Collision event determination method and apparatus, device, and storage medium

Abstract

The present application discloses a collision event determination method and apparatus, a device, and a storage medium, which are used for improving the accuracy of collision event determination. The method of the present application comprises: on the basis of gyroscope signals acquired within a first period, performing weighted fitting processing on acceleration original signals acquired within the first period to obtain first optimization signals; filtering the first optimization signals to obtain second optimization signals; on the basis of the gyroscope signals acquired within the first period, performing weighted fitting processing on the second optimization signals to obtain target optimization signals; on the basis of the target optimization signals corresponding to each first period, obtaining target signals corresponding to a second period; and on the basis of the target signals, determining whether a collision event occurs within the second period. By means of the gyroscope signals, the original noise of an acceleration sensor is reduced, the sensitive noise of mechanical vibration in the acceleration original signals is reduced, and the noise caused by the gravity and inclination effects in the acceleration original signals is reduced, thereby ensuring the accuracy of collision event determination.

Description

A method, device, equipment and storage medium for determining a collision event

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on July 17, 2023, with application number 202310875961.6 and application name “A method, device, equipment and storage medium for determining a collision event”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of electronic control systems, and in particular to a collision event determination method, device, equipment, and storage medium. Background Art

[0003] Currently, automobile collision assessments typically utilize pulse and camera methods. The camera method, however, has not been widely commercialized due to various application scenarios. The pulse method, primarily based on single and dual accelerometers, is however prevalent. However, due to various collision events and the specificity of the collision pulse transmission path, the fundamental frequency often approaches or even exceeds the sampling frequency of the vehicle accelerometer. This causes pulses exceeding the Nyquist frequency to become noise aliasing, affecting collision assessment. This forces traditional manufacturers to employ one or more higher-precision sensors and higher-frequency control chips, while also improving the stability of the filtering algorithm to achieve sampling. However, this method is not only expensive but also inaccurate in collision assessments.

[0004] Summary of the Invention

[0005] In view of this, the present application provides a collision event determination method, apparatus, device, and storage medium for improving the accuracy of collision event determination.

[0006] In a first aspect, an embodiment of the present application provides a method for determining a collision event, the method comprising:

[0007] For the gyroscope signal and the original acceleration signal obtained in each first period, performing: based on the gyroscope signal obtained in the first period, performing weighted fitting processing on the original acceleration signal obtained in the first period to obtain a first optimized signal; performing filtering processing on the first optimized signal to obtain a second optimized signal; and performing weighted fitting processing on the second optimized signal based on the gyroscope signal obtained in the first period to obtain a target optimized signal;

[0008] Obtaining a target signal corresponding to a second cycle based on a target optimization signal corresponding to each first cycle; wherein the second cycle includes a preset number of first cycles;

[0009] Determine whether a collision event occurs within the second period based on the destination signal.

[0010] In the present application, the original noise of the acceleration sensor is reduced by using the gyroscope signal to process the original acceleration signal. The sensitive noise of mechanical vibration in the original acceleration signal is reduced by using the gyroscope signal to process the filtered original acceleration signal. The noise introduced in the original acceleration signal due to gravity and tilt is reduced by filtering the first optimized signal. Therefore, the target signal obtained in the present application is more accurate, thereby ensuring the accuracy of the judgment of the collision event.

[0011] In some possible embodiments, performing weighted fitting processing on the original acceleration signal acquired in the first period based on the gyroscope signal acquired in the first period to obtain the first optimized signal includes:

[0012] constructing a first curve based on the gyroscope signal acquired in the first period;

[0013] constructing a second curve based on the original acceleration signal acquired during the first period;

[0014] The first curve and the second curve are weighted-fitted according to a first preset weight corresponding to the first curve and a second preset weight corresponding to the second curve to obtain the first optimized signal.

[0015] In the present application, different weights are set for the gyroscope signal and the original acceleration signal, thereby reducing the noise introduced into the original acceleration signal due to gravity and tilt effects.

[0016] In some possible embodiments, filtering the first optimized signal to obtain the second optimized signal includes:

[0017] Simplify the Kalman filter to obtain the target filter;

[0018] The first optimized signal is filtered using the target filter to obtain the second optimized signal.

[0019] In the present application, by filtering the first optimized signal, the noise in the original acceleration signal is further reduced, thereby ensuring the accuracy of the determined target signal.

[0020] In some possible embodiments, the simplifying process of the Kalman filter to obtain the target filter includes:

[0021] The preset weights are used to replace the weights in the Kalman filter, where the weights in the Kalman filter are weights updated in real time according to the noise of the original acceleration signal.

[0022] In this application, by simplifying the Kalman filter, the amount of calculation is reduced and computing resources are saved.

[0023] In some possible embodiments, performing weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first period to obtain a target optimization signal includes:

[0024] constructing a first curve based on the gyroscope signal acquired in the first period;

[0025] constructing a third curve based on the first optimization signal;

[0026] performing weighted fitting processing on the first curve and the third curve according to a first preset weight corresponding to the first curve and a third preset weight corresponding to the third curve to obtain a third optimized signal;

[0027] The third optimization signal is sampled to obtain the target optimization signal.

[0028] In this application, by setting different weights, the noise in the original acceleration signal is reduced.

[0029] In some possible embodiments, the sampling and processing of the third optimization signal to obtain the target optimization signal includes:

[0030] Based on the timing of each third optimized signal, a preset filling method is used to fill a first intermediate point between every two third optimized signals to obtain a third optimized signal set;

[0031] Performing Shannon supersampling on the third optimized signal set to obtain a target third optimized signal;

[0032] Integrate the target third optimization signal to obtain the target optimization signal.

[0033] In this application, the target optimization signal is determined by a preset filling method and a Shannon oversampling method, which reduces the peak overflow during software sampling and restoration of the traditional collision accelerometer and improves the technical accuracy and reliability.

[0034] In some possible embodiments, obtaining the target signal corresponding to the second period based on the target optimization signal corresponding to each first period includes:

[0035] Sorting the target optimization signals according to the time sequence of each target optimization signal to obtain a target optimization signal sequence;

[0036] In the target optimization signal sequence, a preset filling method is used to fill a second intermediate signal between every two target optimization signals to obtain a target optimization signal set;

[0037] Performing Shannon supersampling on the target optimization signal set to obtain an initial target signal;

[0038] The initial target signal is integrated to obtain the target signal.

[0039] In this application, the target optimization signal is determined by a preset filling method and a Shannon oversampling method, which reduces the peak overflow during software sampling and restoration of the traditional collision accelerometer and improves the technical accuracy and reliability.

[0040] In a second aspect, an embodiment of the present application provides a collision event determination device, the device comprising:

[0041] a target optimization signal determination module, configured to perform, for each gyroscope signal and the original acceleration signal acquired in each first period: performing weighted fitting processing on the original acceleration signal acquired in the first period based on the gyroscope signal acquired in the first period to obtain a first optimization signal; performing filtering processing on the first optimization signal to obtain a second optimization signal; and performing weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first period to obtain a target optimization signal;

[0042] a target signal determination module, configured to obtain a target signal corresponding to a second period based on a target optimization signal corresponding to each first period; wherein the second period includes a preset number of first periods;

[0043] A collision event determination module is configured to determine whether a collision event occurs within the second period based on the destination signal.

[0044] In some possible embodiments, the target optimization signal determination module performs weighted fitting processing on the original acceleration signal obtained in the first period based on the gyroscope signal obtained in the first period to obtain the first optimization signal, specifically for:

[0045] constructing a first curve based on the gyroscope signal acquired in the first period;

[0046] constructing a second curve based on the original acceleration signal acquired during the first period;

[0047] The first curve and the second curve are weighted-fitted according to a first preset weight corresponding to the first curve and a second preset weight corresponding to the second curve to obtain the first optimized signal.

[0048] In some possible embodiments, when the target optimization signal determination module performs filtering processing on the first optimization signal to obtain the second optimization signal, it is specifically configured to:

[0049] Simplify the Kalman filter to obtain the target filter;

[0050] The first optimized signal is filtered using the target filter to obtain the second optimized signal.

[0051] In some possible embodiments, the target optimization signal determination module performs simplified processing on the Kalman filter to obtain the target filter, specifically for:

[0052] The preset weights are used to replace the weights in the Kalman filter, where the weights in the Kalman filter are weights updated in real time according to the noise of the original acceleration signal.

[0053] In some possible embodiments, the target optimization signal determination module performs weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first period to obtain the target optimization signal, specifically for:

[0054] constructing a first curve based on the gyroscope signal acquired in the first period;

[0055] constructing a third curve based on the first optimization signal;

[0056] performing weighted fitting processing on the first curve and the third curve according to a first preset weight corresponding to the first curve and a third preset weight corresponding to the third curve to obtain a third optimized signal;

[0057] The third optimization signal is sampled to obtain the target optimization signal.

[0058] In some possible embodiments, when the target optimization signal determination module performs sampling processing on the third optimization signal to obtain the target optimization signal, it is specifically configured to:

[0059] Based on the timing of each third optimized signal, a preset filling method is used to fill a first intermediate point between every two third optimized signals to obtain a third optimized signal set;

[0060] Performing Shannon supersampling on the third optimized signal set to obtain a target third optimized signal;

[0061] Integrate the target third optimization signal to obtain the target optimization signal.

[0062] In some possible embodiments, when the target signal determination module obtains the target signal corresponding to the second period based on the target optimization signal corresponding to each first period, it is specifically configured to:

[0063] Sorting the target optimization signals according to the time sequence of each target optimization signal to obtain a target optimization signal sequence;

[0064] In the target optimization signal sequence, a preset filling method is used to fill a second intermediate signal between every two target optimization signals to obtain a target optimization signal set;

[0065] Performing Shannon supersampling on the target optimization signal set to obtain an initial target signal;

[0066] The initial target signal is integrated to obtain the target signal.

[0067] In the third aspect, another embodiment of the present application also provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the methods provided in the embodiment of the first aspect of the present application.

[0068] In a fourth aspect, another embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute any method provided in the embodiment of the first aspect of the present application.

[0069] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0071] FIG1 is a schematic diagram of an application scenario of a collision event determination method provided by an embodiment of the present application;

[0072] FIG2 is a schematic flow chart of a method for determining a collision event according to an embodiment of the present application;

[0073] FIG3 is a schematic diagram of a flow chart of fitting processing of an original acceleration signal in a method for determining a collision event provided by an embodiment of the present application;

[0074] FIG4 is a schematic diagram of a first curve, a second curve, and curves corresponding to a first optimization signal in a method for determining a collision event provided by an embodiment of the present application;

[0075] FIG5 is a schematic diagram of a flow chart of weighted fitting processing of a second optimization signal in a method for determining a collision event provided by an embodiment of the present application;

[0076] FIG6 is a schematic diagram of a first curve, a third curve, and a curve corresponding to a third optimization signal of a collision event determination method provided by an embodiment of the present application;

[0077] FIG7 is a schematic diagram of a flow chart of sampling and processing a third optimized signal in a method for determining a collision event provided by an embodiment of the present application;

[0078] FIG8 is a schematic diagram of a curve corresponding to a third optimized signal set of a collision event determination method provided by an embodiment of the present application;

[0079] FIG9 is a schematic diagram of a flow chart of determining a target signal according to a target optimization signal in a method for determining a collision event provided by an embodiment of the present application;

[0080] FIG10 is a schematic diagram of a device for determining a collision event provided by an embodiment of the present application;

[0081] FIG11 is a schematic diagram of an electronic device for a collision event determination method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0082] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0083] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0084] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0085] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0086] The embodiments of the present application provide a method, apparatus, device and storage medium for determining a collision event, which are used to improve the accuracy of determining a collision event. The inventive concept of the present application can be summarized as follows: for the gyroscope signal and the original acceleration signal obtained in each first period, the following steps are performed: based on the gyroscope signal obtained in the first period, the original acceleration signal obtained in the first period is weightedly fitted to obtain a first optimized signal; the first optimized signal is filtered to obtain a second optimized signal; based on the gyroscope signal obtained in the first period, the second optimized signal is weightedly fitted to obtain a target optimized signal; based on the target optimized signal corresponding to each first period, a target signal corresponding to the second period is obtained; wherein the second period includes a preset number of first periods; based on the target signal, whether a collision event occurs in the second period is determined. In the present application, the original noise of the acceleration sensor is reduced by using the gyroscope signal to process the original acceleration signal. The sensitive noise of mechanical vibration in the original acceleration signal is reduced by using the gyroscope signal to process the filtered original acceleration signal. The noise introduced in the original acceleration signal due to gravity and tilt is reduced by filtering the first optimized signal. Therefore, the target signal obtained in the present application is more accurate, thereby ensuring the accuracy of the judgment of the collision event.

[0087] For ease of understanding, a collision event determination method provided by an embodiment of the present application is described in detail below with reference to the accompanying drawings:

[0088] As shown in FIG1 , an application scenario diagram of a collision event determination method in an embodiment of the present application is shown. The diagram includes: a microcontroller unit (MCU) 10, a gyroscope 20, and an acceleration sensor 30; wherein:

[0089] The MCU10 performs the following operations on the gyroscope signal of the gyroscope 20 and the original acceleration signal of the acceleration sensor 30 obtained in each first cycle: performing weighted fitting processing on the original acceleration signal obtained in the first cycle based on the gyroscope signal obtained in the first cycle to obtain a first optimized signal; performing filtering processing on the first optimized signal to obtain a second optimized signal; performing weighted fitting processing on the second optimized signal based on the gyroscope signal obtained in the first cycle to obtain a target optimized signal; obtaining a target signal corresponding to the second cycle based on the target optimized signal corresponding to each first cycle; wherein the second cycle includes a preset number of first cycles; and determining whether a collision event occurs in the second cycle based on the target signal.

[0090] The description in this application only details a single MCU 10, a gyroscope 20, and an accelerometer 30, but those skilled in the art will appreciate that the illustrated MCU 10, gyroscope 20, and accelerometer 30 are intended to represent the operations of the MCU 10, gyroscope 20, and accelerometer 30 involved in the technical solution of this application. This does not imply any limitation on the number, type, or location of the MCU 10, gyroscope 20, and accelerometer 30. It should be noted that adding additional modules to the illustrated environment or removing individual modules from it does not change the underlying concepts of the example embodiments of this application. Furthermore, those skilled in the art will appreciate that the transmission and reception of the above-mentioned data also needs to be implemented over a network.

[0091] It should be noted that the collision event determination method proposed in this application is not only applicable to the application scenario shown in FIG1 , but also applicable to any device that has a collision event determination requirement.

[0092] FIG2 is a flow chart of a method for determining a collision event according to an embodiment of the present application, wherein:

[0093] In step 201: for the gyroscope signal and the original acceleration signal obtained in each first period, the following steps are performed: based on the gyroscope signal obtained in the first period, the original acceleration signal obtained in the first period is weightedly fitted to obtain a first optimized signal; the first optimized signal is filtered to obtain a second optimized signal; based on the gyroscope signal obtained in the first period, the second optimized signal is weightedly fitted to obtain a target optimized signal.

[0094] In step 202: a target signal corresponding to a second cycle is obtained based on a target optimization signal corresponding to each first cycle; wherein the second cycle includes a preset number of first cycles.

[0095] In step 203 : determining whether a collision event occurs in the second cycle based on the destination signal.

[0096] In the present application, the original noise of the acceleration sensor is reduced by using the gyroscope signal to process the original acceleration signal. The sensitive noise of mechanical vibration in the original acceleration signal is reduced by using the gyroscope signal to process the filtered original acceleration signal. The noise introduced in the original acceleration signal due to gravity and tilt is reduced by filtering the first optimized signal. Therefore, the target signal obtained in the present application is more accurate, thereby ensuring the accuracy of the judgment of the collision event.

[0097] To facilitate further understanding of a collision event determination method provided in an embodiment of the present application, the steps in FIG. 2 are described in detail below:

[0098] In some possible embodiments, in order to save computing resources while ensuring the accuracy of determining a collision event, the present application sets a first cycle and a second cycle. The second cycle is a long cycle, and each second cycle includes multiple first cycles. Whether a collision event will occur is determined once for each second cycle, and there is no need to perform calculations in real time, thereby reducing the waste of computing resources.

[0099] In some possible embodiments, in order to correct the linear acceleration and reduce the original noise of the acceleration sensor, the gyroscope signal obtained in each first cycle is used in the present application to perform fitting processing on the corresponding original acceleration signal. Specifically, the steps shown in FIG3 can be implemented, wherein:

[0100] In step 301 : a first curve is constructed based on the gyroscope signal acquired in a first period.

[0101] In step 302: a second curve is constructed based on the original acceleration signal acquired in the first cycle.

[0102] In step 303 : weighted fitting processing is performed on the first curve and the second curve according to a first preset weight corresponding to the first curve and a second preset weight corresponding to the second curve to obtain a first optimized signal.

[0103] For example: during the first period, 7 gyroscope signals are collected, namely a1, a2, a3, a4, a5, a6, and a7, and the first curve they constitute is shown in Figure 4; during the first period, 7 original acceleration signals are collected, namely b1, b2, b3, b4, b5, b6, and b7, and the second curve they constitute is shown in Figure 4; the first preset weight corresponding to the first curve is determined to be 0.4, and the second preset weight corresponding to the second curve is determined to be 0.6, then the first curve and the second curve are fitted, and the curve corresponding to the first optimized signal is shown in Figure 4.

[0104] In the present application, by setting the first preset weight and the second preset weight, the influence of gravity acceleration and the tilt angle of the vehicle during driving on the acceleration sensor is reduced, thereby reducing the possibility of false triggering of collision events.

[0105] In some possible embodiments, in order to further reduce the noise in the original acceleration signal and ensure the accuracy of the determined target signal, after obtaining the first optimized signal, it is necessary to filter the first optimized signal to obtain the second optimized signal. Specifically, it can be implemented as follows: simplify the Kalman filter to obtain the target filter; use the target filter to filter the first optimized signal to obtain the second optimized signal.

[0106] In some possible embodiments, when simplifying the Kalman filter, it can be implemented as follows: using preset weights to replace the weights in the Kalman filter, where the weights in the Kalman filter are weights that are updated in real time according to the noise of the original acceleration signal.

[0107] The main difference between the filtering method used in this application and the Kalman filter is that the weights of the filter used in this application are preset weights, which are relatively fixed, while in the Kalman filter, the weights are continuously updated according to the noise of the original acceleration signal. The filter provided by this application can provide "excellent controllable" fitting for practical applications. This application combines a list of vehicle calibration scenarios, adjusts the system noise and measurement noise factors based on the actual vehicle test scenario, and has better compatibility with problems encountered in iterating new engineering application scenarios, and enhances operability under the premise of not much difference in accuracy.

[0108] In some possible embodiments, after obtaining the second optimized signal, a weighted fitting process may be performed on the second optimized signal, which may be specifically implemented as shown in FIG5 , wherein:

[0109] In step 501: a first curve is constructed based on the gyroscope signal acquired in a first period.

[0110] In step 502: construct a third curve based on the first optimization signal.

[0111] In step 503 : weighted fitting processing is performed on the first curve and the third curve according to the first preset weight corresponding to the first curve and the third preset weight corresponding to the third curve to obtain a third optimized signal.

[0112] For example: during the first period, 7 gyroscope signals a1, a2, a3, a4, a5, a6, and a7 are collected, and the first curve they constitute is shown in FIG6 ; the third curve corresponding to the first optimization signal is shown in FIG6 ; the first preset weight corresponding to the first curve is determined to be 0.3, and the third preset weight corresponding to the third curve is determined to be 0.7, then the first curve and the third curve are weightedly fitted, and the curve corresponding to the third optimization signal is shown in FIG6 .

[0113] It should be noted that the specific values ​​of the first preset weight, the second preset weight and the third preset weight are not limited in this application. The above is only an embodiment, and does not limit the specific values ​​of the first preset weight, the second preset weight and the third preset weight. Technical personnel can determine the specific values ​​of the first preset weight, the second preset weight and the third preset weight according to their needs.

[0114] In step 504: the third optimized signal is sampled and processed to obtain a target optimized signal.

[0115] In some possible embodiments, sampling the third optimized signal may be specifically implemented as shown in FIG7 , wherein:

[0116] In step 701 : based on the timing of each third optimized signal, a preset filling method is used to fill a first intermediate point between every two third optimized signals to obtain a third optimized signal set.

[0117] For example: the third optimized signals are c1, c2, c3, c4, c5, c6, and c7; the preset filling method is the mean method, then the first midpoint corresponding to c1 and c2 is determined to be d1 according to c1 and c2; the first midpoint corresponding to c2 and c3 is determined to be d2 according to c2 and c3; the first midpoint corresponding to c3 and c4 is determined to be d3 according to c3 and c4; the first midpoint corresponding to c4 and c5 is determined to be d4 according to c4 and c5; the first midpoint corresponding to c5 and c6 is determined to be d5 according to c5 and c6; the first midpoint corresponding to c6 and c7 is determined to be d6 according to c6 and c7; then the obtained third optimized signal set includes: c1, d1, c2, d2, c3, d3, c4, d4, c5, d5, c6, d6, and c7.

[0118] It should be noted that this application does not limit the specific implementation method of the preset filling method. The preset filling method can be the mean method, interpolation method, etc. The technicians can set the implementation method of the preset filling method according to their needs.

[0119] In step 702: Shannon supersampling is performed on the third optimized signal set to obtain a target third optimized signal.

[0120] For example: the third optimization signal set includes c1, d1, c2, d2, c3, d3, c4, d4, c5, d5, c6, d6, c7; the curve corresponding to the third optimization signal set is shown in Figure 8, and the third optimization signal set is subjected to Shannon supersampling, and the target third optimization signal obtained is d1, c2, d2, d3, c4, d4, d5, c6, d6.

[0121] In step 703: performing integration processing on the target third optimization signal to obtain the target optimization signal.

[0122] For example: the target third optimization signal is d1, c2, d2, d3, c4, d4, d5, c6, d6; then d1, c2, d2, d3, c4, d4, d5, c6, d6 are integrated, and the result is e1, then the target optimization signal is e1.

[0123] In some possible embodiments, after determining the target optimization signal corresponding to each first cycle, in order to further determine the target signal corresponding to the second cycle, the target signal may be determined based on the target optimization signal according to the steps shown in FIG. 9 , wherein:

[0124] In step 901: the target optimization signals are sorted according to the time sequence of each target optimization signal to obtain a target optimization signal sequence.

[0125] For example: the second period includes 8 first periods, namely, first period 1, first period 2, first period 3, first period 4, first period 5, first period 6, first period 7, and first period 8. The first period 1 corresponds to the target optimization signal 1, the first period 2 corresponds to the target optimization signal 2, the first period 3 corresponds to the target optimization signal 3, the first period 4 corresponds to the target optimization signal 4, the first period 5 corresponds to the target optimization signal 5, the first period 6 corresponds to the target optimization signal 6, the first period 7 corresponds to the target optimization signal 7, and the first period 8 corresponds to the target optimization signal 8; the target optimization signals 1 to target optimization signal 8 are sorted, and the obtained target optimization signal sequence is: target optimization signal 1, target optimization signal 2, target optimization signal 3, target optimization signal 4, target optimization signal 5, target optimization signal 6, target optimization signal 7, and target optimization signal 8.

[0126] In step 902 : in the target optimization signal sequence, a preset filling method is used to fill a second intermediate signal between every two target optimization signals to obtain a target optimization signal set.

[0127] For example: the preset filling method is the mean method, and the second intermediate signal is determined to be the second intermediate signal 1 according to the target optimization signal 1 and the target optimization signal 2; the second intermediate signal is determined to be the second intermediate signal 2 according to the target optimization signal 2 and the target optimization signal 3; the second intermediate signal is determined to be the second intermediate signal 3 according to the target optimization signal 3 and the target optimization signal 4; the second intermediate signal is determined to be the second intermediate signal 4 according to the target optimization signal 4 and the target optimization signal 5; the second intermediate signal is determined to be the second intermediate signal 5 according to the target optimization signal 5 and the target optimization signal 6; the second intermediate signal is determined to be the second intermediate signal 6 according to the target optimization signal 6 and the target optimization signal 7; the second intermediate signal is determined to be the second intermediate signal 7 according to the target optimization signal 7 and the target optimization signal 8; the obtained target optimization signal set includes: target optimization signal 1, second intermediate signal 1, target optimization signal 2, second intermediate signal 2, target optimization signal 3, second intermediate signal 3, target optimization signal 4, second intermediate signal 4, target optimization signal 5, second intermediate signal 5, target optimization signal 6, second intermediate signal 6, target optimization signal 7, second intermediate signal 7, target optimization signal 8.

[0128] It should be noted that this application does not limit the specific implementation method of the preset filling method. The preset filling method can be the mean method, interpolation method, etc. The technicians can set the implementation method of the preset filling method according to their needs.

[0129] In step 903: Shannon supersampling is performed on the target optimization signal set to obtain an initial target signal.

[0130] For example: the target optimization signal set includes: target optimization signal 1, second intermediate signal 1, target optimization signal 2, second intermediate signal 2, target optimization signal 3, second intermediate signal 3, target optimization signal 4, second intermediate signal 4, target optimization signal 5, second intermediate signal 5, target optimization signal 6, second intermediate signal 6, target optimization signal 7, second intermediate signal 7, target optimization signal 8; the target optimization signal set is subjected to shannon supersampling, and the initial target signal obtained includes: the target optimization signal set includes: second intermediate signal 1, target optimization signal 2, target optimization signal 3, second intermediate signal 3, target optimization signal 4, second intermediate signal 4, second intermediate signal 5, target optimization signal 6, second intermediate signal 6, target optimization signal 7, target optimization signal 8.

[0131] In step 904: the initial target signal is integrated to obtain the target signal.

[0132] For example, the initial target signal includes: the target optimization signal set includes: second intermediate signal 1, target optimization signal 2, target optimization signal 3, second intermediate signal 3, target optimization signal 4, second intermediate signal 4, second intermediate signal 5, target optimization signal 6, second intermediate signal 6, target optimization signal 7, and target optimization signal 8. The initial target signal is integrated to obtain the target signal.

[0133] In some possible embodiments, after determining the target signal corresponding to the second period, in order to ensure the accuracy of the determination of the collision event, when determining whether a collision event occurs in the second period based on the target signal, the data collected by the vehicle based on the radar and the on-board camera can be integrated for judgment. Specifically, it can be implemented as follows: obtain the image data collected by the on-board camera in the second period, and obtain the radar data collected by the radar in the second period; determine whether a collision event occurs in the second period based on the image data, radar data and the target signal.

[0134] For example: if a collision event is determined based on image data, a collision event is determined based on radar data, and a collision event is determined based on the target signal, then it is determined that a collision event has occurred within the second cycle; if it is determined based on image data that no collision event has occurred, a collision event is determined based on radar data, and a collision event is determined based on the target signal, then it is determined that no collision event has occurred within the second cycle, and it can be determined that a false touch event has occurred on the radar.

[0135] In some possible embodiments, if all the data collected by each vehicle and the records of the collision events are stored locally, it will place a burden on the vehicle's storage. Therefore, after each vehicle determines the target signal or collision event, it can upload the target signal and the data corresponding to the collision event to the cloud for storage by the cloud. Alternatively, a memory can be installed locally in the vehicle to store the target signal and the data corresponding to the collision time. It should be noted that the memory in the embodiments of the present application can be, for example, a cache system, hard disk storage, memory storage, etc. This application does not limit the specific storage method, and technicians can set it according to their needs.

[0136] In some possible embodiments, to screen for outliers in the target signal, each vehicle reports the target signal and associated data collected to the cloud after determining it. The cloud then constructs a large database of target signals based on the data reported by each vehicle. The database stores the normal ranges for target signals under various operating conditions. Once the large database is constructed, the target signal can be determined to be normal based on the vehicle's reported target signal and the large database. The determination result is then sent to the vehicle, allowing users to promptly understand the vehicle's status.

[0137] Based on the same inventive concept, the present application further provides a collision event determination device 1000, as shown in FIG10 , comprising:

[0138] The target optimization signal determination module 10001 is configured to perform, for each gyroscope signal and the original acceleration signal acquired in each first period: performing weighted fitting processing on the original acceleration signal acquired in the first period based on the gyroscope signal acquired in the first period to obtain a first optimization signal; performing filtering processing on the first optimization signal to obtain a second optimization signal; and performing weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first period to obtain a target optimization signal.

[0139] a target signal determination module 10002, configured to obtain a target signal corresponding to a second period based on a target optimization signal corresponding to each first period; wherein the second period includes a preset number of first periods;

[0140] The collision event determination module 10003 is configured to determine whether a collision event occurs within the second period based on the destination signal.

[0141] In some possible embodiments, the target optimization signal determination module 10001 performs weighted fitting processing on the acceleration original signal obtained in the first period based on the gyroscope signal obtained in the first period to obtain the first optimization signal, specifically for:

[0142] constructing a first curve based on the gyroscope signal acquired in the first period;

[0143] constructing a second curve based on the original acceleration signal acquired during the first period;

[0144] The first curve and the second curve are weighted-fitted according to a first preset weight corresponding to the first curve and a second preset weight corresponding to the second curve to obtain the first optimized signal.

[0145] In some possible embodiments, when the target optimization signal determination module 10001 performs filtering processing on the first optimization signal to obtain the second optimization signal, it is specifically configured to:

[0146] Simplify the Kalman filter to obtain the target filter;

[0147] The first optimized signal is filtered using the target filter to obtain the second optimized signal.

[0148] In some possible embodiments, the target optimization signal determination module 10001 performs simplified processing on the Kalman filter to obtain the target filter, specifically for:

[0149] The preset weights are used to replace the weights in the Kalman filter, where the weights in the Kalman filter are weights updated in real time according to the noise of the original acceleration signal.

[0150] In some possible embodiments, the target optimization signal determination module 10001 performs weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first period to obtain the target optimization signal, specifically for:

[0151] constructing a first curve based on the gyroscope signal acquired in the first period;

[0152] constructing a third curve based on the first optimization signal;

[0153] performing weighted fitting processing on the first curve and the third curve according to a first preset weight corresponding to the first curve and a third preset weight corresponding to the third curve to obtain a third optimized signal;

[0154] The third optimization signal is sampled to obtain the target optimization signal.

[0155] In some possible embodiments, when the target optimization signal determination module 10001 performs sampling processing on the third optimization signal to obtain the target optimization signal, it is specifically configured to:

[0156] Based on the timing of each third optimized signal, a preset filling method is used to fill a first intermediate point between every two third optimized signals to obtain a third optimized signal set;

[0157] Performing Shannon supersampling on the third optimized signal set to obtain a target third optimized signal;

[0158] Integrate the target third optimization signal to obtain the target optimization signal.

[0159] In some possible embodiments, when the target signal determination module 10002 obtains the target signal corresponding to the second period based on the target optimization signal corresponding to each first period, it is specifically configured to:

[0160] Sorting the target optimization signals according to the time sequence of each target optimization signal to obtain a target optimization signal sequence;

[0161] In the target optimization signal sequence, a preset filling method is used to fill a second intermediate signal between every two target optimization signals to obtain a target optimization signal set;

[0162] Performing Shannon supersampling on the target optimization signal set to obtain an initial target signal;

[0163] The initial target signal is integrated to obtain the target signal.

[0164] Corresponding to the above embodiments, the present application also provides an electronic device. Figure 11 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 1100 may include: a processor 1101, a memory 1102, and a communication unit 1103. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present invention. It can be a bus structure or a star structure, and can also include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0165] The communication unit 1103 is configured to establish a communication channel so that the electronic device can communicate with other devices, receive user data sent by other devices, or send user data to other devices.

[0166] The processor 1101 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 1102, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 1101 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0167] The memory 1102 is used to store the execution instructions of the processor 1101. The memory 1102 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0168] When the execution instructions in the memory 1102 are executed by the processor 1101 , the electronic device 1100 is enabled to execute part or all of the steps in the embodiment shown in FIG. 7 .

[0169] In a specific implementation, the present invention further provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment of the calling method provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0170] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0171] In this specification, reference can be made to the same or similar parts between the various embodiments. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

Claims

1. A method for determining a collision event, characterized in that: The method comprises: For the gyroscope signal and the original acceleration signal obtained in each first period, the following steps are performed: based on the gyroscope signal obtained in the first period, a weighted fitting process is performed on the original acceleration signal obtained in the first period to obtain a first optimized signal; the first optimized signal is filtered to obtain a second optimized signal; based on the gyroscope signal obtained in the first period, a weighted fitting process is performed on the second optimized signal to obtain a target optimized signal; Obtaining a target signal corresponding to a second cycle based on a target optimization signal corresponding to each first cycle; wherein the second cycle includes a preset number of first cycles; Determine whether a collision event occurs within the second period based on the destination signal.

2. The method according to claim 1, characterized in that The step of performing weighted fitting processing on the original acceleration signal acquired in the first period based on the gyroscope signal acquired in the first period to obtain a first optimized signal includes: constructing a first curve based on the gyroscope signal acquired in the first period; constructing a second curve based on the original acceleration signal acquired in the first period; The first curve and the second curve are weighted-fitted according to a first preset weight corresponding to the first curve and a second preset weight corresponding to the second curve to obtain the first optimization signal.

3. The method according to claim 1, characterized in that The filtering the first optimized signal to obtain a second optimized signal includes: Simplify the Kalman filter to obtain the target filter; The first optimized signal is filtered using the target filter to obtain the second optimized signal.

4. The method according to claim 3, characterized in that The Kalman filter is simplified to obtain a target filter, including: The preset weights are used to replace the weights in the Kalman filter, where the weights in the Kalman filter are weights updated in real time according to the noise of the original acceleration signal.

5. The method according to claim 1, characterized in that The step of performing weighted fitting processing on the second optimization signal based on the gyroscope signal acquired in the first cycle to obtain a target optimization signal includes: constructing a first curve based on the gyroscope signal acquired in the first period; constructing a third curve based on the first optimization signal; Performing weighted fitting processing on the first curve and the third curve according to a first preset weight corresponding to the first curve and a third preset weight corresponding to the third curve to obtain a third optimized signal; The third optimization signal is sampled to obtain the target optimization signal.

6. The method according to claim 5, characterized in that The sampling process of the third optimization signal to obtain the target optimization signal includes: Based on the timing of each third optimized signal, a preset filling method is used to fill the first intermediate point between every two third optimized signals to obtain a third optimized signal set; Performing Shannon supersampling on the third optimized signal set to obtain a target third optimized signal; The target third optimization signal is integrated to obtain the target optimization signal.

7. The method according to claim 1, characterized in that The obtaining of the target signal corresponding to the second cycle based on the target optimization signal corresponding to each first cycle includes: Sorting the target optimization signals according to the time sequence of each target optimization signal to obtain a target optimization signal sequence; In the target optimization signal sequence, a second intermediate signal is filled between every two target optimization signals using a preset filling method to obtain a target optimization signal set; Performing Shannon supersampling on the target optimization signal set to obtain an initial target signal; The initial target signal is integrated to obtain the target signal.

8. A collision event determination device, characterized in that: The device comprises: The target optimization signal determination module is used to perform, for each first cycle acquired gyroscope signal and acceleration original signal: based on the gyroscope signal acquired in the first cycle, the acceleration original signal acquired in the first cycle is optimized; The first optimized signal is subjected to weighted fitting processing to obtain a first optimized signal; the first optimized signal is subjected to filtering processing to obtain a second optimized signal; the second optimized signal is subjected to weighted fitting processing based on the gyroscope signal obtained in the first period to obtain a target optimized signal; A target signal determination module, configured to obtain a target signal corresponding to a second cycle based on a target optimization signal corresponding to each first cycle; wherein the second cycle includes a preset number of first cycles; A collision event determination module is used to determine whether a collision event occurs within the second period based on the destination signal.

9. An electronic device, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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