CN112677911A - External airbag triggering method and device and electronic equipment - Google Patents
External airbag triggering method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a triggering method and device of an external airbag and electronic equipment, wherein the method comprises the following steps: acquiring vehicle data acquired by various sensors on a vehicle, and determining corresponding judgment parameters according to the vehicle data of the sensors; comprehensively determining a decision value of the vehicle according to sub-parameters of the plurality of judgment parameters; and judging whether a collision risk exists between the vehicle and the target object according to the decision value, and generating an instruction for triggering the external air bag when the collision risk exists. According to the external airbag triggering method, the external airbag triggering device and the electronic equipment provided by the embodiment of the invention, the judgment parameters are respectively determined through the various sensors, and then the decision value is comprehensively determined based on the sub-parameters of the judgment parameters, so that the decision value is more accurate than a single sub-parameter, whether the external airbag is triggered can be more accurately judged based on the decision value, and therefore, false triggering is effectively avoided.
Description
Technical Field
The invention relates to the technical field of external airbags, in particular to a triggering method and device of an external airbag, electronic equipment and a computer-readable storage medium.
Background
Along with the increase of automobiles running on the road, the probability of rubbing and collision accidents among the automobiles, the automobiles and pedestrians or two-wheeled battery cars is correspondingly increased. In the aspect of active safety, front collision is mainly avoided by braking in advance; in the aspect of passive safety, can be in the front of the car outside, external gasbag of side-mounting, can promptly pop out external gasbag and aerify the inflation after the emergence collision to cushion the impact, avoid causing bigger injury.
The existing air bag triggering mode in the market at present mainly realizes mechanical triggering through a collision sensor; the external airbag is not identical to the internal airbag in the conventional vehicle. If the external air bag is ejected after the collision occurs, the buffer effect is poor; when the automobile slowly collides with the pedestrian, the automobile cannot hurt the pedestrian, but the air bag which is quickly popped up can quickly pop up the pedestrian, so that the pedestrian is more injured.
Furthermore, predictive triggering methods exist, in which an external airbag is ejected in advance when a collision risk is predicted for the vehicle. However, the predictive triggering may cause erroneous judgment, and since the airbag is generally disposable, the erroneous judgment is costly, and therefore the possibility of erroneous judgment needs to be reduced as much as possible.
Disclosure of Invention
In order to solve the existing technical problems, embodiments of the present invention provide a method and an apparatus for triggering an external airbag, an electronic device, and a computer-readable storage medium.
In a first aspect, an embodiment of the present invention provides a method for triggering an external airbag, including:
the method comprises the steps of obtaining vehicle data collected by various sensors on a vehicle, and determining corresponding judgment parameters according to the vehicle data of the sensors, wherein the judgment parameters comprise: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object;
comprehensively determining a decision value of the vehicle according to sub-parameters of the plurality of judgment parameters;
and judging whether a collision risk exists between the vehicle and the target object according to the decision value, and generating an instruction for triggering the external air bag when the collision risk exists.
In a second aspect, an embodiment of the present invention further provides a triggering device for an external airbag, including:
the acquisition module is used for acquiring vehicle data acquired by various sensors on a vehicle and determining corresponding judgment parameters according to the vehicle data of the sensors, wherein the judgment parameters comprise: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object;
the processing module is used for comprehensively determining a decision value of the vehicle according to sub-parameters of the plurality of judgment parameters;
and the judging module is used for judging whether a collision risk exists between the vehicle and the target object according to the decision value and generating an instruction for triggering the external air bag when the collision risk exists.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the steps in the method for triggering an external airbag described in any one of the above are implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for triggering an external airbag described in any one of the above.
According to the external airbag triggering method, the external airbag triggering device, the electronic equipment and the computer readable storage medium, the judgment parameters are respectively determined through the various sensors, then the decision value is comprehensively determined based on the sub-parameters of the judgment parameters, the decision value can be more accurate than a single sub-parameter, whether the external airbag is triggered or not can be more accurately judged based on the decision value, and therefore false triggering is effectively avoided. If a larger collision risk exists, the external airbag can be opened in advance, so that the external airbag can play a role in buffering even if the vehicle collides with the target object, and the vehicle and the target object are effectively protected.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present invention, the drawings required to be used in the embodiments or the background art of the present invention will be described below.
Fig. 1 is a flowchart illustrating a triggering method of an external airbag according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram illustrating an external airbag triggering device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for executing a triggering method of an external airbag according to an embodiment of the present invention.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only Memory (ROM), an erasable programmable read-only Memory (EPROM), a Flash Memory, an optical fiber, a compact disc read-only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
The computer program code embodied on the computer readable storage medium may be transmitted using any appropriate medium, including: wireless, wire, fiber optic cable, Radio Frequency (RF), or any suitable combination thereof.
Computer program code for carrying out operations for embodiments of the present invention may be written in assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages, including an object oriented programming language, such as: java, Smalltalk, C + +, and also include conventional procedural programming languages, such as: c or a similar programming language. The computer program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be over any of a variety of networks, including: a Local Area Network (LAN) or a Wide Area Network (WAN), which may be connected to the user's computer, may be connected to an external computer.
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
Fig. 1 shows a flowchart of a triggering method for an external airbag according to an embodiment of the present invention. The method is performed by a vehicle having an external airbag, as shown in fig. 1, and comprises:
step 101: acquiring vehicle data acquired by various sensors on a vehicle, and determining corresponding judgment parameters according to the vehicle data of the sensors, wherein the judgment parameters comprise: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object.
In the embodiment of the invention, the external air bag is arranged on the outer surface of the vehicle, and various sensors are directly or indirectly arranged on the vehicle. In the present embodiment, the "sensor" is a device in a broad sense as long as the device can determine the determination parameter of the vehicle. For example, the sensor may be a Lidar (laser radar), a camera, a speed sensor, etc. directly mounted on the vehicle, or may be a device capable of obtaining the determination parameter through a network, and the device may be regarded as being indirectly disposed on the vehicle, such as V2V (vehicle-to-vehicle communication), and the vehicle may directly communicate with other running vehicles around through the network by means of V2V, so as to determine the position, speed, etc. of the other vehicles.
In the embodiment of the present invention, the determination parameter includes a parameter of the vehicle itself, such as a vehicle speed, a vehicle position, a vehicle acceleration, and the like, and may further include a parameter of the target object, such as a target object speed, a target object position, a target object acceleration, and the like, and may further include a relative parameter between the vehicle and the target object, such as a relative speed between the vehicle and the target object, a relative position between the vehicle and the target object (which may specifically include a relative distance and/or a relative direction), and the like. The parameters of the vehicle and the parameters of the target object are also referred to as absolute parameters. In the present embodiment, the "object" is something other than the outside of the vehicle, such as another vehicle, a pedestrian, an external fixture (e.g., a railing, a building, etc.), and in an abnormal situation, a collision can occur between the vehicle and the object.
In the embodiment of the invention, each sensor can acquire data related to the vehicle, namely vehicle data, including data of the vehicle and data of an external target object, and corresponding judgment parameters can be determined according to the vehicle data. After part of sensors acquire vehicle data, judgment parameters can be obtained only by processing; for example, Lidar collects point cloud data, and the relative position between the vehicle and the target object can be determined after the point cloud data is processed. And the vehicle data collected by part of the sensors is the judgment parameters, for example, the vehicle data collected by the speed sensor is the vehicle speed. Alternatively, part of the vehicle data collected by the sensors is the determination parameter a, and the determination parameter B may be obtained from other vehicle data, or the other determination parameter C may be determined from the obtained determination parameter a. For example, vehicle data acquired by a GPS (global positioning system) is a vehicle position, that is, the vehicle data can be directly used as a determination parameter at this time; the vehicle speed may also be determined based on the change in the vehicle position. The present embodiment does not limit the process of determining the determination parameter from the vehicle data.
Furthermore, different sensors may determine the same or different decision parameters, i.e. the sub-parameters determined by different sensors are also different. Specifically, the sensor may include Lidar, a camera, a millimeter wave radar, an external ultrasonic wave (an ultrasonic sensor additionally installed), an external GPS (such as a smartphone used by a driver, etc.), an IMU (inertial measurement unit), an internal ultrasonic wave (an ultrasonic sensor provided in the vehicle itself and readable through an OBD interface), a wheel speed meter, an internal GPS, V2V, and the like. The determination parameters that can be determined by some sensors are specifically shown in table 1 below:
TABLE 1
Step 102: and comprehensively determining the decision value of the vehicle according to the sub-parameters of the plurality of judgment parameters.
In the embodiment of the invention, each sensor can determine the judgment parameter containing one or more sub-parameters, and the corresponding decision value can be determined according to each sub-parameter. In the embodiment of the invention, the decision value is also a sub-parameter in nature, and can be used for making a decision, namely judging whether the external air bag needs to be opened or not, as long as the decision value has higher precision. Wherein a decision value is determined based on the same sub-parameter. For example, Lidar, a camera and a millimeter wave radar can all determine a sub-parameter "relative speed between the vehicle and the target object", and at this time, the "relative speed between the vehicle and the target object" used for decision making, that is, a decision value, can be determined according to the "relative speed between the vehicle and the target object" determined by the three. The decision value is determined synthetically by sub-parameters of a plurality of decision parameters, which may be made more accurate than a single sub-parameter.
Step 103: and judging whether a collision risk exists between the vehicle and the target object according to the decision value, and generating an instruction for triggering the external air bag when the collision risk exists.
In the embodiment of the invention, after the decision value is determined, whether the collision risk exists between the vehicle and the target object can be judged based on the decision value, and if the collision risk exists, an instruction for triggering the external airbag is generated, so that the external airbag can be opened in advance, and the external airbag can play a role in buffering even if the vehicle collides with the target object, thereby effectively protecting the vehicle. Meanwhile, the collision risk is that the vehicle does not collide, and the external air bag is triggered in advance, so that the vehicle can also play a role in protecting the target object. The decision value is also a sub-parameter in nature, and it is a mature technology in the prior art to determine whether there is a collision risk between the vehicle and the target object according to multiple decision values (sub-parameters such as speed and position), which is not described in detail in this embodiment.
According to the triggering method of the external airbag provided by the embodiment of the invention, the judgment parameters are respectively determined by the various sensors, and then the decision value is comprehensively determined based on the sub-parameters of the plurality of judgment parameters, so that the decision value is more accurate than a single sub-parameter, whether the external airbag is triggered can be more accurately judged based on the decision value, and the false triggering is effectively avoided. If a larger collision risk exists, the external airbag can be opened in advance, so that the external airbag can play a role in buffering even if the vehicle collides with the target object, and the vehicle and the target object are effectively protected.
On the basis of the above embodiments, the embodiments of the present invention need to determine multiple decision values, and each decision value is obtained by a weighting manner. Specifically, the above-mentioned step 102 "comprehensively determining the decision value of the vehicle based on the sub-parameters of the plurality of determination parameters" includes the steps of A1-A2.
Step A1: determining a target sub-parameter and a target sensor corresponding to the target sub-parameter; the target sub-parameter is a sub-parameter, the target sensor is a sensor capable of determining the target sub-parameter according to the collected vehicle data, and the number of the target sensors is multiple.
In the embodiment of the present invention, although one sensor may determine only one sub-parameter, all sensors may determine multiple sub-parameters as a whole, and each sub-parameter may correspond to one decision value. In this embodiment, each sub-parameter is used as a target sub-parameter, and not all sensors can determine the target sub-parameter, in this embodiment, a sensor corresponding to the target sub-parameter is referred to as a target sensor, that is, a sensor capable of determining the target sub-parameter according to the collected vehicle data is referred to as a target sensor. For example, for the sub-parameter "relative speed between the vehicle and the target object", Lidar, a camera, a millimeter wave radar, and the like may determine the sub-parameter, and thus the three sensors are all target sensors of the sub-parameter. Meanwhile, in order to accurately calculate the decision value, the number of the target sensors corresponding to each target sub-parameter is multiple. In addition, each sub-parameter can be used as a target sub-parameter, so that the target sensor corresponding to each sub-parameter can be determined respectively.
Optionally, the step a1 of "determining the target sub-parameter" includes:
step A11: a plurality of vehicle data sampled by the target sensor are acquired.
Step A12: and sequencing the vehicle data according to the sampling time of the vehicle data to generate a data queue which contains a plurality of vehicle data and has a fixed length, wherein the data queue is used for discarding the last data in the data queue and arranging the new vehicle data at the first of the data queue when the new vehicle data is acquired.
Step A13: and determining the corresponding target sub-parameters according to the average value of the data queue.
In the embodiment of the invention, as the sensor can work in a high sampling rate mode, a large amount of data can be collected within one second, and the proper target sub-parameters are determined by establishing a data queue. Specifically, a plurality of vehicle data newly collected by the target sensor are sorted to generate a queue, i.e., a data queue, containing the plurality of vehicle data and having a fixed length, where the data queue may be specifically in the form of a circular array. When the latest vehicle data (namely new vehicle data) is collected again, the latest vehicle data is put at the head of the queue, and the data at the tail of the queue is deleted. Wherein, the data of the last bit in the data queue is the oldest, and the data of the first bit in the data queue is the newest. For example, k pieces of vehicle data are arranged according to the sequence of sampling time, and the generated data queues are dk, dk-1, …, d2 and d1, wherein the sampling time of dk is latest (namely the data d1 is just sampled), and the sampling time of d1 is oldest; when data dk +1 is sampled again, then the oldest d1 is deleted and dk +1 is added to the head of the queue, the data queue at this time is: dk +1, dk-1, …, d3, d2, which still contain k data, i.e. the data queue is always k in length. The target sub-parameters are determined through the data queue with the fixed length, sudden change of vehicle data obtained through sampling can be avoided, errors caused by sampling can be reduced, and accuracy is further improved. It should be noted that the above steps a11-a13 are preferably applied to a sensor capable of determining sub-parameters at high frequency, for example, a sensor having a high sampling frequency and capable of directly using vehicle data as a corresponding determination parameter.
Step A2: and determining the weight of each target sub-parameter, and determining a decision value of the vehicle corresponding to the target sub-parameter in a weighted mode according to the target sub-parameter determined by each target sensor and the corresponding weight.
In the embodiment of the invention, the weight is distributed to each target sub-parameter, so that the decision value corresponding to the target sub-parameter can be calculated according to the specific numerical value of the target sub-parameter and the corresponding weight. For each target sub-parameter, the above-mentioned step A2 is performed, that is, each target sub-parameter pair is determinedThe corresponding decision value. Specifically, if a decision value of a target sub-parameter is required to be calculated according to the target sub-parameters obtained by n target sensors, the target sub-parameter and the weight of the ith target sensor are xiAnd wiAt this time, the decision value P of the target sub-parameter is:
P＝w1x1+w2x2+…+wnxn。
for example, the vehicle speed x obtained from 3 target sensors1,x2,x3To calculate a more accurate value (i.e., decision value) of the vehicle speed, the decision value P for the vehicle speed is P-w1x1+w2x2+w3x3。
Optionally, the step a2 of "determining the weight of each item sub-parameter" includes:
step A21: and determining the current error of the target sensor according to the attribute parameters of the target sensor and the current environmental parameters.
Step A22: and determining the weight of the target sub-parameter according to the current error, wherein the weight and the current error are in a negative correlation relationship.
In the embodiment of the invention, the sensor has errors in data acquisition, and the errors can be reduced by sampling for multiple times, averaging and the like. However, the method provided by this embodiment needs to have real-time performance, and the multi-sampling mode will reduce the real-time performance, so that the method is not suitable for the multi-sampling mode when the sensor cannot determine the sub-parameters at high frequency. The weight of each target sub-parameter is determined by the error, so that the influence of the error on the finally calculated decision value is reduced. The conventional error is generally expressed by ± and the embodiment only relates to the numerical value of the error and does not pay attention to the sign of the error. Alternatively, the error may specifically be a median error.
Specifically, the error of the sensor is generally constant, and the error is only related to the attribute parameters of the sensor, for example, the error of the wheel speed meter can be regarded as constant; however, errors of some sensors are related to current environmental parameters, for example, in foggy days, the measurement result of Lidar has large errors, and even data cannot be collected. Therefore, the present embodiment determines the current error by the attribute parameter and the current environmental parameter. Wherein the current error may be determined at intervals, such as 10 minutes, to avoid large variations in the current error of the sensor. The attribute parameters are intrinsic parameters of the sensor, such as error drift, temperature vibration sensitivity, and the like of the sensor, and the current environmental parameters specifically include: GPS signal intensity, light intensity, rain and fog environment, shelter, etc.
In the embodiment of the invention, since the target sub-parameter is determined by the corresponding target sensor, the current error of the target sensor can be used as the current error of the corresponding target sub-parameter. Also, because each target sensor may determine one or more target sub-parameters, the current error of the target sensor may be taken as the current error of each target sub-parameter it may determine. For example, Lidar may determine two sub-parameters: the current error of the two sub-parameters can be the current error of Lidar. In addition, the step a22 "determining the weight of the target sub-parameter according to the current error" specifically includes:
step A221: determining the current error m of the ith target sensori。
Step A222: determining the weight of the target sub-parameter corresponding to each target sensor according to the current errors of all the target sensors, and determining the weight w of the target sub-parameter corresponding to the ith target sensoriComprises the following steps:
in the embodiment of the invention, the corresponding decision value can be determined according to the target sub-parameters of the plurality of target sensors. As described above, if the decision value of the target sub-parameter needs to be calculated according to the target sub-parameters obtained by the n target sensors, the target sub-parameter and the weight of the ith target sensor are x respectivelyiAnd wiAt this time, the eyeThe decision value P of the scalar parameter is:
P＝w1x1+w2x2+…+wnxn。
let the current error of the ith target sensor be miThen the current error of the corresponding target sub-parameter is also miMeanwhile, if the error of the decision value P is pm, the error can be known according to the error propagation law
Wherein each current error miIs deterministic, it is necessary to assign each weight w with the minimum error pm of the decision value P guaranteediThat is to say need to make
And minimum.
Since all weights satisfy the weight condition, i.e. the sum of the weights is 1,
in this embodiment, the lagrangian function F is constructed by using the weight condition as a constraint condition:
let function F pair wiAnd the first partial derivative of λ is zero, then:
solving the equation set to obtain:
That is to say that the first and second electrodes,
verified, the weight w at this timeiCorresponding to the minimum value of the error pm. Wherein the current error miThe larger the weight w isiThe smaller, i.e. the negative correlation between the weight and the current error. The present embodiment sets the weight of the target sub-parameter to
The finally determined error pm of the decision value P can be minimized, and the accuracy of the decision value P can be effectively improved.
In addition, the remaining decision values P can be performed according to the above steps A221-A222. Further, since the current error generally does not vary much, the current error may be predetermined at intervals, such as 10 minutes each, etc. After determining the current error, since which sub-parameter corresponds to which sensor is determined, i.e. the target sensor corresponding to a certain target sub-parameter is determined, the weight of each target sub-parameter can be determined in advance, and a plurality of target sub-parameters x are acquired according to different target sensorsiThen, the final decision value P can be determined according to a weighting method.
Optionally, before the step a22 "determining the weight of the target sub-parameter according to the current error", the method further includes:
step A23: if the target sub-parameter is a relative parameter, determining two absolute parameters corresponding to the target sub-parameter, determining a new relative parameter according to the two absolute parameters, taking the new relative parameter as a target sub-parameter, and allocating a target sensor for the new relative parameter; the relative parameters are: the relative speed between the vehicle and the target object or the relative position between the vehicle and the target object, the absolute parameters are: vehicle speed, vehicle position, object speed, or object position.
Step A24: determining new relative parameters based on the current error of two absolute parametersA current error of the number; current error m of new relative parameterrComprises the following steps:
wherein m isa1Is the current error of an absolute parameter, ma2Is the current error of another absolute parameter.
In the embodiment of the invention, if the target sub-parameter is a relative parameter, that is, the target sub-parameter is the relative speed between the vehicle and the target object or the relative position between the vehicle and the target object, although some sensors cannot directly calculate the relative parameter, other parameters can determine an absolute parameter, and the corresponding relative parameter can be determined by the difference between the two absolute parameters. For example, the target sub-parameter is the relative speed between the vehicle and the target object, which cannot be directly obtained by the wheel speed meter and V2V, but the wheel speed meter can determine the vehicle speed, and the V2V can determine the target object speed, and then the relative speed between the vehicle and the target object can also be determined according to the vehicle speed determined by the wheel speed meter and the target object speed determined by V2V; or preferentially taking the absolute parameter as a target sub-parameter and determining a decision value of the absolute parameter; a new relative parameter is then calculated based on the decision values of the two absolute parameters. The new relative parameter is a parameter with the same property as the target sub-parameter, such as the relative speed between the vehicle and the target, but the specific value may be different. Therefore, the new relative parameter can be used as a target sub-parameter, and the decision value corresponding to the target sub-parameter is further determined.
In addition, the new relative parameter may also correspond to a target sensor, which is virtual only for convenience of description later, for example, when steps a221-a222 are performed later, the new relative parameter is also taken into account. At this time, since the new relative parameter is determined by two absolute parameters, and the new relative parameter is one of the absolute parameters — the other absolute parameter; therefore, based on the law of error propagation, if the current error of an absolute parameter is ma1The current error of another absolute parameter is ma2Then the current error m of the new relative parameterrComprises the following steps:
when step A221 is subsequently performed, the current error mrCurrent error m corresponding to one of the target sensorsi。
Optionally, the step a2 "determining the decision value corresponding to the target sub-parameter of the vehicle in a weighted manner according to the target sub-parameter determined by each target sensor and the corresponding weight" includes:
step A25: and sequencing the target sub-parameters, eliminating the maximum value and/or the minimum value, and taking the remaining target sub-parameters as effective target sub-parameters.
Step A26: determining a multiple M according to the precision of the weight, and calculating a decision value P of the vehicle corresponding to the target sub-parameter, and:
In the embodiment of the invention, as the target sub-parameters are determined by a plurality of target sensors, the jitter problem of a certain target sensor is inevitably caused by various reasons, so that the determined target sub-parameters are abnormal, such as too large or too small. The present embodiment avoids this problem by eliminating the maximum value and/or the minimum value in the target sub-parameter, and generally removes the maximum value and the minimum value. At this time, since the sum of the weights of the remaining effective target sub-parameters is less than 1, the weight of each effective target sub-parameter needs to be determined again; in the case that the processing speed is fast enough, the process of the above steps a221-a222 can be adopted to calculate the weight corresponding to each valid target sub-parameter. Or, the weight is directly determined again according to the original proportion. However, since the value of the weight is smaller than 1, there is a problem of accuracy itself, and if the weight (smaller than 1) is determined again and then the decision value P is calculated based on the weight, the accuracy of the weight itself is greatly affected. In this embodiment, the weights are not determined again, but the decision value P is directly calculated according to the original weights.
Specifically, the weight W of the ith valid target sub-parameter in this embodimentiThe weight w of the jth target sub-parameter corresponding to the abovejThe same; meanwhile, the multiple is determined based on the precision of the weight. Typically, the multiple M is the inverse of the precision of the weights. For example, the weight precision is 0.01 (i.e., weight W)iE.g., 0.21, 0.40, etc.), the multiple M is 1/0.01-100. At this time, the decision value P is directly calculated according to the following equation:
in this embodiment, the weight W isiThe weight of the target sub-parameter itself, i.e. the weight determined in step A22 or step A222, is obtained when the ith valid target sub-parameter X is obtainediThen, due to the existence of the multiple M, MW is enabledi(i∈[1,m]) The decision value P is an integer, the influence of the weight precision can be eliminated by calculating the decision value P according to the formula, and the error caused by re-determining the weight according to the original proportion can be avoided; therefore, even if the maximum value or the minimum value is deleted, the decision value P can be calculated on the basis of the original precision, and the precision of the decision value P can be ensured.
The method for triggering the external airbag according to the embodiment of the present invention is described above in detail, and the method may also be implemented by a corresponding device.
Fig. 2 shows a schematic structural diagram of a triggering device of an external airbag according to an embodiment of the present invention. As shown in fig. 2, the triggering device of the external airbag includes:
the acquiring module 21 is configured to acquire vehicle data acquired by various sensors on a vehicle, and determine corresponding determination parameters according to the vehicle data of the sensors, where the determination parameters include: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object;
a processing module 22, configured to comprehensively determine a decision value of the vehicle according to sub-parameters of the plurality of determination parameters;
and the judging module 23 is configured to judge whether there is a collision risk between the vehicle and the target object according to the decision value, and generate an instruction for triggering the external airbag when there is a collision risk.
On the basis of the above embodiment, the processing module 22 includes:
the determining unit is used for determining the target sub-parameters and the target sensors corresponding to the target sub-parameters; the target sub-parameter is one sub-parameter, the target sensor is a sensor capable of determining the target sub-parameter according to collected vehicle data, and the number of the target sensors is multiple;
and the weighting processing unit is used for determining the weight of each target sub-parameter, and determining the decision value of the vehicle corresponding to the target sub-parameter in a weighting mode according to the target sub-parameter determined by each target sensor and the corresponding weight.
On the basis of the above embodiment, the determining unit determines the target sub-parameter by:
acquiring a plurality of vehicle data sampled by the target sensor latest;
the vehicle data are sequenced according to the sampling time of the vehicle data, a data queue which contains a plurality of vehicle data and has a fixed length is generated, and the data queue is used for discarding the last data in the data queue and arranging the new vehicle data at the head of the data queue when new vehicle data are acquired;
and determining corresponding target sub-parameters according to the average value of the data queue.
On the basis of the foregoing embodiment, the determining, by the weighting processing unit, the weight of each of the target sub-parameters includes:
determining the current error of the target sensor according to the attribute parameters of the target sensor and the current environmental parameters;
and determining the weight of the target sub-parameter according to the current error, wherein the weight and the current error are in a negative correlation relationship.
On the basis of the foregoing embodiment, before the weighting processing unit determines the weight of the target sub-parameter according to the current error, the determining unit is further configured to:
if the target sub-parameter is a relative parameter, determining two absolute parameters corresponding to the target sub-parameter, determining a new relative parameter according to the two absolute parameters, taking the new relative parameter as the target sub-parameter, and allocating one target sensor to the new relative parameter; the relative parameters are as follows: the relative speed between the vehicle and the target object, or the relative position between the vehicle and the target object, and the absolute parameter is: vehicle speed, vehicle position, target speed, or target position;
determining the current error of the new relative parameter according to the current errors of the two absolute parameters; current error m of the new relative parameterrComprises the following steps:
wherein m isa1Is the current error of an absolute parameter, ma2Is the current error of another absolute parameter.
On the basis of the foregoing embodiment, the determining, by the weighting processing unit, the weight of the target sub-parameter according to the current error includes:
determining the current error m of the ith target sensori；
Determining the weight of the target sub-parameter corresponding to each target sensor according to the current errors of all the target sensors, wherein the weight w of the target sub-parameter corresponding to the ith target sensoriComprises the following steps:
on the basis of the above embodiment, the determining, by the weighting processing unit, a decision value of the vehicle corresponding to the target sub-parameter in a weighted manner according to the target sub-parameter determined by each target sensor and the corresponding weight includes:
sorting the target sub-parameters, eliminating the maximum value and/or the minimum value, and taking the rest target sub-parameters as effective target sub-parameters;
determining a multiple M according to the precision of the weight, calculating a decision value P of the vehicle corresponding to the target sub-parameter, and:
According to the triggering device of the external airbag, provided by the embodiment of the invention, the judgment parameters are respectively determined through the various sensors, and then the decision value is comprehensively determined based on the sub-parameters of the judgment parameters, so that the decision value is more accurate than a single sub-parameter, whether the external airbag is triggered can be more accurately judged based on the decision value, and the false triggering is effectively avoided. If a larger collision risk exists, the external airbag can be opened in advance, so that the external airbag can play a role in buffering even if the vehicle collides with the target object, and the vehicle and the target object are effectively protected. The target sub-parameters are determined through the data queue with the fixed length, sudden change of vehicle data obtained through sampling can be avoided, errors brought by sampling can be reduced, and accuracy is further improved. The weight w of the target sub-parameter will be determined according to the current erroriThe finally determined error pm of the decision value P can be minimized, and the accuracy of the decision value P can be effectively improved. By multiplying the weight by the multiple M, even if the maximum value or the minimum value is deleted, the error caused by re-determining the weight according to the original proportion can be avoided, and the calculation can be carried out on the basis of the original precisionAnd the decision value P can ensure the precision of the decision value P.
In addition, an embodiment of the present invention further provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the external airbag triggering method embodiment are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Specifically, referring to fig. 3, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, wherein the computer program when executed by the processor 1120 implements the processes of the external airbag triggering method embodiment.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus, and memory controller, a peripheral bus, an Accelerated Graphics Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include: an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA), a Peripheral Component Interconnect (PCI) bus.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet (intranet), an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and combinations of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced Mobile Broadband (eMBB) system, a mass Machine Type Communication (mtc) system, an Ultra Reliable Low Latency Communication (urrllc) system, or the like.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), or Flash Memory.
The volatile memory includes: random Access Memory (RAM), which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory (Static RAM, SRAM), Dynamic random access memory (Dynamic RAM, DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct memory bus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media Player (Media Player), Browser (Browser), for implementing various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the external airbag triggering method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may be tangible devices that retain and store instructions for use by an instruction execution apparatus. The computer-readable storage medium includes: electronic memory devices, magnetic memory devices, optical memory devices, electromagnetic memory devices, semiconductor memory devices, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape cartridge storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanically encoded devices (e.g., punched cards or raised structures in a groove having instructions recorded thereon), or any other non-transmission medium useful for storing information that may be accessed by a computing device. As defined in embodiments of the present invention, the computer-readable storage medium does not include transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses traveling through a fiber optic cable), or electrical signals transmitted through a wire.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the embodiment of the invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be substantially or partially contributed by the prior art, or all or part of the technical solutions may be embodied in a software product stored in a storage medium and including instructions for causing a computer device (including a personal computer, a server, a data center, or other network devices) to execute all or part of the steps of the methods of the embodiments of the present invention. And the storage medium includes various media that can store the program code as listed in the foregoing.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A triggering method of an external airbag is characterized by comprising the following steps:
the method comprises the steps of obtaining vehicle data collected by various sensors on a vehicle, and determining corresponding judgment parameters according to the vehicle data of the sensors, wherein the judgment parameters comprise: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object;
comprehensively determining a decision value of the vehicle according to sub-parameters of the plurality of judgment parameters;
and judging whether a collision risk exists between the vehicle and the target object according to the decision value, and generating an instruction for triggering the external air bag when the collision risk exists.
2. The method of claim 1, wherein the determining a decision value for the vehicle based on a combination of sub-parameters of the plurality of decision parameters comprises:
determining a target sub-parameter and a target sensor corresponding to the target sub-parameter; the target sub-parameter is one sub-parameter, the target sensor is a sensor capable of determining the target sub-parameter according to collected vehicle data, and the number of the target sensors is multiple;
determining the weight of each target sub-parameter, and determining the decision value of the vehicle corresponding to the target sub-parameter in a weighted mode according to the target sub-parameter determined by each target sensor and the corresponding weight.
3. The method of claim 2, wherein the determining target sub-parameters comprises:
acquiring a plurality of vehicle data sampled by the target sensor latest;
the vehicle data are sequenced according to the sampling time of the vehicle data, a data queue which contains a plurality of vehicle data and has a fixed length is generated, and the data queue is used for discarding the last data in the data queue and arranging the new vehicle data at the head of the data queue when new vehicle data are acquired;
and determining corresponding target sub-parameters according to the average value of the data queue.
4. The method of claim 2, wherein the determining the weight of each of the target sub-parameters comprises:
determining the current error of the target sensor according to the attribute parameters of the target sensor and the current environmental parameters;
and determining the weight of the target sub-parameter according to the current error, wherein the weight and the current error are in a negative correlation relationship.
5. The method of claim 4, further comprising, prior to said determining the weight of the target sub-parameter based on the current error:
if the target sub-parameter is a relative parameter, determining two absolute parameters corresponding to the target sub-parameter, determining a new relative parameter according to the two absolute parameters, taking the new relative parameter as the target sub-parameter, and allocating one target sensor to the new relative parameter; the relative parameters are as follows: the relative speed between the vehicle and the target object, or the relative position between the vehicle and the target object, and the absolute parameter is: vehicle speed, vehicle position, target speed, or target position;
determining the current error of the new relative parameter according to the current errors of the two absolute parameters; current error m of the new relative parameterrComprises the following steps:
wherein m isa1Is the current error of an absolute parameter, ma2Is the current error of another absolute parameter.
6. The method of claim 4, wherein the determining the weight of the target sub-parameter according to the current error comprises:
determining the current error m of the ith target sensori；
Determining the weight of the target sub-parameter corresponding to each target sensor according to the current errors of all the target sensors, wherein the weight w of the target sub-parameter corresponding to the ith target sensoriComprises the following steps:
7. the method according to any one of claims 2-6, wherein determining the decision value of the vehicle corresponding to the target sub-parameter in a weighted manner according to the target sub-parameter determined by each target sensor and the corresponding weight comprises:
sorting the target sub-parameters, eliminating the maximum value and/or the minimum value, and taking the rest target sub-parameters as effective target sub-parameters;
determining a multiple M according to the precision of the weight, calculating a decision value P of the vehicle corresponding to the target sub-parameter, and:
8. An external airbag triggering device, comprising:
the acquisition module is used for acquiring vehicle data acquired by various sensors on a vehicle and determining corresponding judgment parameters according to the vehicle data of the sensors, wherein the judgment parameters comprise: one or more sub-parameters of vehicle speed, vehicle position, vehicle acceleration, object speed, object position, object acceleration, relative speed between vehicle and object, relative position between vehicle and object;
the processing module is used for comprehensively determining a decision value of the vehicle according to sub-parameters of the plurality of judgment parameters;
and the judging module is used for judging whether a collision risk exists between the vehicle and the target object according to the decision value and generating an instruction for triggering the external air bag when the collision risk exists.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, wherein the computer program, when executed by the processor, implements the steps of the method for triggering an external airbag according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for triggering an external airbag according to one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112660054A (en) * | 2021-01-05 | 2021-04-16 | 北京家人智能科技有限公司 | Method and device for triggering external airbags in grading manner and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2284165Y (en) * | 1996-10-31 | 1998-06-17 | 北京市赫达汽车安全技术公司 | Intelligent control device of automotive safety air sac |
| US20060208169A1 (en) * | 1992-05-05 | 2006-09-21 | Breed David S | Vehicular restraint system control system and method using multiple optical imagers |
| CN101837782A (en) * | 2009-01-26 | 2010-09-22 | 通用汽车环球科技运作公司 | Be used to collide the multiple goal Fusion Module of preparation system |
| CN203198901U (en) * | 2013-02-01 | 2013-09-18 | 康金腾翔 | Proactive automobile externally-arranged anti-collision airbag control system |
| CN103863233A (en) * | 2012-12-10 | 2014-06-18 | 现代自动车株式会社 | External airbag deployment method and system |
| CN104842923A (en) * | 2015-05-20 | 2015-08-19 | 浙江吉利汽车研究院有限公司 | Active protection system and method thereof for preventing secondary damage of pedestrians |
| CN108197652A (en) * | 2018-01-02 | 2018-06-22 | 百度在线网络技术（北京）有限公司 | For generating the method and apparatus of information |
-
2021
- 2021-01-05 CN CN202110008465.1A patent/CN112677911A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060208169A1 (en) * | 1992-05-05 | 2006-09-21 | Breed David S | Vehicular restraint system control system and method using multiple optical imagers |
| CN2284165Y (en) * | 1996-10-31 | 1998-06-17 | 北京市赫达汽车安全技术公司 | Intelligent control device of automotive safety air sac |
| CN101837782A (en) * | 2009-01-26 | 2010-09-22 | 通用汽车环球科技运作公司 | Be used to collide the multiple goal Fusion Module of preparation system |
| CN103863233A (en) * | 2012-12-10 | 2014-06-18 | 现代自动车株式会社 | External airbag deployment method and system |
| CN203198901U (en) * | 2013-02-01 | 2013-09-18 | 康金腾翔 | Proactive automobile externally-arranged anti-collision airbag control system |
| CN104842923A (en) * | 2015-05-20 | 2015-08-19 | 浙江吉利汽车研究院有限公司 | Active protection system and method thereof for preventing secondary damage of pedestrians |
| CN108197652A (en) * | 2018-01-02 | 2018-06-22 | 百度在线网络技术（北京）有限公司 | For generating the method and apparatus of information |
Non-Patent Citations (1)
| Title |
|---|
| 杨军佳，叶晨亮，冯少华，钱冰: "多传感器数据加权融合方法", 《传感器与微系统》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112660054A (en) * | 2021-01-05 | 2021-04-16 | 北京家人智能科技有限公司 | Method and device for triggering external airbags in grading manner and electronic equipment |
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