Self-diagnosis method and device for vehicle occupant protection system

Abstract

The present invention relates to the technical field of vehicle safety. Disclosed are a self-diagnosis method and device for a vehicle occupant protection system. According to the present invention, the real vehicle control parameters are input into an AEB digital twin model, and a virtual second vehicle motion response parameter and a virtual second AEB system response parameter are calculated; the correlation between a real first vehicle motion response parameter and the virtual second vehicle motion response parameter of a vehicle is calculated, and the correlation between a real first AEB system response parameter and the virtual second AEB system response parameter of the vehicle is calculated; and on the basis of the correlations between the real response parameters and the virtual response parameters, it is determined whether an AEB system is abnormal or not. The self-diagnosis function of the AEB system based on digital twin technology is achieved.

Description

Vehicle occupant protection system self-diagnosis method and device Technical Field

[0001] The present invention relates to the field of vehicle safety technology, and in particular to a self-diagnosis method and device for a vehicle occupant protection system. Background Art

[0002] The vehicle's occupant protection system includes AEB (Autonomous Emergency Braking) and MSB (Motorized Seat Belt). AEB automatically applies the brakes to avoid a collision if the distance between the vehicle and a forward obstacle falls below a safe distance, while MSB protects occupants after a collision. Together, AEB and MSB protect occupants.

[0003] However, the AEB system cannot always provide good occupant protection. When its safety performance declines, this abnormal performance decline needs to be discovered in a timely manner. Therefore, the industry needs to consider how to detect whether the vehicle's AEB system is abnormal.

[0004] Summary of the Invention

[0005] The present invention solves the technical problem of how to detect whether a vehicle's AEB system is abnormal by providing a vehicle occupant protection system self-diagnosis method and device.

[0006] On the one hand, the present invention provides the following technical solutions:

[0007] A vehicle occupant protection system self-diagnosis method, comprising:

[0008] Obtaining a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before and a second time period after each event that triggers the AEB function;

[0009] Inputting the vehicle control parameters into the AEB digital twin model to calculate a second vehicle motion response parameter and a second AEB system response parameter;

[0010] calculating a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter;

[0011] If both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds, it is determined that the AEB system is normal;

[0012] If at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold, it is determined that the AEB system is abnormal.

[0013] Optionally, the vehicle control parameters include gear position, steering, vehicle speed, steering wheel angle and vehicle distance.

[0014] Optionally, the first vehicle motion response parameter or the second vehicle motion response parameter includes vehicle relative distance, vehicle relative speed and vehicle acceleration.

[0015] Optionally, the first AEB system response parameter or the second AEB system response parameter includes AEB warning and brake pressure.

[0016] Optionally, calculating a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter includes:

[0017] f[n] is the first vehicle motion response parameter, g[n] is the second vehicle motion response parameter, and R is the first correlation coefficient; or f[n] is the first AEB system response parameter, g[n] is the second AEB system response parameter, and R is the second correlation coefficient.

[0018] Optionally, the vehicle occupant protection system self-diagnosis method further includes:

[0019] Obtain the first MSB system response parameter within a first time period before the start and a second time period after the end of each event that triggers the AEB function;

[0020] After calculating the first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and the second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter, the method further includes:

[0021] If both the first correlation coefficient and the second correlation coefficient are higher than the corresponding correlation threshold, inputting the second vehicle motion response parameter into the MSB digital twin model to calculate a second MSB system response parameter;

[0022] calculating a third correlation coefficient between the first MSB system response parameter and the second MSB system response parameter;

[0023] If the third correlation coefficients are all higher than the corresponding correlation thresholds, it is determined that the MSB system is normal;

[0024] If at least one of the third correlation coefficients is lower than the corresponding correlation threshold, it is determined that the MSB system is abnormal.

[0025] Optionally, the first MSB system response parameter or the second MSB system response parameter includes preload force, preload moment and retraction amount.

[0026] On the other hand, the present invention also provides the following technical solutions:

[0027] A vehicle occupant protection system self-diagnosis device, comprising:

[0028] An acquisition module is configured to acquire a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before and a second time period after each event triggering the AEB function;

[0029] a calculation module configured to input the vehicle control parameter into the AEB digital twin model to calculate a second vehicle motion response parameter and a second AEB system response parameter; calculate a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and calculate a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter;

[0030] The judgment module is configured to judge that the AEB system is normal if both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds; and judge that the AEB system is abnormal if at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold.

[0031] On the other hand, the present invention also provides the following technical solutions:

[0032] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any one of the above-mentioned vehicle occupant protection system self-diagnosis methods is implemented.

[0033] On the other hand, the present invention also provides the following technical solutions:

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any of the above-mentioned vehicle occupant protection system self-diagnosis methods.

[0035] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0036] The present invention inputs the vehicle's real control parameters into the AEB digital twin model, calculates the virtual second vehicle motion response parameters and the second AEB system response parameters, calculates the correlation between the vehicle's real first vehicle motion response parameters and the virtual second vehicle motion response parameters, and the correlation between the vehicle's real first AEB system response parameters and the virtual second AEB system response parameters, and can determine whether the AEB system is abnormal based on the correlation between the real response parameters and the virtual response parameters, thereby realizing the AEB system self-diagnosis function based on digital twin technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] FIG1 is a flow chart of a self-diagnosis method for a vehicle occupant protection system according to an embodiment of the present invention;

[0039] FIG2 is a schematic diagram of a self-diagnosis device for a vehicle occupant protection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiments of the present invention solve the technical problem of how to detect whether a vehicle's AEB system is abnormal by providing a vehicle occupant protection system self-diagnosis method and device.

[0041] In order to better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0042] As shown in FIG1 , the vehicle occupant protection system self-diagnosis method of this embodiment includes:

[0043] Step S1, obtaining a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before and a second time period after each event triggering the AEB function;

[0044] Step S2: inputting the vehicle control parameters into the AEB digital twin model to calculate a second vehicle motion response parameter and a second AEB system response parameter;

[0045] Step S3, calculating a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter;

[0046] Step S4: If both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds, it is determined that the AEB system is normal;

[0047] In step S5 , if at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold, it is determined that the AEB system is abnormal.

[0048] In step S1, the AEB function is primarily triggered by the detection of an obstacle in front of the vehicle. Both the first and second durations can be 5 seconds. Step S1 acquires the vehicle control parameters, first vehicle motion response parameters, and first AEB system response parameters for the seconds before and after the AEB system activates. The vehicle control parameters determine the vehicle's motion state and the AEB system's response state for the seconds before and after each AEB activation. The AEB system comprises an AEB environmental perception system, an AEB decision system, and an AEB actuator system. AEB environmental perception involves vehicle control parameters, which can include gear position, steering, vehicle speed, steering wheel angle, and vehicle distance.

[0049] In step S2, the AEB digital twin model is a simulation model consistent with the actual vehicle's AEB system, fully simulating the AEB system's functionality. The AEB digital twin model meets required accuracy. The AEB digital twin model includes an AEB model, an AEB actuator model, and a vehicle dynamics model. The vehicle motion response parameter represents the vehicle's motion state within a few seconds before and after each AEB system activation, while the AEB system response parameter represents the AEB system's response state within a few seconds before and after each AEB system activation. The vehicle control parameters determine the vehicle's motion state and the AEB system's response state, thereby determining the second vehicle motion response parameter and the second AEB system response parameter. The first vehicle motion response parameter includes vehicle relative distance, vehicle relative speed, and vehicle acceleration. The vehicle relative distance is the distance of the vehicle from the forward obstacle, and the vehicle relative speed is the speed of the vehicle from the forward obstacle. The second vehicle motion response parameter also includes vehicle relative distance, vehicle relative speed, and vehicle acceleration. The first AEB system response parameter includes AEB warning and brake pressure, while the second AEB system response parameter also includes AEB warning and brake pressure. The first vehicle motion response parameter and the first AEB system response parameter are both real response signals of the vehicle, and the second vehicle motion response parameter and the second AEB system response parameter are both virtual response signals obtained by simulation.

[0050] It is understood that if the safety of the vehicle's AEB system has not decreased, the correlation between the second vehicle motion response parameter and the first vehicle motion response parameter should be high, and the correlation between the second AEB system response parameter and the first AEB system response parameter should also be high. If the correlation between the second vehicle motion response parameter and the first vehicle motion response parameter or the correlation between the second AEB system response parameter and the first AEB system response parameter is low, it can be considered that the safety of the AEB system has decreased and the AEB system is abnormal. Step S3 provides a correlation coefficient that represents the correlation, and step S3 may include:

[0051] f[n] is the first vehicle motion response parameter, g[n] is the second vehicle motion response parameter, and R is the first correlation coefficient; or f[n] is the first AEB system response parameter, g[n] is the second AEB system response parameter, and R is the second correlation coefficient. Specifically, let f[n] be the vehicle relative distance and g[n] be the vehicle relative distance, and the first correlation coefficient R obtained is the correlation coefficient of the vehicle relative distance; let f[n] be the vehicle relative speed and g[n] be the vehicle relative speed, and the first correlation coefficient R obtained is the correlation coefficient of the vehicle relative speed; let f[n] be the vehicle acceleration and g[n] be the vehicle acceleration, and the first correlation coefficient R obtained is the correlation coefficient of the vehicle acceleration; let f[n] be the AEB warning and g[n] be the AEB warning, and the second correlation coefficient R obtained is the correlation coefficient of the AEB warning; let f[n] be the brake pressure and g[n] be the brake pressure, and the second correlation coefficient R obtained is the correlation coefficient of the brake pressure.

[0052] For the correlation thresholds in step S4 and step S5, the correlation thresholds corresponding to vehicle relative distance, vehicle relative speed, vehicle acceleration, AEB warning, and brake pressure may be 0.95, 0.90, 0.85, 0.95, and 0.90, respectively.

[0053] As can be seen from the above, the embodiment of the present invention inputs the vehicle's real control parameters into the AEB digital twin model, calculates the virtual second vehicle motion response parameters and the second AEB system response parameters, calculates the correlation between the vehicle's real first vehicle motion response parameters and the virtual second vehicle motion response parameters, and the correlation between the vehicle's real first AEB system response parameters and the virtual second AEB system response parameters. Based on the correlation between the real response parameters and the virtual response parameters, it can be determined whether the AEB system is abnormal, thereby realizing the AEB system self-diagnosis function based on digital twin technology.

[0054] As mentioned above, the vehicle occupant protection system includes the AEB system and the MSB system. Similarly, the MSB system may not always provide a good occupant protection effect. When its safety performance deteriorates, it is also necessary to promptly detect this abnormal performance degradation. Therefore, the vehicle occupant protection system self-diagnosis method of the embodiment of the present invention may also include:

[0055] Obtain the first MSB system response parameter within a first time period before the start and a second time period after the end of each event that triggers the AEB function;

[0056] After step S3, the vehicle occupant protection system self-diagnosis method also includes: if the first correlation coefficient and the second correlation coefficient are both higher than the corresponding correlation threshold, the second vehicle motion response parameter is input into the MSB digital twin model to calculate the second MSB system response parameter; the third correlation coefficient between the first MSB system response parameter and the second MSB system response parameter is calculated; if the third correlation coefficients are all higher than the corresponding correlation threshold, the MSB system is judged to be normal; if at least one third correlation coefficient is lower than the corresponding correlation threshold, the MSB system is judged to be abnormal.

[0057] The MSB digital twin model is a simulation model consistent with the actual vehicle's MSB system, fully simulating the MSB system's functionality. The MSB digital twin model includes the MSB model, the MSB actuator model, and the vehicle and occupant motion models. The MSB system response parameters represent the MSB system's response state within a few seconds before and after each MSB system activation. The vehicle's motion state determines the MSB system's response state, meaning that the second vehicle motion response parameters determine the second MSB system response parameters. Inputting the second vehicle motion response parameters into the MSB digital twin model yields a virtual second MSB system response parameter. If the first MSB system response parameters include preload, preload moment, and retraction amount, then the second MSB system response parameters also include preload, preload moment, and retraction amount.

[0058] It can be understood that if the safety of the vehicle's MSB system is not compromised, the correlation between the second MSB system response parameter and the first MSB system response parameter should be high. If the correlation between the second MSB system response parameter and the first MSB system response parameter is low, it can be considered that the safety of the MSB system has deteriorated and the MSB system is abnormal. The correlation coefficient is used to represent correlation. The third correlation coefficient is calculated using the same formula as in step S3. Let f[n] be the preload force and g[n] be the preload force, and the third correlation coefficient R is the correlation coefficient for the preload force; let f[n] be the preload moment and g[n] be the preload moment, and the third correlation coefficient R is the correlation coefficient for the preload moment; let f[n] be the retraction amount and g[n] be the retraction amount, and the third correlation coefficient R is the correlation coefficient for the retraction amount.

[0059] As shown in FIG2 , an embodiment of the present invention further provides a vehicle occupant protection system self-diagnosis device, comprising:

[0060] An acquisition module is configured to acquire a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before and a second time period after each event triggering the AEB function;

[0061] a calculation module configured to input the vehicle control parameters into the AEB digital twin model to calculate a second vehicle motion response parameter and a second AEB system response parameter; calculate a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and calculate a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter;

[0062] The judgment module is configured to judge that the AEB system is normal if both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds; and to judge that the AEB system is abnormal if at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold.

[0063] Furthermore, the acquisition module may be further configured to acquire a first MSB system response parameter within a first time period before the start and a second time period after the end of each event triggering the AEB function;

[0064] The calculation module may be further configured to input the second vehicle motion response parameter into the MSB digital twin model to calculate a second MSB system response parameter if both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds; and calculate a third correlation coefficient between the first MSB system response parameter and the second MSB system response parameter;

[0065] The judgment module may also be configured to judge that the MSB system is normal if all third correlation coefficients are higher than corresponding correlation thresholds; and judge that the MSB system is abnormal if at least one third correlation coefficient is lower than corresponding correlation thresholds.

[0066] Based on the same inventive concept as the vehicle occupant protection system self-diagnosis method described above, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the steps of any one of the vehicle occupant protection system self-diagnosis methods described above are implemented.

[0067] Among them, the bus architecture (represented by the bus), the bus can include any number of interconnected buses and bridges, and the bus links together various circuits including one or more processors represented by the processor and memory represented by the memory. The bus can also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter can be the same component, namely a transceiver, which provides a unit for communicating with various other devices on a transmission medium. The processor is responsible for managing the bus and general processing, while the memory can be configured to store data used by the processor when performing operations.

[0068] Since the electronic device described in the embodiment of the present invention is an electronic device used to implement the vehicle occupant protection system self-diagnosis method in the embodiment of the present invention, based on the vehicle occupant protection system self-diagnosis method described in the embodiment of the present invention, those skilled in the art will be able to understand the specific implementation of the electronic device in the embodiment of the present invention and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention will not be described in detail here. As long as those skilled in the art implement the electronic device used in the vehicle occupant protection system self-diagnosis method in the embodiment of the present invention, it falls within the scope of protection of the present invention.

[0069] Based on the same inventive concept as the above-mentioned vehicle occupant protection system self-diagnosis method, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any of the above-mentioned vehicle occupant protection system self-diagnosis methods.

[0070] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0072] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0074] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0075] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A vehicle occupant protection system self-diagnosis method, characterized in that: include: Obtaining a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before and within a second time period after each event that triggers the AEB function; Inputting the vehicle control parameter into the AEB digital twin model to calculate a second vehicle motion response parameter and a second AEB system response parameter; calculating a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter; If both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds, it is determined that the AEB system is normal; If at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold, it is determined that the AEB system is abnormal.

2. The vehicle occupant protection system self-diagnosis method according to claim 1, characterized in that: The vehicle control parameters include gear position, steering, vehicle speed, steering wheel angle and vehicle distance.

3. The vehicle occupant protection system self-diagnosis method according to claim 1, characterized in that: The first vehicle motion response parameter or the second vehicle motion response parameter includes a vehicle relative distance, a vehicle relative speed and a vehicle acceleration.

4. The vehicle occupant protection system self-diagnosis method according to claim 1, characterized in that: The first AEB system response parameter or the second AEB system response parameter includes AEB warning and brake pressure.

5. The vehicle occupant protection system self-diagnosis method according to claim 1, characterized in that: The calculating a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter, includes: f[n] is the first vehicle motion response parameter, g[n] is the second vehicle motion response parameter, and R is the first correlation coefficient; or f[n] is the first AEB system response parameter, g[n] is the second AEB system response parameter, and R is the second correlation coefficient.

6. The vehicle occupant protection system self-diagnosis method according to claim 1, characterized in that: Also includes: Obtain the first MSB system response parameter within a first time period before each event triggering the AEB function and within a second time period after the event ends; After calculating the first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and the second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter, the method further includes: If both the first correlation coefficient and the second correlation coefficient are higher than the corresponding correlation threshold, the second vehicle motion response parameter is input into the MSB digital twin model to calculate a second MSB system response parameter; calculating a third correlation coefficient between the first MSB system response parameter and the second MSB system response parameter; If the third correlation coefficients are all higher than the corresponding correlation thresholds, it is determined that the MSB system is normal; If at least one of the third correlation coefficients is lower than the corresponding correlation threshold, it is determined that the MSB system is abnormal.

7. The vehicle occupant protection system self-diagnosis method according to claim 6, characterized in that: The first MSB system response parameter or the second MSB system response parameter includes a preload force, a preload moment, and a retraction amount.

8. A vehicle occupant protection system self-diagnosis device, characterized in that: include: An acquisition module is configured to acquire a vehicle control parameter, a first vehicle motion response parameter, and a first AEB system response parameter within a first time period before each event triggering the AEB function and within a second time period after each event triggering the AEB function; A calculation module is configured to input the vehicle control parameter into the AEB digital twin model, calculate a second vehicle motion response parameter and a second AEB system response parameter; calculate a first correlation coefficient between the first vehicle motion response parameter and the second vehicle motion response parameter, and a second correlation coefficient between the first AEB system response parameter and the second AEB system response parameter; The judgment module is configured to judge that the AEB system is normal if both the first correlation coefficient and the second correlation coefficient are higher than corresponding correlation thresholds; and judge that the AEB system is abnormal if at least one of the first correlation coefficient or the second correlation coefficient is lower than the corresponding correlation threshold.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the vehicle occupant protection system self-diagnosis method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle occupant protection system self-diagnosis method according to any one of claims 1 to 7 is implemented.

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