personnel. Verification is time-consuming, requiring one person and one vehicle per day for the verification of a single roadside device. The verification cycle is long, and a single roadside device will not be verified again for at least one month after verification. This means that maintenance and testing personnel cannot be aware of problems with the service of roadside equipment in a timely manner, and retesting requires a lot of manpower, resulting in a high probability that problematic data will be sent to autonomous vehicles. Summary of the Invention

[0004] This application provides a service control method, device, electronic device, and storage medium for roadside equipment, enabling timely detection of problems with roadside equipment and adjustment of the service range of roadside equipment, thereby improving the safety of autonomous vehicles.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a service control method for roadside equipment, wherein the method includes:

[0007] Acquire operational data from roadside equipment and driving data from autonomous vehicles;

[0008] The service status of the perception data of the roadside equipment is determined based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0009] The service range of the roadside equipment is controlled based on the service status of the sensing data of the roadside equipment.

[0010] Optionally, the roadside equipment includes multiple roadside sensors, and determining the service status of the roadside equipment's perception data based on the operating data of the roadside equipment and the driving data of the autonomous vehicle includes:

[0011] The online status of each roadside sensor is determined based on the operating data of each roadside sensor;

[0012] The service status of the sensing data of each roadside sensor is determined based on the online status of each roadside sensor and the type of each roadside sensor.

[0013] Optionally, the roadside sensors include roadside cameras and roadside lidar, and determining the service status of the sensing data of each roadside sensor based on the online status and type of each roadside sensor includes:

[0014] If all roadside sensors are online, then the service status of the sensing data of each roadside sensor is determined to be available.

[0015] If any one or more roadside sensors are offline, the service status of the sensing data of the offline roadside sensors is determined to be unavailable. Based on the type of the offline roadside sensors, the service status of the sensing data of the online roadside sensors is determined to be either unavailable or questionable.

[0016] Optionally, determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes:

[0017] The data acquisition frequency of the roadside equipment is determined based on its operating data.

[0018] If the data collection frequency of the roadside equipment is lower than the first preset frequency threshold, the service status corresponding to the sensing data of the roadside equipment is determined to be questionable.

[0019] If the data collection frequency of the roadside equipment is lower than the second preset frequency threshold, then the service status corresponding to the sensing data of the roadside equipment is determined to be unavailable.

[0020] Otherwise, the perceived data of the roadside equipment is determined to be a trusted service state.

[0021] Wherein, the first preset frequency threshold is greater than the second preset frequency threshold.

[0022] Optionally, the driving data includes log data, and determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes:

[0023] The communication latency of the roadside equipment and / or the data transmission frequency of the roadside equipment are determined based on the log data, wherein the communication latency includes the latency of each stage of the roadside algorithm and the end-to-end latency;

[0024] The service status of the sensing data of the roadside equipment is determined based on the operating data of the roadside equipment, the communication latency of the roadside equipment, and / or the data transmission frequency of the roadside equipment.

[0025] Optionally, determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes:

[0026] Data mining is performed on the operating data of the roadside equipment and the driving data of the autonomous vehicle using a preset data mining strategy to obtain data mining results. The data mining results include at least one of the following: the perception error of the roadside equipment, the service rate of the roadside equipment, and the traffic light accuracy of the roadside equipment.

[0027] The service status of the sensing data of the roadside equipment is determined based on the data mining results.

[0028] Optionally, controlling the service range of the roadside equipment based on the service status of the roadside equipment's sensing data includes:

[0029] The roadside equipment's sensing data is marked and / or anomaly alarms are initiated based on the service status of the sensing data.

[0030] Secondly, embodiments of this application also provide a service control device for roadside equipment, wherein the device includes:

[0031] The acquisition unit is used to acquire the operating data of roadside equipment and the driving data of autonomous vehicles;

[0032] The determining unit is configured to determine the service status of the perception data of the roadside equipment based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0033] The control unit is used to control the service range of the roadside equipment based on the service status of the sensing data of the roadside equipment.

[0034] Thirdly, embodiments of this application also provide an electronic device, including:

[0035] Processor; and

[0036] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0037] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform any of the methods described above.

[0038] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: The service control method for roadside equipment in this application embodiment first acquires the operating data of the roadside equipment and the driving data of the autonomous vehicle; then, it determines the service status of the perception data of the roadside equipment based on the operating data of the roadside equipment and the driving data of the autonomous vehicle, the service status including available status and unavailable status, and the available status including trusted status and questionable status; then, it controls the service range of the roadside equipment based on the service status of the perception data of the roadside equipment. The service control method for roadside equipment in this application embodiment evaluates the service quality of the roadside equipment by acquiring the operating data of the roadside equipment and the driving data of the autonomous vehicle in real time and performing mining analysis. Based on the evaluation results, it can promptly discover problems existing in the roadside equipment and adjust the service range according to the problems, avoiding the roadside equipment from sending abnormal data to the autonomous vehicle for processing, thus improving the safety of the roadside equipment's service to the autonomous vehicle. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0040] Figure 1 This is a flowchart illustrating a service control method for roadside equipment according to an embodiment of this application;

[0041] Figure 2 This is a schematic diagram of the structure of a service control device for a roadside equipment according to an embodiment of this application;

[0042] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0045] This application provides a service control method for roadside equipment, such as... Figure 1 The diagram shows a flowchart of a service control method for roadside equipment according to an embodiment of this application. The method includes at least the following steps S110 to S130:

[0046] Step S110: Obtain the operating data of the roadside equipment and the driving data of the autonomous vehicle.

[0047] The service control method for roadside equipment in this application embodiment can be executed by a cloud server. When performing service control of roadside equipment, it is necessary to first obtain the operating data of the roadside equipment in real time based on communication between the cloud and the roadside, and then obtain the driving data of the autonomous vehicle in real time based on communication between the cloud and the vehicle. The operating data of the roadside equipment mainly includes the communication status and perception data of the roadside equipment, which can be subscribed to according to actual monitoring needs. Autonomous vehicles refer to vehicles that enter the perception range of the roadside equipment. The driving data of autonomous vehicles mainly includes data perceived by the autonomous vehicle's sensors during driving and log data.

[0048] Step S120: Determine the service status of the perception data of the roadside equipment based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0049] By deeply mining and analyzing data from two dimensions—operational data of roadside equipment and driving data of autonomous vehicles—the service status of the perception data of roadside equipment can be determined. It should be clarified that the "service status" defined in this application embodiment does not simply refer to the overall service status of each roadside equipment, but rather to the service status of the different perception data generated by different roadside equipment. In other words, the evaluation of the service quality of roadside equipment is refined to the data level, determining which perception data from roadside equipment is reliable (i.e., has a high degree of confidence), which perception data is questionable, and which perception data is unusable, thereby providing a basis for selection when sending perception data from the roadside to the vehicle.

[0050] Step S130: Control the service range of the roadside equipment based on the service status of the sensing data of the roadside equipment.

[0051] The service status of the roadside equipment's sensing data, determined by the aforementioned steps, is fed back to the roadside equipment. This allows the roadside equipment to selectively send data to the vehicle based on the service status of the corresponding sensing data, thereby achieving the goal of dynamically adjusting the service range of the roadside equipment. This avoids the problem of the roadside equipment sending abnormal data to the vehicle for processing, and also avoids the problem that normal data may not be processed by the vehicle when there is some abnormal data on the roadside equipment.

[0052] The service control method for roadside equipment in this application evaluates the service quality of roadside equipment by acquiring and analyzing the real-time operating data of roadside equipment and the driving data of autonomous vehicles. Based on the evaluation results, problems with roadside equipment can be identified in a timely manner and the service scope can be adjusted accordingly. This avoids roadside equipment sending abnormal data to autonomous vehicles for processing, improves the safety of roadside equipment services to autonomous vehicles, and reduces the possibility of accidents involving autonomous vehicles. Furthermore, this method can be executed in real time and has high efficiency in problem detection.

[0053] In some embodiments of this application, the roadside equipment includes multiple roadside sensors, and determining the service status of the roadside equipment's perception data based on the roadside equipment's operating data and the autonomous vehicle's driving data includes: determining the online status of each roadside sensor based on the operating data of each roadside sensor; and determining the service status of each roadside sensor's perception data based on the online status of each roadside sensor and the type of each roadside sensor.

[0054] The roadside equipment in this application embodiment may include multiple roadside sensors, such as roadside cameras and roadside lidar. When determining the service status of the sensing data of the roadside equipment, the online status of each roadside sensor can be determined first based on the operating data of each roadside sensor. The online status here refers to whether the roadside sensor and the edge computing unit of the roadside equipment can communicate normally. Specifically, it can be determined by using a timed Ping (Packet Internet Groper) command.

[0055] After determining the online status of each roadside sensor, it's further possible to identify its type. This classification includes not only roadside cameras and LiDAR, but also primary and secondary sensors. Roadside equipment typically has a pre-defined primary sensor. The online status and specific type of each roadside sensor affect the service status of its own collected data, as well as the service status of data collected by other sensors. Therefore, the service status of each roadside sensor's data can be determined based on its online status and type.

[0056] In some embodiments of this application, the roadside sensors include roadside cameras and roadside lidar. Determining the service status of the sensing data of each roadside sensor based on the online status and type of each roadside sensor includes: if all roadside sensors are online, then the service status of the sensing data of each roadside sensor is determined to be available; if any one or more roadside sensors are offline, then the service status of the sensing data of the offline roadside sensors is determined to be unavailable, and the service status of the sensing data of the online roadside sensors is determined to be unavailable or questionable based on the type of the offline roadside sensors.

[0057] If all roadside sensors are online, then the service status of the sensing data of each roadside sensor can be determined as available. Here, we can further combine other evaluation indicators to determine whether the service status of the sensing data of each roadside sensor is reliable or questionable.

[0058] If one or more roadside sensors are offline, the service status of their sensing data can be determined as unavailable. The service status of the remaining online roadside sensors can be further determined by considering the specific type of the offline sensor. For example, if a roadside camera is offline and is the primary sensor, the lack of crucial visual perception information means that the service status of all roadside sensor data is unavailable, and no further sensing data will be sent to the vehicle. If a roadside lidar is offline and is the primary sensor, the speed and heading angle data can be considered unreliable, thus the service status of these data is questionable. In other words, the offline status of different roadside sensors has varying impacts on determining the service status of the sensing data from other sensors.

[0059] In some embodiments of this application, determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes: determining the roadside device's data collection frequency based on the roadside device's operating data; if the roadside device's data collection frequency is lower than a first preset frequency threshold, then determining the service status corresponding to the roadside device's perception data as a questionable state; if the roadside device's data collection frequency is lower than a second preset frequency threshold, then determining the service status corresponding to the roadside device's perception data as an unavailable state; otherwise, then determining the roadside device's perception data as a reliable state; wherein, the first preset frequency threshold is greater than the second preset frequency threshold.

[0060] Based on the operating data of the roadside equipment, the data acquisition frequency of each roadside sensor can be determined, and it can be further determined whether the data acquisition frequency of each roadside sensor is normal. Here, the data acquisition frequency of each roadside sensor can be compared with the corresponding preset frequency threshold. Different roadside sensors usually have different data acquisition frequencies, so the corresponding preset frequency thresholds are also different.

[0061] Furthermore, to further refine the assessment of the service capabilities of roadside equipment, multiple preset frequency thresholds can be set. For example, a first preset frequency threshold and a second preset frequency threshold can be set, with the first preset frequency threshold being greater than the second preset frequency threshold. When the data acquisition frequency of the roadside sensor is lower than the first preset frequency threshold, the service status of the sensor's perception data can be determined to be questionable. When the data acquisition frequency of the roadside sensor is lower than the second preset frequency threshold, the service status of the sensor's perception data can be determined to be unavailable, meaning the data acquisition frequency is too low to meet the real-time requirements of autonomous vehicles and cannot be used on the vehicle side. The specific values ​​of the first and second preset frequency thresholds can be flexibly set according to actual needs and are not specifically limited here.

[0062] In some embodiments of this application, the driving data includes log data, and determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes: determining the roadside device's communication latency and / or the roadside device's data transmission frequency based on the log data, wherein the communication latency includes the latency of each stage of the roadside algorithm and the end-to-end latency; and determining the service status of the roadside device's perception data based on the roadside device's operating data and the roadside device's communication latency and / or the roadside device's data transmission frequency.

[0063] The vehicle-side log data can specifically include timestamps of the roadside algorithm at each stage of the roadside sensors. Therefore, the communication latency and end-to-end communication latency of the roadside sensors at each stage can be calculated, as well as the data transmission frequency of the roadside sensors. When the latency of any stage of the roadside algorithm exceeds expectations, the roadside equipment can be controlled to only send event information that has no latency requirements to the vehicle. When the end-to-end latency of the roadside algorithm exceeds expectations, an anomaly alarm can be issued, and the vehicle decides how to handle the data. Similarly, when the data transmission frequency of the roadside equipment is lower than expected, an anomaly alarm can also be issued, and the vehicle decides how to handle the data.

[0064] The roadside equipment in this application embodiment may also include traffic light equipment. The end-to-end communication latency of the traffic light equipment can be calculated using vehicle-side log data. When the latency exceeds expectations, the traffic light data can be considered partially reliable. For example, the service status of the light color data can be considered reliable, while the service status corresponding to the countdown data can be considered questionable. Furthermore, the communication frequency of the traffic light equipment can be further calculated. If the communication frequency of the traffic light equipment is lower than expected, an anomaly alarm for the traffic light equipment can be triggered.

[0065] In some embodiments of this application, determining the service status of the roadside device's perception data based on the roadside device's operating data and the autonomous vehicle's driving data includes: performing data mining on the roadside device's operating data and the autonomous vehicle's driving data using a preset data mining strategy to obtain data mining results, wherein the data mining results include at least one of the roadside device's perception error, the roadside device's service rate, and the roadside device's traffic light accuracy; and determining the service status of the roadside device's perception data based on the data mining results.

[0066] In addition to the methods described in the foregoing embodiments, which directly determine the service status of the roadside equipment's perception data based on the roadside equipment's operational data and based on data such as the autonomous vehicle's operational logs, the embodiments of this application can further conduct in-depth mining and analysis of the data reported by the roadside equipment and autonomous vehicles, specifically including the following analysis dimensions:

[0067] 1) Sensing error of roadside equipment

[0068] a) Roadside sensing position accuracy: The position accuracy of the roadside sensors is determined by comparing the position of the autonomous vehicle sensed by the roadside sensors with the position reported by the autonomous vehicle.

[0069] b) Roadside sensing speed accuracy: The speed accuracy of the roadside sensors is determined by comparing the speed of the autonomous vehicle sensed by the roadside sensors with the vehicle speed reported by the autonomous vehicle.

[0070] c) Accuracy of heading angle perceived by roadside sensors: The accuracy of the heading angle perceived by roadside sensors is determined by comparing the heading angle of the autonomous vehicle perceived by the roadside sensors with the heading angle of the autonomous vehicle reported by the autonomous vehicle.

[0071] If the accuracy of the roadside sensor position, the accuracy of the roadside sensor speed, and the accuracy of the roadside sensor heading angle are lower than the corresponding expected values, then the corresponding abnormal alarm will be issued.

[0072] 2) Service rate of roadside equipment: When an autonomous vehicle passes through the perception range of roadside equipment, the proportion of perception data and traffic light data provided by the roadside equipment. If the service rate of roadside equipment is lower than expected, an abnormal alarm will be issued.

[0073] 3) Traffic light accuracy of roadside equipment: The traffic light recognition results of roadside equipment are compared with the traffic light recognition results of autonomous vehicles and the traffic light data reported by the traffic light equipment to the cloud to calculate the traffic light recognition accuracy of roadside equipment.

[0074] 4) Service accuracy of autonomous vehicles themselves: When autonomous vehicles show abnormal indicators at multiple intersections, it indicates that there is a problem with the vehicle itself.

[0075] In some embodiments of this application, controlling the service range of the roadside device based on the service status of the roadside device's sensing data includes: marking the roadside device's sensing data and / or initiating anomaly alarms based on the service status of the roadside device's sensing data.

[0076] Based on the service status of the roadside equipment's sensing data determined in the aforementioned embodiments, this application embodiment can further adjust the service range of the roadside equipment. This adjustment can be achieved by marking the sensing data of the corresponding roadside equipment according to the determined service status, such as marking it as a trusted state, a questionable state, or an unavailable state. This facilitates the roadside equipment selectively sending data to the vehicle based on the status markings. Furthermore, when certain evaluation indicators are abnormal, an anomaly alarm can be initiated so that the roadside and vehicle ends can promptly detect anomalies and take appropriate measures.

[0077] In summary, the service control method for roadside equipment of this application has achieved at least the following technical effects:

[0078] 1) It can analyze and evaluate the service quality of roadside equipment in real time, promptly detect problematic data from roadside equipment, and adjust the service scope based on the problematic data. This controls the range and type of data sent by roadside equipment to autonomous vehicles, avoids sending abnormal data to the vehicle, and issues abnormal alarms, reducing the possibility of traffic accidents caused by abnormal data from roadside equipment to autonomous vehicles.

[0079] 2) The vehicle can determine whether to use the sensing data from the roadside equipment based on the alarm;

[0080] 3) Testing a single roadside device takes less time, can be monitored and tested 24 hours a day, consumes fewer resources (only one server is needed), and can react within one minute after a problem occurs in the roadside device. Service status identifiers for the sensing data of the roadside device can be set to control the transmission of problem data.

[0081] This application provides a service control device 200 for roadside equipment, such as... Figure 2 The diagram provided illustrates the structure of a service control device for a roadside facility according to an embodiment of this application. The device 200 includes at least:

[0082] The acquisition unit 210 is used to acquire the operating data of the roadside equipment and the driving data of the autonomous vehicle;

[0083] The determining unit 220 is used to determine the service status of the perception data of the roadside equipment based on the operation data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0084] Control unit 230 is used to control the service range of the roadside equipment based on the service status of the roadside equipment's sensing data.

[0085] In some embodiments of this application, the roadside equipment includes multiple roadside sensors, and the determining unit 220 is specifically used to: determine the online status of each roadside sensor based on the operating data of each roadside sensor; and determine the service status of the sensing data of each roadside sensor based on the online status of each roadside sensor and the type of each roadside sensor.

[0086] In some embodiments of this application, the roadside sensors include roadside cameras and roadside lidar. The determining unit 220 is specifically used to: if the online status of each roadside sensor is online, then determine that the service status of the sensing data of each roadside sensor is available; if the online status of any one or more roadside sensors is offline, then determine that the service status of the sensing data of the offline roadside sensors is unavailable, and determine that the service status of the sensing data of the online roadside sensors is unavailable or questionable based on the type of the offline roadside sensors.

[0087] In some embodiments of this application, the determining unit 220 is specifically used to: determine the data collection frequency of the roadside device based on the operating data of the roadside device; if the data collection frequency of the roadside device is lower than a first preset frequency threshold, then determine that the service status corresponding to the sensing data of the roadside device is a questionable state; if the data collection frequency of the roadside device is lower than a second preset frequency threshold, then determine that the service status corresponding to the sensing data of the roadside device is an unavailable state; otherwise, then determine that the service status corresponding to the sensing data of the roadside device is a reliable state; wherein, the first preset frequency threshold is greater than the second preset frequency threshold.

[0088] In some embodiments of this application, the driving data includes log data, and the determining unit 220 is specifically used to: determine the communication delay of the roadside device and / or the data transmission frequency of the roadside device based on the log data, wherein the communication delay includes the delay of each stage of the roadside algorithm and the end-to-end delay; and determine the service status of the perception data of the roadside device based on the operating data of the roadside device and the communication delay and / or the data transmission frequency of the roadside device.

[0089] In some embodiments of this application, the determining unit 220 is specifically used to: perform data mining on the operating data of the roadside equipment and the driving data of the autonomous vehicle using a preset data mining strategy to obtain data mining results, wherein the data mining results include at least one of the perception error of the roadside equipment, the service rate of the roadside equipment, and the traffic light accuracy of the roadside equipment; and determine the service status of the perception data of the roadside equipment based on the data mining results.

[0090] In some embodiments of this application, the control unit 230 is specifically used to: mark the sensing data of the roadside equipment and / or initiate abnormal alarms based on the service status of the sensing data of the roadside equipment.

[0091] It is understood that the service control device for the roadside equipment described above can implement each step of the service control method for the roadside equipment provided in the foregoing embodiments. The relevant explanations of the service control method for the roadside equipment are applicable to the service control device for the roadside equipment, and will not be repeated here.

[0092] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 3At the hardware level, the electronic device includes a processor, and optionally also an internal bus, network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0093] The processor, network interface, and memory can be interconnected via 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, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0094] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0095] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming the service control device for the roadside equipment at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0096] Acquire operational data from roadside equipment and driving data from autonomous vehicles;

[0097] The service status of the perception data of the roadside equipment is determined based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0098] The service range of the roadside equipment is controlled based on the service status of the sensing data of the roadside equipment.

[0099] The above is as stated in this application. Figure 1The method executed by the service control device of the roadside equipment 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. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The 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 gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0100] The electronic device can also perform Figure 1 The method for executing the service control device of the roadside equipment, and realizing the service control device of the roadside equipment in Figure 1 The functions of the embodiments shown are not described in detail here.

[0101] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method executed by the service control device of the roadside equipment in the illustrated embodiment is specifically used to perform:

[0102] Acquire operational data from roadside equipment and driving data from autonomous vehicles;

[0103] The service status of the perception data of the roadside equipment is determined based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status.

[0104] The service range of the roadside equipment is controlled based on the service status of the sensing data of the roadside equipment.

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0110] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0111] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0112] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0114] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A service control method of a roadside device, wherein, The method includes: Acquire operational data from roadside equipment and driving data from autonomous vehicles; The service status of the perception data of the roadside equipment is determined based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status. The service range of the roadside equipment is controlled based on the service status of the sensing data of the roadside equipment. The driving data includes log data, which includes timestamps of the roadside algorithm of the roadside sensors at each stage. Determining the service status of the roadside device's perception data based on the operating data of the roadside device and the driving data of the autonomous vehicle includes: The communication latency of the roadside equipment and / or the data transmission frequency of the roadside equipment are determined based on the log data, wherein the communication latency includes the latency of each stage of the roadside algorithm and the end-to-end latency; The service status of the sensing data of the roadside equipment is determined based on the operating data of the roadside equipment, the communication latency of the roadside equipment, and / or the data transmission frequency of the roadside equipment. The roadside equipment includes multiple roadside sensors, and determining the service status of the roadside equipment's perception data based on the operating data of the roadside equipment and the driving data of the autonomous vehicle includes: The online status of each roadside sensor is determined based on the operating data of each roadside sensor; The service status of the sensing data of each roadside sensor is determined based on the online status of each roadside sensor and the type of each roadside sensor. The roadside sensors include roadside cameras and roadside lidar. Determining the service status of the sensing data from each roadside sensor based on its online status and type includes: If all roadside sensors are online, then the service status of the sensing data of each roadside sensor is determined to be available. If any one or more roadside sensors are offline, the service status of the sensing data of the offline roadside sensors is determined to be unavailable, and the service status of the sensing data of the online roadside sensors is determined to be unavailable or questionable based on the type of the offline roadside sensors. The types of roadside sensors include roadside cameras and roadside lidar, as well as main sensors and sub-sensors.

2. The method of claim 1, wherein, Determining the service status of the roadside device's perception data based on the roadside device's operational data and the autonomous vehicle's driving data includes: The data acquisition frequency of the roadside equipment is determined based on its operating data. If the data collection frequency of the roadside equipment is lower than the first preset frequency threshold, the service status corresponding to the sensing data of the roadside equipment is determined to be questionable. If the data collection frequency of the roadside equipment is lower than the second preset frequency threshold, then the service status corresponding to the sensing data of the roadside equipment is determined to be unavailable. Otherwise, the perceived data of the roadside equipment is determined to be a trusted service state. Wherein, the first preset frequency threshold is greater than the second preset frequency threshold.

3. The method of claim 1, wherein, Determining the service status of the roadside device's perception data based on the roadside device's operational data and the autonomous vehicle's driving data includes: Data mining is performed on the operating data of the roadside equipment and the driving data of the autonomous vehicle using a preset data mining strategy to obtain data mining results. The data mining results include at least one of the following: the perception error of the roadside equipment, the service rate of the roadside equipment, and the traffic light accuracy of the roadside equipment. The service status of the sensing data of the roadside equipment is determined based on the data mining results.

4. The method of claim 1, wherein, The step of controlling the service range of the roadside equipment based on the service status of the roadside equipment's sensing data includes: The roadside equipment's sensing data is marked and / or anomaly alarms are initiated based on the service status of the sensing data.

5. A service control device of a roadside device, wherein, The device includes: The acquisition unit is used to acquire the operating data of roadside equipment and the driving data of autonomous vehicles; The determining unit is configured to determine the service status of the perception data of the roadside equipment based on the operating data of the roadside equipment and the driving data of the autonomous vehicle. The service status includes an available status and an unavailable status. The available status includes a trusted status and a questionable status. A control unit is used to control the service range of the roadside equipment based on the service status of the roadside equipment's sensing data. The driving data includes log data, which includes timestamps of the roadside algorithm from the roadside sensors at each stage. The determining unit is specifically used for: The communication latency of the roadside equipment and / or the data transmission frequency of the roadside equipment are determined based on the log data, wherein the communication latency includes the latency of each stage of the roadside algorithm and the end-to-end latency; The service status of the sensing data of the roadside equipment is determined based on the operating data of the roadside equipment, the communication latency of the roadside equipment, and / or the data transmission frequency of the roadside equipment. The roadside equipment includes multiple roadside sensors, and the determining unit is specifically used for: The online status of each roadside sensor is determined based on the operating data of each roadside sensor; The service status of the sensing data of each roadside sensor is determined based on the online status of each roadside sensor and the type of each roadside sensor. The roadside sensors include roadside cameras and roadside lidar, and the determining unit is specifically used for: If all roadside sensors are online, then the service status of the sensing data of each roadside sensor is determined to be available. If any one or more roadside sensors are offline, the service status of the sensing data of the offline roadside sensors is determined to be unavailable, and the service status of the sensing data of the online roadside sensors is determined to be unavailable or questionable based on the type of the offline roadside sensors. The types of roadside sensors include roadside cameras and roadside lidar, as well as main sensors and sub-sensors.

6. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 4.

7. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 4.

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