Vehicle safety detection method, vehicle-mounted unit and storage medium

Abstract

The present application provides a vehicle safety detection method, a vehicle-mounted unit and a storage medium. The safety detection method is applied to the vehicle-mounted unit and comprises: performing detection on a data acquisition result of a positioning data acquisition module of a vehicle to obtain data detection information; performing algorithm execution validity detection on an algorithm execution module of the vehicle to obtain algorithm detection information, the algorithm execution module being configured to use a target algorithm to process data input by the positioning data acquisition module, to determine the location of the vehicle; and analyzing the data detection information and the algorithm detection information to determine the safety level of the vehicle.

Description

Vehicle safety detection method, vehicle-mounted unit and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to Chinese patent application 202311763759.0 filed with the State Intellectual Property Office of China on December 20, 2023, and the disclosure of this Chinese patent application is incorporated herein by reference in its entirety. Technical Field

[0003] The present application relates to the field of vehicle detection technology, and in particular to a vehicle safety detection method, a vehicle-mounted unit, and a storage medium. Background Art

[0004] With the rapid development of intelligent vehicle networks and vehicles, in order to ensure the normal driving of the vehicle, a specific chip is usually used inside the vehicle to locate the vehicle to determine the real-time location of the vehicle. Summary of the Invention

[0005] The present application provides a vehicle safety detection method, a vehicle-mounted unit, and a storage medium.

[0006] An embodiment of the present application provides a vehicle safety detection method, which is applied to an on-board unit and includes: detecting data acquisition results of a vehicle's positioning data acquisition module to obtain data detection information; detecting the effectiveness of algorithm execution of the vehicle's algorithm execution module to obtain algorithm detection information, wherein the algorithm execution module is configured to use a target algorithm to process data input by the positioning data acquisition module to determine the vehicle's location; and analyzing the data detection information and the algorithm detection information to determine the vehicle's safety level.

[0007] An embodiment of the present application provides a vehicle-mounted unit, comprising: one or more processors; a memory on which one or more programs are stored. When the one or more programs are executed by one or more processors, the one or more processors implement any vehicle safety detection method in the embodiment of the present application.

[0008] An embodiment of the present application provides a readable storage medium, which stores a computer program. When the computer program is executed by a processor, any one of the vehicle safety detection methods in the embodiments of the present application is implemented.

[0009] With respect to the above embodiments and other aspects of the present application and their implementation, further description is provided in the accompanying drawings, detailed description and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG1 shows a structural diagram of a vehicle safety detection system provided by a related technical solution.

[0011] FIG2 shows a flow chart of a vehicle safety detection method provided in an embodiment of the present application.

[0012] FIG3 shows a block diagram of the composition of the vehicle-mounted unit provided in an embodiment of the present application.

[0013] FIG4 shows a block diagram of a vehicle safety detection system according to an embodiment of the present application.

[0014] FIG5 shows a block diagram of a vehicle safety detection system according to an embodiment of the present application.

[0015] FIG6 is a flow chart showing a working method of a vehicle safety detection system according to an embodiment of the present application.

[0016] FIG7 shows a block diagram of the composition of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] To help those skilled in the art better understand the technical solutions of the present application, the following description of exemplary embodiments of the present application is provided in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding. These details should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0018] In the absence of conflict, the various embodiments of the present application and the various features therein may be combined with each other.

[0019] As used herein, the term "and / or" includes any and all combinations of one or more of the associated enumerated items. The terms used herein are used only to describe specific embodiments and are not intended to limit this application. As used herein, the singular forms "a" and "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of..." are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0020] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined as such herein.

[0021] The Internet of Vehicles (IoV) technology uses moving vehicles as information sensors and leverages next-generation information and communication technologies to connect vehicles to other things (i.e., vehicles, people, roadside equipment, platform servers, etc.). IoV can provide users with intelligent and efficient transportation services, thereby improving traffic efficiency.

[0022] In the fields of autonomous driving and assisted driving, vehicle networking technology has been widely used. To ensure the normal operation of the vehicle, a specific chip is usually used inside the vehicle to locate the vehicle and determine its real-time location.

[0023] However, the commonly used positioning chips that meet functional safety requirements perform specific processing on the original positioning data. The vehicle's control system needs to be highly coupled with the positioning chip to enable the vehicle's control system to obtain accurate positioning information and the vehicle's functional safety level. This is not conducive to updating the vehicle's control system. In addition, the hardware cost of the positioning chip is relatively high, which increases the production cost of the vehicle.

[0024] Figure 1 shows a structural diagram of a vehicle safety detection system provided by a related art solution. As shown in Figure 1 , the safety detection system includes a data splitter 101, an ST Microelectronics application interface chip (Application IC) 102, an ST Microelectronics radio frequency interface chip (RF IC) 103, a crystal oscillator 104, and a memory 105.

[0025] The data separator 101 is used to parse the data received from other network layers (such as Layer 1 (L1), Layer 2 (L2) and Layer 5 (L5)), obtain data that meets STMicroelectronics' usage requirements, and send the parsed data to STMicroelectronics' App IC 102 and STMicroelectronics' RF IC 103.

[0026] The crystal oscillator 104 can be a temperature-compensated crystal oscillator (TCXO). TCXOs include digitally compensated crystal oscillators and microcomputer-compensated crystal oscillators. A TCXO maintains the output frequency of the crystal oscillator within a preset accuracy range (e.g., 10-7 or 10-6) within a preset temperature range through a preset compensation method (e.g., capacitor compensation, thermistor network compensation, etc.). TCXOs have advantages such as good power-on characteristics, low power consumption, and high frequency-temperature stability. Memory 105 stores program code and / or data.

[0027] STMicroelectronics' App IC 102 has built-in Automotive Safety Integrity Level (ASIL)-compliant hardware components, implementing ASIL B-level Global Navigation Satellite System (GNSS)-based positioning services using a pure hardware approach. However, this hardware component requires a high degree of coupling with other components and software designs of the safety detection system, which is not conducive to updating the safety detection system (for example, the positioning algorithm cannot be updated in a timely manner, resulting in unsatisfactory positioning accuracy in different application scenarios). Furthermore, the hardware cost of STMicroelectronics' App IC 102 is relatively high, increasing the production cost of the vehicle.

[0028] The present application provides a vehicle safety detection method, a vehicle-mounted unit, and a storage medium to solve the above-mentioned problems.

[0029] Figure 2 shows a flow chart of a vehicle safety detection method provided by an embodiment of the present application. The method can be applied to a vehicle-mounted unit, where the vehicle where the vehicle-mounted unit is located includes a positioning data acquisition module and an algorithm execution module.

[0030] As shown in FIG2 , the vehicle safety detection method in the embodiment of the present application includes but is not limited to the following steps.

[0031] Step S201 : detecting the data acquisition result of the vehicle positioning data acquisition module to obtain data detection information.

[0032] The positioning data acquisition module is a module in the vehicle used to acquire positioning data, for example, acquiring positioning data sent by a Global Positioning System (GPS) antenna and / or positioning data fed back by a GNSS antenna.

[0033] When it is detected that the positioning data acquisition module can acquire real-time positioning data from the GPS antenna and / or GNSS antenna, it is determined that the data detection information indicates that the real-time positioning data is successfully acquired; otherwise, it is determined that the data detection information indicates that the real-time positioning data acquisition fails.

[0034] Step S202 : performing a detection on the algorithm execution module of the vehicle to check the effectiveness of the algorithm execution and obtaining algorithm detection information.

[0035] The algorithm execution module is used to process the first data using the target algorithm to determine the location of the vehicle; the first data is the data obtained by the positioning data acquisition module and output to the algorithm execution module for processing.

[0036] When it is detected that the algorithm execution module is able to use the target algorithm to accurately process the first data and obtain the data processing result, it is determined that the algorithm detection information indicates that the execution of the target algorithm is valid; otherwise, it is determined that the algorithm detection information indicates that the execution of the target algorithm is invalid, that is, there is an error in the execution process of the target algorithm and it is unable to accurately process the data input by the positioning data acquisition module.

[0037] It should be noted that the target algorithm includes at least one of the following: Real Time Kinematic (RTK) algorithm, Precise Point Position (PPP) algorithm, fusion positioning algorithm and Dead Reckoning (DR) algorithm.

[0038] A fused positioning algorithm combines position, velocity, and time (PVT) to determine the position of a positioning antenna (such as a GPS or GNSS antenna) and its receiver. This fused positioning algorithm calculates the position of the positioning antenna and receiver based on the timestamp information corresponding to positioning signals from multiple satellites, improving positioning accuracy.

[0039] Step S203: Analyze the data detection information and the algorithm detection information to determine the safety level of the vehicle.

[0040] The safety level represents the degree to which anomalies encountered during the vehicle positioning process impact the vehicle's functional safety. For example, the safety level can be set to include at least three levels: low, medium, and high. The higher the safety level, the greater the risk of harm caused by anomalies during the vehicle positioning process. Based on different safety levels, the vehicle's positioning accuracy can be accurately measured, allowing for timely adjustments to the vehicle's positioning method.

[0041] In this embodiment, by detecting the data acquisition results of the vehicle's positioning data acquisition module, it can be determined based on the acquired data detection information whether the positioning data acquisition module has accurately and timely acquired the positioning data; the vehicle's algorithm execution module is tested for algorithm execution effectiveness, and based on the acquired algorithm detection information, it is determined whether the algorithm execution module has execution anomalies in the process of using the target algorithm to process the data input by the positioning data acquisition module; then, the data detection information and the algorithm detection information are analyzed to determine whether there are anomalies in the vehicle's positioning process, so as to determine the vehicle's safety level. There is no need to use a specific positioning chip that meets functional safety requirements to locate the vehicle, which reduces the degree of coupling between the internal modules of the vehicle's control system, facilitates timely updating of the vehicle's control system, improves the stability of the vehicle, and reduces the production cost of the vehicle.

[0042] In some exemplary embodiments, the detection of the data acquisition result of the vehicle's positioning data acquisition module in step S201 to obtain data detection information can be implemented as follows: when the positioning data acquisition module acquires real-time positioning data at the current moment, data detection information including a data acquisition success identifier is generated, and the positioning data acquisition module is controlled to send the real-time positioning data to the algorithm execution module.

[0043] The data acquisition success flag indicates that the positioning data acquisition module has successfully acquired the current real-time positioning data. Real-time positioning data is obtained by parsing satellite data received by the positioning antenna. It reflects the current satellite positioning data, allowing the vehicle to be accurately located based on this real-time positioning data.

[0044] When the positioning data acquisition module successfully obtains the real-time positioning data at the current moment, the on-board unit will control the positioning data acquisition module to send the real-time positioning data to the algorithm execution module so that the algorithm execution module can use the target algorithm therein to process the real-time positioning data and speed up the positioning efficiency of the vehicle.

[0045] In some exemplary embodiments, the data acquisition results of the vehicle's positioning data acquisition module are detected in step S201 to obtain data detection information, which can be implemented in the following manner: when the positioning data acquisition module fails to obtain the real-time positioning data at the current moment, data detection information including a data acquisition failure identifier is generated, and the vehicle's backup module is controlled to send the positioning backup data of the previous moment to the algorithm execution module.

[0046] The backup module is used to back up the positioning data and generate positioning backup data. The data acquisition failure flag is used to indicate that the positioning data acquisition module fails to acquire the real-time positioning data at the current moment.

[0047] In some embodiments, the positioning backup data is data obtained by the backup module backing up the real-time positioning data at the previous moment. The positioning backup data can reflect the positioning information at the previous moment, facilitating subsequent positioning analysis.

[0048] For example, the backup positioning data includes at least one of the following: Global Navigation Satellite System (GNSS) data, RTK data, and inertial navigation data.

[0049] Inertial navigation data is obtained using an inertial measurement unit (IMU), which is installed in the vehicle. During actual measurements, the vehicle's position can be directly determined using data collected by the IMU's accelerometers and gyroscopes, enabling precise measurement of the vehicle's position.

[0050] When it is detected that the positioning data acquisition module fails to obtain the real-time positioning data at the current moment, the on-board unit needs to provide the algorithm execution module with positioning backup data through the backup module, so that the algorithm execution module can use the target algorithm to analyze and process the positioning backup data to determine the vehicle's location.

[0051] It should be noted that since the backup positioning data is the one backed up from the moment before the current moment, the time difference is small, and the distance the vehicle moves within this time difference range will not be very far. Therefore, after the algorithm execution module uses the target algorithm to analyze and process the backup positioning data, the obtained vehicle position will not have a large deviation and will still meet the vehicle's positioning requirements. After the positioning data acquisition module successfully obtains the current moment's real-time positioning data, it further updates the vehicle's positioning position, thereby ensuring that the vehicle's positioning requirements are continuously and stably output, improving the vehicle's positioning stability.

[0052] In some exemplary embodiments, the detection of the effectiveness of the algorithm execution of the vehicle's algorithm execution module and the acquisition of algorithm detection information in step S202 can be implemented as follows: when the target algorithm in the algorithm execution module operates normally, the first data processing result sent by the algorithm execution module is obtained, and algorithm detection information including an indication that the algorithm is operating normally is generated.

[0053] The first data processing result can reflect the result obtained by the algorithm execution module using the target algorithm to process the data input by the positioning data acquisition module.

[0054] For example, the first data processing result includes: a first real-time data processing result obtained by processing the real-time positioning data using the target algorithm, and a first backup data processing result obtained by processing the positioning backup data using the target algorithm.

[0055] The first real-time data processing result and the first backup data processing result can reflect the execution effect of the target algorithm, that is, whether the target algorithm can accurately process the real-time positioning data (or, positioning backup data), thereby evaluating the execution effectiveness of the target algorithm. When it is determined based on the first data processing result that the target algorithm is operating normally, algorithm detection information including an indicator of normal algorithm operation is generated, so that the algorithm detection information can reflect the detection result of the execution effectiveness of the target algorithm and ensure that the target algorithm in the algorithm execution module can be executed normally and orderly.

[0056] In some exemplary embodiments, the detection of the effectiveness of the algorithm execution of the vehicle's algorithm execution module in step S202 to obtain algorithm detection information can be implemented as follows: when the target algorithm in the algorithm execution module operates abnormally, the backup module is controlled to use the backup algorithm to process the positioning data, obtain a second data processing result, and generate algorithm detection information including an algorithm operation abnormality identifier.

[0057] The backup module is used to back up the target algorithm and generate a backup algorithm, which includes at least one of the following: a real-time dynamic positioning algorithm, a fusion positioning algorithm, and a dead reckoning algorithm.

[0058] It should be noted that the target algorithm in the algorithm execution module and the backup algorithm in the backup module are stored separately. For example, the backup algorithm can be stored in the Trusted Execution Environment (TEE) area of ​​a processing core, which is a secure environment area.

[0059] Using different hardware (e.g., algorithm execution module and backup module) to store the target algorithm and backup algorithm respectively, which serve as the primary and backup for each other, can enable each algorithm to run in an independent and closed inter-core environment area, reduce the possibility of data loss due to abnormal power outages, and ensure the stability and reliability of the measurement process and results.

[0060] In some exemplary embodiments, when the target algorithm in the algorithm execution module operates abnormally and / or the positioning data acquisition module fails to obtain the real-time positioning data at the current moment, the data detection information and the algorithm detection information are analyzed in step S203 to determine the safety level of the vehicle. This can be implemented in the following manner: the data detection information and the algorithm detection information are analyzed to determine that the algorithm detection information includes an algorithm operation abnormality identifier and / or the data detection information includes a data acquisition failure identifier; based on the data acquisition failure identifier and / or the algorithm operation abnormality identifier, the first abnormality information of the algorithm execution module and / or the second abnormality information of the data acquisition module are obtained; based on the first abnormality information and / or the second abnormality information, the vehicle's positioning abnormality is measured to determine the vehicle's positioning abnormality and its corresponding safety level.

[0061] Vehicle positioning anomalies include single-point positioning anomalies and / or multi-point positioning anomalies. The metric used to measure single-point positioning anomalies is the single-point fault metric, while the metric used to measure multi-point positioning anomalies is the latent-fault metric.

[0062] When it is detected that the algorithm detection information includes an algorithm operation abnormality identifier, it is determined that there is an operation abnormality in the algorithm execution module. The positioning abnormality existing in the vehicle positioning process and the safety level corresponding to the positioning abnormality can be determined through the first abnormality information (for example, a program error (bug), alarm information, etc. that occurs during the operation of the algorithm execution module).

[0063] When it is detected that the data detection information includes a data acquisition failure identifier, it is determined that there is an abnormality in the process of the positioning data acquisition module acquiring the positioning data. The positioning abnormality in the vehicle's positioning process and the safety level corresponding to the positioning abnormality can be determined through the second abnormality information (for example, the reason why the positioning data acquisition module failed to acquire the positioning data (such as an abnormality in the positioning antenna), or the time information corresponding to the positioning data acquired by the positioning data acquisition module is wrong, etc.).

[0064] When it is detected at the same time that the algorithm detection information includes an algorithm operation abnormality flag, and the data detection information includes a data acquisition failure flag, it can be determined that there are abnormalities in both the algorithm execution module and the positioning data acquisition module. The first abnormality information and the second abnormality information can be combined for analysis to determine the positioning abnormality that exists in the vehicle during the positioning process and the safety level corresponding to the positioning abnormality.

[0065] By using the above-mentioned multiple different measurement methods, it is possible to accurately analyze whether there may be single-point positioning anomalies and / or multi-point positioning anomalies in the vehicle's positioning process. In other words, whether there is an anomaly in one module or multiple modules in the vehicle, so as to accurately measure the anomaly category of the vehicle in the positioning process. Based on different anomaly categories, corresponding safety levels are used to reflect the degree of damage caused by the anomaly to the vehicle, so as to facilitate subsequent maintenance of the vehicle according to different safety levels to ensure the safe operation of the vehicle.

[0066] In some exemplary embodiments, the safety level corresponding to a vehicle positioning anomaly is determined based on at least one of the degree of damage caused to the vehicle by the positioning anomaly, the likelihood of the positioning anomaly occurring, and the controllability of the positioning anomaly. The degree of damage caused to the vehicle by the positioning anomaly includes at least one of the following: no damage, mild or moderate damage, severe damage, and extremely severe damage; the likelihood of the positioning anomaly occurring includes at least one of the following: low likelihood, medium likelihood, and high likelihood; and the controllability of the positioning anomaly includes at least one of the following: vehicle controllable, controllable by simple operation of vehicle components, controllable by normal operation of vehicle components, and vehicle uncontrollable.

[0067] In some embodiments, different percentages can be used to represent different levels. For example, when the positioning anomaly does not cause any damage to the vehicle, the degree of damage caused by the positioning anomaly to the vehicle can be determined as no damage; when the positioning anomaly causes 10-30% damage to the vehicle, the degree of damage caused by the positioning anomaly to the vehicle can be determined as mild damage; when the positioning anomaly causes 31-60% damage to the vehicle, the degree of damage caused by the positioning anomaly to the vehicle can be determined as moderate damage; when the positioning anomaly causes 61-90% damage to the vehicle, the degree of damage caused by the positioning anomaly to the vehicle can be determined as severe damage; when the positioning anomaly causes more than 90% damage to the vehicle, the degree of damage caused by the positioning anomaly to the vehicle can be determined as extremely severe damage.

[0068] Similarly, the probability of occurrence of positioning anomalies and the controllability of positioning anomalies can also be determined to different levels based on different percentages.

[0069] Using these multiple dimensions to categorize vehicle positioning anomalies into different safety levels allows the corresponding safety level to more accurately reflect the corresponding risk. The higher the safety level, the greater the risk associated with the anomaly during the vehicle positioning process.

[0070] In some exemplary embodiments, the vehicle's application module is used to use highly reliable positioning results (i.e., the vehicle's real-time location information), and / or provide the vehicle's real-time location information to other applications that need to use positioning information, thereby better assisting vehicle operation.

[0071] After analyzing the data detection information and the algorithm detection information in step S203 to determine the safety level of the vehicle, the method further includes: screening the first real-time data processing result, the second real-time data processing result, the first backup data processing result, and the second backup data processing result to obtain a target data processing result with the highest data reliability; and sending the target data processing result to the application module.

[0072] The first data processing result sent by the algorithm execution module includes a first real-time data processing result obtained by processing the real-time positioning data using the target algorithm, and a first backup data processing result obtained by processing the positioning backup data using the target algorithm. The second data processing result includes a second real-time data processing result obtained by processing the real-time positioning data using the backup algorithm, and a second backup data processing result obtained by processing the positioning backup data using the backup algorithm. The positioning backup data is data obtained by backing up the real-time positioning data at the previous moment.

[0073] In some embodiments, the reliability of real-time positioning data can be set higher than the reliability of backup positioning data. Accordingly, the reliability of the first real-time data processing result is the highest, while the reliability of the second real-time data processing result is the second lowest. The backup algorithm is a backup of the target algorithm. Therefore, the reliability of the first backup data processing result can be set between the reliability of the second real-time data processing result and the reliability of the second backup data processing result. The second backup data processing result has the lowest reliability.

[0074] By screening the above four different data processing results to obtain the target data processing result with the highest data reliability, the target data processing result can have the highest reliability to ensure that the data sent to the application module is the most reliable, so that the application module can use the highly reliable positioning result (i.e., the real-time location information of the vehicle).

[0075] In some exemplary embodiments, after analyzing the data detection information and the algorithm detection information in step S203 to determine the safety level of the vehicle, the method further includes: determining an alarm level based on the corresponding safety level of the vehicle; and generating alarm information corresponding to the alarm level.

[0076] Each safety level corresponds to an alarm level, so that different alarm levels can reflect different safety levels. The alarm information includes at least one of the following information: the alarm level, the abnormal information of the vehicle corresponding to the alarm level, and the module identifier that generated the abnormal information.

[0077] In some embodiments, the warning information may alert the driver of the vehicle to the presence of an abnormality in the vehicle using at least one of the following methods: sound, data, image, and text. For example, the onboard unit may send the warning information to a display module of the vehicle, so that the display module of the vehicle can display the warning information based on images and / or text.

[0078] When the display module receives an alarm message, it parses the alarm message and displays the parsed alarm message to the driver of the vehicle, so that the driver of the vehicle can promptly know the abnormality of the vehicle and make timely adjustments to the abnormal module in the vehicle to ensure the safe and stable operation of the vehicle and improve the safety of the vehicle.

[0079] Figure 3 shows a block diagram of the vehicle-mounted unit provided in an embodiment of the present application. As shown in Figure 3, the vehicle-mounted unit 300 includes but is not limited to the following modules.

[0080] The data detection module 301 is configured to detect the data acquisition result of the vehicle positioning data acquisition module to obtain data detection information.

[0081] The algorithm detection module 302 is configured to detect the effectiveness of the algorithm execution of the vehicle's algorithm execution module and obtain algorithm detection information.

[0082] The algorithm execution module is used to process the data input by the positioning data acquisition module using the target algorithm to determine the vehicle's location.

[0083] The analysis module 303 is configured to analyze the data detection information and the algorithm detection information to determine the safety level of the vehicle.

[0084] It should be noted that the on-board unit 300 in this embodiment can implement any vehicle safety detection method in the embodiments of the present application.

[0085] For detailed descriptions of the data detection module 301 , the algorithm detection module 302 and the analysis module 303 , please refer to the above description with reference to FIG. 2 , which will not be repeated here.

[0086] According to the vehicle-mounted unit of the embodiment of the present application, the data acquisition result of the vehicle's positioning data acquisition module is detected by the data detection module, and it can be determined whether the positioning data acquisition module has accurately and timely acquired the positioning data based on the acquired data detection information; the algorithm detection module is used to detect the effectiveness of the algorithm execution of the vehicle's algorithm execution module, and based on the acquired algorithm detection information, it is determined whether the algorithm execution module has an execution anomaly in the process of using the target algorithm to process the data input by the positioning data acquisition module; then, the analysis module is used to analyze the data detection information and the algorithm detection information, and it can be determined whether there is an anomaly in the vehicle's positioning process, so as to determine the vehicle's safety level. There is no need to use a specific positioning chip that meets functional safety requirements to locate the vehicle, which reduces the degree of coupling between the internal modules of the vehicle's control system, facilitates timely updating of the vehicle's control system, improves the stability of the vehicle, and reduces the production cost of the vehicle.

[0087] Figure 4 shows a block diagram of a vehicle safety detection system provided by an embodiment of the present application. The safety detection system can be applied to autonomous vehicles in autonomous driving scenarios.

[0088] As shown in FIG4 , the vehicle safety detection system includes but is not limited to the following equipment: a vehicle 410 and an on-board unit (OBU) 420 .

[0089] The vehicle 410 includes a positioning data acquisition module 411 and an algorithm execution module 412. The vehicle-mounted unit 420 includes a data detection module 421, an algorithm detection module 422 and an analysis module 423.

[0090] The positioning data acquisition module 411 is used to acquire positioning data between the vehicle 410 and other devices (for example, a positioning antenna (not shown in the figure) in the vehicle 410 and a satellite communication device (not shown in the figure)).

[0091] The algorithm execution module 412 is used to process the data input by the positioning data acquisition module 411 to determine the location of the vehicle.

[0092] The data detection module 421 is used to detect the data acquisition result of the positioning data acquisition module 411 to obtain data detection information.

[0093] The algorithm detection module 422 is used to detect the effectiveness of the algorithm execution of the algorithm execution module 412 and obtain algorithm detection information.

[0094] The analysis module 423 is used to analyze the data detection information and the algorithm detection information to determine the safety level of the vehicle 410 .

[0095] For detailed descriptions of the algorithm execution module 412 , the data detection module 421 , the data detection module 421 , the algorithm detection module 4222 and the analysis module 423 , please refer to the description above with reference to FIG. 2 , which will not be repeated here.

[0096] In this embodiment, the data acquisition result of the positioning data acquisition module is detected by the data detection module, and it can be determined whether the positioning data acquisition module has accurately and timely acquired the positioning data based on the acquired data detection information; the algorithm detection module is used to detect the effectiveness of the algorithm execution of the algorithm execution module, and based on the acquired algorithm detection information, it is determined whether there is an execution anomaly in the process of the algorithm execution module using the target algorithm to process the data input by the positioning data acquisition module; then, the data detection information and the algorithm detection information are analyzed by the analysis module, and it can be determined whether there is an anomaly in the vehicle positioning process, so as to determine the safety level of the vehicle. There is no need to use a specific positioning chip that meets the functional safety requirements to locate the vehicle, which reduces the degree of coupling between the internal modules of the vehicle's control system, facilitates timely updating of the vehicle's control system, improves the stability of the vehicle, and reduces the production cost of the vehicle.

[0097] Figure 5 shows a block diagram of a vehicle safety detection system according to an embodiment of the present application. As shown in Figure 5 , the safety detection system includes, but is not limited to, the following devices: a standalone GNSS chip 510 , a system-on-chip (SoC) 520 , and a 5G modem 530 .

[0098] The independent GNSS chip 510 is connected to the system-on-chip 520 via a serial peripheral interface (SPI) and provides pulse per second (PPS) to the system-on-chip 520. The independent GNSS chip 510 also communicates with the 5G modem 530 based on a universal asynchronous receiver / transmitter (UART) and provides PPS to the 5G modem 530.

[0099] The independent GNSS chip 510 is used to perform operation maintenance (OM) control on the system-on-chip 520 and the 5G modem 530 .

[0100] The system-on-chip 520 and the 5G modem 530 can be communicated with each other using an Ethernet (Controller Area Network, CAN) connection method or a Universal Serial Bus (USB) connection method.

[0101] The system-level chip 520 supports multi-channel CAN and multi-channel vehicle CAN.

[0102] The SoC 520 includes a GNSS algorithm component 521 (corresponding to the algorithm execution module described above), a GNSS data component 522 (corresponding to the positioning data acquisition module described above), and a functional safety component 523. Functional safety component 523 includes an arbitration module 5231 (corresponding to the analysis module described above), an algorithm monitoring module 5232 (corresponding to the algorithm detection module described above), a failure handling module 5233, a data monitoring module 5234 (corresponding to the data detection module described above), and a data / algorithm redundancy module 5235 (corresponding to the backup module described above).

[0103] The GNSS algorithm component 521 is in communication with the algorithm monitoring module 5232, and the GNSS data component 522 is in communication with the data monitoring module 5234. The failure handling module 5233 is in communication with both the algorithm monitoring module 5232 and the data monitoring module 5234, respectively. The data / algorithm redundancy module 5235 is in communication with both the algorithm monitoring module 5232, the data monitoring module 5234, and the arbitration module 5231, respectively. The arbitration module 5231 is in communication with the algorithm monitoring module 5232, the failure handling module 5233, the data monitoring module 5234, and the data / algorithm redundancy module 5235, respectively.

[0104] The algorithm monitoring module 5232 is used to detect whether the GNSS algorithm component 521 can operate normally through the communication interface between the GNSS algorithm component 521, so as to obtain algorithm detection information (for example, the algorithm detection information includes: whether the GNSS algorithm component 521 uses the target algorithm to process the data input by the GNSS data component 522, and whether there is any abnormal information during the processing, etc.).

[0105] The algorithm monitoring module 5232 detects the metric range corresponding to the target algorithm to determine the algorithm detection information.

[0106] When the GNSS algorithm component 521 detects that the data processing result obtained by processing the data input by the GNSS data component 522 using the target algorithm exceeds a preset range, the algorithm monitoring module 5232 determines that the target algorithm is operating abnormally. In this case, the algorithm monitoring module 5232 generates algorithm detection information including an algorithm operation abnormality indicator. This algorithm detection information including the algorithm operation abnormality indicator is then sent to the arbitration module 5231, which instructs the arbitration module 5231 to control the data / algorithm redundancy module 5235 to process the data input by the GNSS data component 522 using a backup algorithm to obtain a second data processing result.

[0107] When it is detected that the data processing result obtained by the GNSS algorithm component 521 using the target algorithm to process the data input by the GNSS data component 522 does not exceed the preset range, the algorithm monitoring module 5232 determines that the target algorithm is operating normally and generates algorithm detection information including an indicator that the algorithm is operating normally.

[0108] The data monitoring module 5234 is used to detect the data acquisition results of the GNSS data component 522 through the communication interface between the data monitoring module 5234 and obtain data detection information (such as whether the GNSS data component 522 can successfully obtain real-time positioning data).

[0109] When the data monitoring module 5234 detects that the GNSS data component 522 has successfully acquired the real-time positioning data at the current moment, the data monitoring module 5234 generates data detection information including a data acquisition success indicator. When the data monitoring module 5234 detects that the GNSS data component 522 has failed to acquire the real-time positioning data at the current moment, the data monitoring module 5234 generates data detection information including a data acquisition failure indicator.

[0110] The failure processing module 5233 is used to collect the abnormality reports fed back by the algorithm monitoring module 5232 and the data monitoring module 5234, and identify and classify the abnormalities according to the functional safety level.

[0111] For example, if the algorithm monitoring module 5232 detects that the target algorithm in the GNSS algorithm component 521 is operating abnormally, the failure processing module 5233 obtains the algorithm detection information including the algorithm operation abnormality indicator generated by the algorithm monitoring module 5232. If the data monitoring module 5234 detects that the GNSS data component 522 has failed to obtain real-time positioning data at the current moment, the failure processing module 5233 obtains the data detection information including the data acquisition failure indicator generated by the algorithm monitoring module 5232.

[0112] The data / algorithm redundancy module 5235 is used to back up the positioning data at a preset time to obtain positioning backup data; and to back up the target algorithm to obtain a backup algorithm.

[0113] The backup algorithm includes at least one of the following: RTK algorithm, fusion positioning algorithm and DR algorithm; the backup positioning data includes at least one of the following: GNSS data, RTK data and inertial navigation data.

[0114] When the failure processing module 5233 feeds back to the arbitration module 5231 that there is a data anomaly and / or an algorithm anomaly, the arbitration module 5231 may control the data / algorithm redundancy module 5235 to provide corresponding positioning backup data and / or backup algorithm.

[0115] In some embodiments, the data / algorithm redundancy module 5235 may use the real-time operating system (RTOS) in the microcontroller unit (MCU) core in the system-level chip 520 to perform access operations on the positioning backup data and / or backup algorithm, which can reduce the possibility of interruption by abnormal scheduling; then, the retrieved positioning backup data and / or backup algorithm are transmitted to the upper-level application module through the inter-core hardware channel between the application chip (Application Processor, AP) core and the MCU core to ensure the real-time performance of data scheduling (e.g., controlling the data access time to be 0 to a preset time (e.g., 20 milliseconds)).

[0116] Positioning backup data and / or backup algorithms can be stored in the TEE area of ​​the AP core. Because the TEE area is a secure environment area that runs on isolated hardware (such as the AP core), that is, it runs in an independent and closed inter-core environment area, it can reduce the possibility of data loss due to abnormal power outages and ensure the stability and reliability of the measurement process and results.

[0117] The arbitration module 5231 is used to analyze the data detection information and the algorithm detection information to determine the safety level of the vehicle. The safety level is used to represent the impact level of abnormalities generated during the positioning process of the vehicle on the functional safety of the vehicle.

[0118] Abnormalities during vehicle positioning include single-point positioning abnormalities and / or multi-point positioning abnormalities. The metric used to measure single-point positioning abnormalities is the single-point fault metric, and the metric used to measure multi-point positioning abnormalities is the latent-fault metric. Arbitration module 5231 can determine the corresponding level of the single-point fault metric and the latent-fault metric based on different safety level requirements.

[0119] In some embodiments, the safety level of the vehicle is a level determined based on at least one of the degree of damage caused to the vehicle by the positioning anomaly, the likelihood of occurrence of the positioning anomaly, and the controllability of the positioning anomaly.

[0120] The degree of damage caused to the vehicle by the positioning abnormality includes at least one of the following: no damage, mild or moderate damage, severe damage and extremely severe damage; the possibility of the occurrence of the positioning abnormality includes at least one of the following: low possibility, medium possibility and high possibility; the controllability of the positioning abnormality includes at least one of the following: the vehicle is controllable, the vehicle components are controllable by simple operation, the vehicle components are controllable by normal operation and the vehicle is uncontrollable.

[0121] For example, the degree of damage caused by positioning anomalies to a vehicle can be divided into four levels as shown in Table 1.

[0122] Table 1 Level of damage caused by positioning abnormality to vehicles

[0123] The probability of occurrence of positioning anomalies can be divided into five levels as shown in Table 2.

[0124] Table 2 Probability level of positioning anomaly

[0125] The degree of damage caused to the vehicle by positioning anomalies can be divided into four levels as shown in Table 3.

[0126] Table 3. Damage level of the vehicle caused by positioning abnormality

[0127] By combining the classification methods in the above three tables, we can obtain the safety level information representing the vehicle as shown in Table 4.

[0128] Table 4 Vehicle safety level information

[0129] QM indicates whether the vehicle's positioning safety meets normal positioning requirements. A corresponds to the lowest safety level, with A, B, C, and D corresponding to increasing safety levels, and D corresponding to the highest safety level. The higher the safety level, the greater the risk of harm caused by anomalies during the vehicle positioning process.

[0130] It should be noted that the GNSS algorithm component 521 uses the target algorithm to process the real-time positioning data input by the GNSS data component 522 to obtain a first real-time data processing result; the GNSS algorithm component 521 uses the target algorithm to process the positioning backup data input by the data / algorithm redundancy module 5235 to obtain a first backup data processing result.

[0131] By using the backup algorithm in the data / algorithm redundancy module 5235 to process the real-time positioning data input by the GNSS data component 522, a second real-time data processing result can be obtained, and, by using the backup algorithm in the data / algorithm redundancy module 5235 to process the positioning backup data therein, a second backup data processing result can be obtained.

[0132] The arbitration module 5231 obtains the target data processing result with the highest data reliability by screening the first real-time data processing result, the second real-time data processing result, the first backup data processing result and the second backup data processing result; then, the target data processing result is sent to the application module.

[0133] In this embodiment, by quantitatively measuring single-point failures and latent failures required by functional safety, the category of vehicle positioning anomalies and the corresponding safety level can be determined; there is no need to use a specific positioning chip that meets functional safety requirements to locate the vehicle, which reduces the degree of coupling between internal modules of the vehicle's control system and facilitates timely updating of the vehicle's control system.

[0134] Figure 6 shows a schematic flow chart of the working method of the vehicle safety detection system provided by an embodiment of the present application. As shown in Figure 6, the working method of the vehicle safety detection system includes but is not limited to the following steps.

[0135] In step S601 , the independent GNSS chip 510 reads the positioning data in the GNSS data component 522 .

[0136] The GNSS data component 522 can be connected to the main and backup GNSS antennas to facilitate more accurate acquisition of real-time positioning data.

[0137] When the GNSS data component 522 receives the satellite data sent by the GNSS antenna, the GNSS data component 522 will parse the satellite data to obtain the real-time positioning data at the current moment.

[0138] In step S602 , the data monitoring module 5234 detects the data acquisition result of the GNSS data component 522 to obtain data detection information.

[0139] The data detection information includes: a data acquisition success flag or a data acquisition failure flag. The data acquisition failure flag is used to indicate that the GNSS data component 522 fails to acquire the current real-time positioning data; the data acquisition success flag is used to indicate that the GNSS data component 522 successfully acquires the current real-time positioning data.

[0140] In step S603, when the GNSS data component 522 fails to obtain the real-time positioning data at the current moment, the data monitoring module 5234 requests the data / algorithm redundancy module 5235 to provide positioning backup data to the GNSS algorithm component 521, so that the GNSS algorithm component 521 can use the target algorithm to process the positioning backup data and obtain the first data processing result.

[0141] The data / algorithm redundancy module 5235 can use storage redundancy to realize the backup of real-time positioning data, that is, the positioning backup data is the data obtained by the data / algorithm redundancy module 5235 by backing up the real-time positioning data at a preset moment (for example, the moment before the current moment or the preset time length).

[0142] The data / algorithm redundancy module 5235 implements backup of the target algorithm through isolated monitoring, that is, the target algorithm and the backup algorithm are algorithms stored in different hardware respectively.

[0143] While executing step S603 , step S604 is also executed so that the failure processing module 5233 is informed that the GNSS data component 522 is abnormal in obtaining real-time positioning data.

[0144] In step S604 , the data monitoring module 5234 generates data detection information including a data acquisition failure identifier, and sends the data detection information to the failure processing module 5233 .

[0145] It should be noted that, while executing steps S602 to S604 , steps S606 to S608 may also be executed synchronously, so as to obtain data detection information and algorithm detection information at the same time.

[0146] In step S605 , the algorithm monitoring module 5232 detects the effectiveness of the algorithm execution of the GNSS algorithm component 521 and obtains algorithm detection information.

[0147] The algorithm detection information includes an algorithm abnormality flag or an algorithm normality flag. The algorithm abnormality flag is used to indicate that the target algorithm in the GNSS algorithm component 521 is abnormal; the algorithm normality flag is used to indicate that the target algorithm in the GNSS algorithm component 521 is normal.

[0148] In step S606, when the target algorithm in the GNSS algorithm component 521 runs abnormally, the algorithm monitoring module 5232 requests the data / algorithm redundancy module 5235 to provide a backup algorithm so that the data / algorithm redundancy module 5235 can use the backup algorithm to process the positioning data and obtain a second data processing result.

[0149] While executing step S606 , step S607 is also executed so that the failure processing module 5233 is informed that the GNSS algorithm component 521 is operating abnormally.

[0150] In step S607 , the algorithm monitoring module 5232 generates algorithm detection information including an algorithm operation abnormality identifier, and sends the algorithm detection information to the failure processing module 5233 .

[0151] In step S608, the failure processing module 5233 determines whether there is any abnormality in the vehicle positioning process and determines the safety level of the vehicle based on the acquired algorithm detection information including the algorithm operation abnormality flag and / or the data detection information including the data acquisition failure flag.

[0152] The safety level is used to characterize the impact of abnormalities generated during the vehicle positioning process on the functional safety of the vehicle.

[0153] In step S609 , the failure processing module 5233 requests the data / algorithm redundancy module 5235 to provide the arbitration module 5231 with location backup data and / or backup algorithms.

[0154] In step S610 , the data / algorithm redundancy module 5235 sends the located backup data and / or backup algorithm, as well as the second backup data processing result and the second real-time data processing result to the arbitration module 5231 .

[0155] The second backup data processing result is the result obtained by the data / algorithm redundancy module 5235 using its stored backup algorithm to process the stored positioning backup data; the second real-time data processing result is the result obtained by the data / algorithm redundancy module 5235 using its stored backup algorithm to process the real-time positioning data input by the GNSS data component 522.

[0156] It should be noted that the arbitration module 5231 can also obtain: a first real-time data processing result and a first backup data processing result. The first real-time data processing result is obtained by the GNSS algorithm component 521 processing the real-time positioning data input by the GNSS data component 522 using the target algorithm; the first backup data processing result is obtained by the GNSS algorithm component 521 processing the positioning backup data input by the data / algorithm redundancy module 5235 using the target algorithm.

[0157] In step S611 , the arbitration module 5231 obtains a target data processing result with the highest data reliability by screening the first real-time data processing result, the second real-time data processing result, the first backup data processing result, and the second backup data processing result.

[0158] In some embodiments, the data reliability of the first real-time data processing result is the highest; the data reliability of the second real-time data processing result is second; the data reliability of the first backup data processing result is between the data reliability of the second real-time data processing result and the data reliability of the second backup data processing result; and the data reliability of the second backup data processing result is the lowest.

[0159] It should be noted that the backup algorithm and the target algorithm are in a master-backup relationship. The backup algorithm is an algorithm with high reliability and security that is loaded from the GNSS algorithm component 521 at the beginning of system operation.

[0160] In this embodiment, by adopting a combination of software and hardware, the safety level of anomalies in the vehicle positioning process is confirmed. There is no need to use a specific positioning chip that meets functional safety requirements to position the vehicle, which reduces the degree of coupling between the internal modules of the vehicle's control system, facilitates timely updates of the vehicle's control system, improves the stability of the vehicle, and reduces the production cost of the vehicle.

[0161] It should be understood that the present invention is not limited to the specific configurations and processes described in the above embodiments and illustrated in the figures. For the sake of convenience and brevity, detailed descriptions of known methods are omitted here. The specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0162] FIG7 shows a block diagram of an electronic device provided in an embodiment of the present application.

[0163] As shown in Figure 7, the electronic device includes at least one processor 701, at least one memory 702, and one or more I / O interfaces 703. The processor 701, memory 702, and I / O interface 703 are interconnected via a bus 704. The memory 702 stores one or more computer programs, which are executed by the at least one processor 701 to enable the at least one processor 701 to implement any of the vehicle safety detection methods described in the above embodiments.

[0164] Each module in the above-mentioned electronic device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0165] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the vehicle safety detection methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile computer-readable storage medium.

[0166] An embodiment of the present application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned vehicle safety detection method.

[0167] It will be understood by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or non-transitory medium).

[0168] As is well known to those skilled in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0169] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0170] The computer program instructions for performing the operation of the present application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data or source code or object code written in any combination of one or more programming languages, wherein the programming language includes object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or executed completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as by using an Internet service provider to connect to the Internet). In certain embodiments, by utilizing the state information of computer-readable program instructions to personalize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs), the electronic circuits can execute computer-readable program instructions, thereby realizing various aspects of the present application.

[0171] The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0172] Various aspects of the present application are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0173] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0174] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0175] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a special hardware-based system that performs the function or action of the specification, or can be implemented by a combination of special hardware and computer instructions.

[0176] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly indicated, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the scope of the present application as set forth in the appended claims.

Claims

1. A vehicle safety detection method, which is applied to a vehicle-mounted unit, the method comprising: Detecting the data acquisition result of the positioning data acquisition module of the vehicle to obtain data detection information; Performing an algorithm execution validity test on an algorithm execution module of the vehicle to obtain algorithm detection information, wherein the algorithm execution module is configured to process the data input by the positioning data acquisition module using a target algorithm to determine the location of the vehicle; The data detection information and the algorithm detection information are analyzed to determine the safety level of the vehicle.

2. The method according to claim 1, wherein: Detecting the data acquisition result of the positioning data acquisition module of the vehicle to obtain data detection information includes: When the positioning data acquisition module acquires the real-time positioning data at the current moment, data detection information including a data acquisition success identifier is generated, and the positioning data acquisition module is controlled to send the real-time positioning data to the algorithm execution module.

3. The method according to claim 1, wherein: Detecting the data acquisition result of the positioning data acquisition module of the vehicle to obtain data detection information includes: In the event that the positioning data acquisition module fails to acquire the real-time positioning data at the current moment, data detection information including a data acquisition failure identifier is generated, and the backup module of the vehicle is controlled to send the positioning backup data of the previous moment to the algorithm execution module, wherein the backup module is configured to back up the positioning data and generate the positioning backup data.

4. The method according to claim 3, wherein: The backup positioning data includes at least one of the following: global satellite navigation data, real-time dynamic positioning data and inertial navigation data.

5. The method according to claim 1, wherein: Performing a detection of the effectiveness of algorithm execution on the algorithm execution module of the vehicle to obtain algorithm detection information includes: In the case that the target algorithm in the algorithm execution module operates normally, the first data processing result sent by the algorithm execution module is acquired, and algorithm detection information including an indication that the algorithm operates normally is generated.

6. The method according to claim 1, wherein: Performing a detection of the effectiveness of algorithm execution on the algorithm execution module of the vehicle to obtain algorithm detection information includes: In the event that the target algorithm in the algorithm execution module operates abnormally, the backup module controlling the vehicle uses a backup algorithm to process the positioning data, obtains a second data processing result, and generates algorithm detection information including an algorithm operation abnormality identifier, wherein the backup module is configured to back up the target algorithm and generate a backup algorithm.

7. The method according to claim 6, wherein: The backup algorithm includes at least one of the following: a real-time dynamic positioning algorithm, a fusion positioning algorithm, and a dead reckoning algorithm.

8. The method according to claim 1, wherein: In the case where the target algorithm in the algorithm execution module runs abnormally and / or the positioning data acquisition module fails to acquire the real-time positioning data at the current moment, analyzing the data detection information and the algorithm detection information to determine the safety level of the vehicle includes: Analyze the data detection information and the algorithm detection information to determine that the algorithm detection information includes an algorithm operation abnormality flag and / or the data detection information includes a data acquisition failure flag; Acquire first abnormality information of the algorithm execution module and / or second abnormality information of the data acquisition module according to the data acquisition failure flag and / or the algorithm operation abnormality flag; Based on the first abnormal information and / or the second abnormal information, the positioning abnormality of the vehicle is measured to determine the positioning abnormality of the vehicle and its corresponding safety level, wherein the positioning abnormality of the vehicle includes single-point positioning abnormality and / or multi-point positioning abnormality of the vehicle.

9. The method according to claim 6, wherein: The first data processing result sent by the algorithm execution module includes a first real-time data processing result obtained by processing the real-time positioning data at the current moment using the target algorithm, and a first backup data processing result obtained by processing the positioning backup data using the target algorithm; The second data processing result includes a second real-time data processing result obtained by processing the real-time positioning data using the backup algorithm, and a second backup data processing result obtained by processing the positioning backup data using the backup algorithm. The positioning backup data is data obtained by backing up the real-time positioning data at a moment before the current moment; After analyzing the data detection information and the algorithm detection information to determine the safety level of the vehicle, the method further includes: Screening the first real-time data processing result, the second real-time data processing result, the first backup data processing result, and the second backup data processing result to obtain a target data processing result with the highest data reliability; The target data processing result is sent to an application module of the vehicle.

10. The method according to any one of claims 1 to 9, wherein: After analyzing the data detection information and the algorithm detection information to determine the safety level of the vehicle, the method further includes: Determining an alarm level according to a safety level corresponding to the vehicle; Generate warning information corresponding to the warning level.

11. The method according to any one of claims 1 to 9, wherein: The safety level of the vehicle is a level determined based on at least one of the degree of damage caused by the positioning abnormality to the vehicle, the possibility of occurrence of the positioning abnormality, and the controllability of the positioning abnormality. The degree of damage caused by the positioning anomaly to the vehicle includes at least one of the following: no damage, slight or moderate damage, severe damage, and extremely severe damage; The probability of occurrence of the positioning anomaly includes at least one of the following: low probability, medium probability and high probability; The controllability of the positioning abnormality includes at least one of the following: the vehicle is controllable, components of the vehicle are controllable by simple operation, components of the vehicle are controllable by normal operation, and the vehicle is uncontrollable.

12. A vehicle-mounted unit, comprising: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle safety detection method as described in any one of claims 1 to 11.

13. A readable storage medium, wherein: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle safety detection method according to any one of claims 1 to 11 is implemented.

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