CN110659696A - Method and device for detecting driving safety - Google Patents
Method and device for detecting driving safety Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a method and a device for detecting driving safety. One embodiment of the method comprises: acquiring a vehicle running parameter group in real time, wherein a plurality of vehicle running parameters contained in the vehicle running parameter group have a correlation relationship; importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group, wherein the running parameter abnormity detection model is used for representing the corresponding relation between the vehicle running parameter group and the running state result information, and the running state result information comprises normal running and abnormal running; and sending out an alarm signal in response to the driving state result information being abnormal driving. This embodiment improves the driving safety of the vehicle.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a method and a device for detecting driving safety.
Background
The automobile expands the range of people going out, brings convenience to people going out and improves the quality of life of people. With the development and progress of science and technology, vehicles controlled by an intelligent system can acquire more driving information than automobiles driven by people, have higher safety, and become an important trend of automobile development in the future. The vehicle controlled by the intelligent system can acquire a plurality of data in the driving process of the vehicle, so that a driver can perform accurate driving operation in time according to the acquired data, and the driving safety of the vehicle is facilitated.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for detecting driving safety.
In a first aspect, an embodiment of the present disclosure provides a method for detecting driving safety, including: acquiring a vehicle running parameter group in real time, wherein a plurality of vehicle running parameters contained in the vehicle running parameter group have a correlation relationship; importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group, wherein the running parameter abnormity detection model is used for representing the corresponding relation between the vehicle running parameter group and the running state result information, and the running state result information comprises normal running and abnormal running; and sending out an alarm signal in response to the driving state result information being abnormal driving.
In some embodiments, the driving parameter abnormality detection model is obtained by training through the following steps: obtaining a plurality of sample vehicle running parameter groups and sample running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups; and training the driving parameter abnormality detection model by taking each of the plurality of sample vehicle driving parameter groups as an input and taking the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter groups as an output.
In some embodiments, the training of the driving parameter abnormality detection model using, as an input, each of the plurality of sample vehicle driving parameter sets and using, as an output, sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter sets includes: the following training steps are performed: sequentially inputting each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups into an initialized running parameter abnormity detection model to obtain predicted running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups, comparing the predicted running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups with the sample running state result information corresponding to the sample vehicle running parameter group to obtain the prediction accuracy of the initialized running parameter abnormity detection model, determining whether the prediction accuracy is greater than a preset accuracy threshold, and if so, taking the initialized running parameter abnormity detection model as a trained running parameter abnormity detection model.
In some embodiments, the training of the driving parameter abnormality detection model using, as an input, each of the plurality of sample vehicle driving parameter sets and using, as an output, sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter sets includes: and responding to the condition that the accuracy is not greater than the preset accuracy threshold, adjusting the parameters of the initialized driving parameter abnormity detection model, and continuing to execute the training step.
In some embodiments, the sample driving state result information is obtained by: obtaining a plurality of sample vehicle driving parameter sets of each sample driving state of at least one sample driving state, wherein the sample driving state comprises at least one of the following items: a sample straight line driving type, a sample curve driving type and a sample climbing driving type; for a sample driving state in the at least one sample driving state, counting parameter ranges of a plurality of sample vehicle driving parameter sets to obtain a reference parameter range corresponding to the sample driving state, wherein the reference parameter range comprises a safety parameter range of each sample vehicle driving parameter in the sample vehicle driving parameter sets; and setting sample driving state result information for the corresponding sample driving state through the reference parameter range.
In some embodiments, the type information of the sample driving state includes a safe driving state type and a dangerous driving state type, and the setting of the sample driving state result information for the corresponding sample driving state through the reference parameter range includes: and responding to the type information of the sample driving state as a safe driving state type, setting the result information of the sample driving state corresponding to the sample driving state as normal driving, and otherwise, setting as abnormal driving.
In a second aspect, an embodiment of the present disclosure provides an apparatus for detecting driving safety, the apparatus including: the vehicle driving parameter acquisition unit is configured to acquire a vehicle driving parameter group in real time, and a plurality of vehicle driving parameters contained in the vehicle driving parameter group have a correlation relationship; a driving state result information obtaining unit, configured to import the vehicle driving parameter group into a pre-trained driving parameter abnormality detection model, and obtain driving state result information corresponding to the vehicle driving parameter group, wherein the driving parameter abnormality detection model is used for representing a corresponding relationship between the vehicle driving parameter group and the driving state result information, and the driving state result information includes normal driving and abnormal driving; and the safety alarm unit is used for responding to the driving state result information as abnormal driving and is configured to send out an alarm signal.
In some embodiments, the apparatus includes a driving parameter abnormality detection model training unit configured to train a driving parameter abnormality detection model, the driving parameter abnormality detection model training unit includes: a sample acquisition subunit configured to acquire a plurality of sample vehicle travel parameter groups and sample travel state result information corresponding to each of the plurality of sample vehicle travel parameter groups; and a driving parameter abnormality detection model training subunit configured to train the driving parameter abnormality detection model by taking each of the plurality of sample vehicle driving parameter groups as an input and taking the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter groups as an output.
In some embodiments, the driving parameter abnormality detection model training subunit includes: a training module configured to sequentially input each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups to an initialized running parameter abnormality detection model, obtain predicted running state result information corresponding to each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups, compare the predicted running state result information corresponding to each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups with the sample running state result information corresponding to the sample vehicle running parameter group, obtain a predicted accuracy of the initialized running parameter abnormality detection model, determine whether the predicted accuracy is greater than a preset accuracy threshold, and if so, and taking the initialized driving parameter abnormity detection model as a trained driving parameter abnormity detection model.
In some embodiments, the driving parameter abnormality detection model training subunit includes: and the parameter modification module is used for responding to the condition that the accuracy is not greater than the preset accuracy threshold, adjusting the parameters of the initialized driving parameter abnormity detection model and returning to the training module.
In some embodiments, the apparatus includes a sample driving state result information obtaining unit configured to obtain sample driving state result information, and the sample driving state result information obtaining unit includes: a sample vehicle driving parameter group acquiring subunit configured to acquire a plurality of sample vehicle driving parameter groups of each of at least one sample driving state, wherein the sample driving state includes at least one of: a sample straight line driving type, a sample curve driving type and a sample climbing driving type; a reference parameter range obtaining subunit, configured to, for a sample driving state in the at least one sample driving state, count parameter ranges of a plurality of sample vehicle driving parameter sets to obtain a reference parameter range corresponding to the sample driving state, where the reference parameter range includes a safety parameter range of each sample vehicle driving parameter in the sample vehicle driving parameter sets; and the sample driving state result information setting subunit is configured to set sample driving state result information for the corresponding sample driving state through the reference parameter range.
In some embodiments, the type information of the sample driving state includes a safe driving state type and a dangerous driving state type, and the sample driving state result information setting subunit includes: and the sample driving state result information setting module is used for setting the sample driving state result information corresponding to the sample driving state as normal driving in response to the fact that the type information of the sample driving state is the safe driving state type, and otherwise, setting the sample driving state result information as abnormal driving.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to perform the method for detecting driving safety of the first aspect.
In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for detecting driving safety of the first aspect.
The method and the device for detecting the driving safety provided by the embodiment of the disclosure firstly acquire a vehicle driving parameter group in real time; then, importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group; and finally, sending out a warning signal when the driving state result information is abnormal driving. According to the scheme, the abnormity detection can be carried out on the vehicle running parameter set in real time, and the alarm signal is sent out in time, so that the running safety of the vehicle is improved.
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Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for detecting driving safety according to the present disclosure;
FIG. 3 is a schematic diagram of an application scenario of a method for detecting driving safety according to the present disclosure;
FIG. 4 is a flow diagram of one embodiment of a driving parameter anomaly detection model training method according to the present disclosure;
FIG. 5 is a schematic structural diagram of one embodiment of an apparatus for detecting driving safety according to the present disclosure;
FIG. 6 is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows an exemplary system architecture 100 of a method for detecting driving safety or an apparatus for detecting driving safety to which embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include vehicles 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the vehicles 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The vehicles 101, 102, 103 may interact with a server 105 over a network 104 to receive or send messages, etc. The vehicles 101, 102, 103 may have mounted thereon various data acquisition devices, such as distance sensors, speed sensors, gyroscopes, GPS, radar, antennas, and the like.
The vehicles 101, 102, 103 may be various vehicles having a plurality of data acquisition units and data processing units, including but not limited to electric vehicles, hybrid electric vehicles, internal combustion engine vehicles, unmanned vehicles, and the like.
The server 105 may be a server that provides various services, such as a server that processes vehicle travel parameter groups acquired on the vehicles 101, 102, 103. The server may analyze and otherwise process the received data, such as the vehicle travel parameter set, and determine which vehicle travel parameters in the vehicle travel parameter set are in error.
It should be noted that the method for detecting driving safety provided by the embodiment of the present disclosure is generally performed by the vehicles 101, 102, 103, and accordingly, the apparatus for detecting driving safety is generally disposed in the vehicles 101, 102, 103.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (for example, to provide distributed services), or may be implemented as a single software or software module, and is not limited specifically herein.
It should be understood that the number of vehicles, networks, and servers in FIG. 1 is merely illustrative. There may be any number of vehicles, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for detecting driving safety according to the present disclosure is shown. The method for detecting driving safety comprises the following steps:
In the present embodiment, the execution subject of the method for detecting driving safety (for example, the vehicles 101, 102, 103 shown in fig. 1) may acquire the vehicle driving parameter group by a wired connection manner or a wireless connection manner. According to actual needs, the vehicle driving parameters in the vehicle driving parameter group may also be other types of data, which are not described in detail herein. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Existing data stream processing for vehicles also has some disadvantages. The normal running of the vehicle requires processing of a large amount of data from various signal acquisition devices, signal processing devices and network signals. However, the signal acquisition device, the signal processing device and the network signal are easy to be intercepted and tampered, and even the virus signal is invaded, so that the vehicle makes an error judgment. The virus signal may even directly control the vehicle, which seriously affects the driving safety of the vehicle.
In order to detect driving safety, the executive body can acquire data acquired by each data acquisition device and then acquire a vehicle driving parameter group from the data. The vehicle driving parameter group may be various parameters related to driving safety, which are predetermined by a technician. That is, the set of vehicle travel parameters can characterize the driving safety of the vehicle. In practice, the safe driving of the vehicle is related to a plurality of parameters, and the actual driving safety cannot be really reflected by one parameter alone. For example, existing vehicle systems may generate an alert when a vehicle accelerates straight within a race track beyond a certain threshold in order to detect vehicle performance. Obviously, the alarm at this time does not conform to the actual driving safety state. Therefore, the method and the device for representing the driving safety through the vehicle driving parameter set are more real than the method and the device for representing the driving safety through a single parameter. The vehicle travel parameter group may include a plurality of vehicle travel parameters having a correlation therebetween. The vehicle running parameter set may include at least one of: a running speed, a running speed change rate, a steering angle change rate, an accelerator pedal change, a brake pedal change, an intake air pressure change, and the like. There is a correlation between the running speed, the rate of change in the running speed, and the rate of change in the steering angle. For example, when the traveling speed is high, the rate of change in the steering angle cannot be made too large, otherwise rollover or the like is likely to occur.
After obtaining the vehicle running parameter group, the execution subject may import the vehicle running parameter group into a running parameter abnormality detection model trained in advance, and obtain running state result information corresponding to the vehicle running parameter group. The driving parameter abnormality detection model can be used for representing the corresponding relation between the vehicle driving parameter group and the driving state result information. The driving state result information may include driving normal and driving abnormal.
And when the driving state result information is that the driving is normal, the current driving safety of the execution main body is explained. When the driving state result information indicates that the driving is abnormal, the conditions that the execution main body has vehicle faults, detection equipment faults, virus invasion and the like are described. At this time, the execution main body may issue an alarm signal.
In some optional implementations of this embodiment, the method further includes: and sending out an alarm signal through a vehicle-mounted alarm device in response to that the vehicle is a manned vehicle.
When the execution subject is a manned vehicle, the warning signal may notify the driver through an in-vehicle horn.
In some optional implementations of this embodiment, the method further includes: and responding to the fact that the vehicle is an unmanned vehicle, sending out an alarm signal through a vehicle-mounted alarm device, sending the vehicle running parameter group to a server, and setting the vehicle running parameter group to be invalid.
When the execution subject is the unmanned vehicle, besides informing a possible driver through an in-vehicle horn, the vehicle running parameter group needs to be set invalid so as to send the current vehicle running parameter group to the vehicle processor. The vehicle running parameter group is set to be invalid by setting an invalid tag for the vehicle running parameter group, and the vehicle processor can abandon the vehicle running parameter group after finding the invalid tag; it is also possible to modify the data type of the vehicle travel parameter group so that the vehicle processor cannot perform data processing using the vehicle travel parameter group. The executing agent may then send the original set of vehicle travel parameters to the server 105 associated with the executing agent over the network, such that the server 105 performs analysis on the original set of vehicle travel parameters to determine which parameters of the original set of vehicle travel parameters caused the travel anomaly.
In some optional implementations of this embodiment, the method further includes: and controlling the execution main body to stop through the emergency control instruction.
The execution main body can also control the execution main body to stop through the emergency control instruction. Thus, the driving safety of the vehicle is improved.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for detecting driving safety according to the present embodiment. In the application scenario of fig. 3, the vehicle 102 travels at an abnormal travel position on the road, and among the vehicle travel parameters, an abnormality occurs in a plurality of vehicle travel parameters such as a travel speed change rate and a steering angle change rate. The vehicle 102 acquires a vehicle running parameter group in real time; the vehicle 102 imports the vehicle running parameter group into a pre-trained running parameter abnormality detection model, and obtains running state result information corresponding to the vehicle running parameter group as running abnormality; at this point, the vehicle 102 may issue a warning signal. Then, the vehicle travel parameters at this time are transmitted to the server 105 via the network 104.
The method provided by the embodiment of the disclosure firstly obtains the vehicle running parameter group in real time; then, importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group; and finally, sending out a warning signal when the driving state result information is abnormal driving. According to the scheme, the abnormity detection can be carried out on the vehicle running parameter set in real time, and the alarm signal is sent out in time, so that the running safety of the vehicle is improved.
With further reference to FIG. 4, a flow 400 of one embodiment of a driving parameter anomaly detection model training method is shown. The process 400 of the driving parameter anomaly detection model training method comprises the following steps:
In this embodiment, an executing entity (for example, the server 105 shown in fig. 1) of the driving parameter abnormality detection model training method may obtain the sample vehicle driving parameter group and the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter groups through a wired connection manner or a wireless connection manner.
In this embodiment, the executing entity may obtain a plurality of sample vehicle driving parameter sets and analyze and process them for those skilled in the art. One skilled in the art may empirically set corresponding sample driving state result information for each of a plurality of sample vehicle driving parameter sets.
In some optional implementation manners of this embodiment, the sample driving state result information is obtained through the following steps:
the method comprises the steps of firstly, obtaining a plurality of sample vehicle running parameter sets of each sample running state of at least one sample running state.
The technician may pre-select a plurality of sample vehicle driving parameter sets for each of the at least one sample driving state. Wherein the sample driving state comprises at least one of: a sample straight-line driving type, a sample curve driving type, a sample climbing driving type, and the like. The sample vehicle driving parameter set may be an actual parameter collected by a technician during actual driving of the vehicle. The sample vehicle running parameter set may include a positive sample vehicle running parameter set for safe running of the vehicle, and may also include a negative sample vehicle running parameter set for unsafe running of the vehicle. Through sample driving states such as a sample straight line driving type, a sample curve driving type and a sample climbing driving type, the method is favorable for obtaining sample driving state result information according with actual driving conditions, and is favorable for improving the driving safety of the vehicle.
And secondly, counting the parameter ranges of the plurality of sample vehicle running parameter groups according to the sample running state in the at least one sample running state to obtain a reference parameter range corresponding to the sample running state.
After obtaining the sample vehicle driving parameter group, the executive agent may perform statistics on parameters in the sample vehicle driving parameter group corresponding to each sample driving state to determine a range of each parameter in the sample driving state, so as to obtain a reference parameter range corresponding to the sample driving state. The reference parameter range may include a safety parameter range of each of the sample vehicle driving parameters in the sample vehicle driving parameter group.
And thirdly, setting sample driving state result information for the corresponding sample driving state through the reference parameter range.
After the reference parameter range is determined, the execution subject may set sample driving state result information for the corresponding sample driving state.
In some optional implementation manners of this embodiment, the type information of the sample driving state may include a safe driving state type and a dangerous driving state type, and the setting of the sample driving state result information for the corresponding sample driving state through the reference parameter range includes: and responding to the type information of the sample driving state as a safe driving state type, setting the result information of the sample driving state corresponding to the sample driving state as normal driving, and otherwise, setting as abnormal driving.
As can be seen from the above description, the sample vehicle running parameter set may include a positive sample vehicle running parameter set in which the vehicle runs safely, and may also include a negative sample vehicle running parameter set in which the vehicle runs unsafe. Accordingly, the type information of the sample driving state may include a safe driving state type and a dangerous driving state type. The execution subject can set the sample driving state result information corresponding to the sample driving state as normal driving when the type information of the sample driving state is the safe driving state type; and when the type information of the sample driving state is the dangerous driving state type, setting the sample driving state result information corresponding to the sample driving state as abnormal driving.
In this embodiment, based on the plurality of sample vehicle running parameter sets obtained in step 401, the executing entity may sequentially input each of the plurality of sample vehicle running parameter sets to the initialized running parameter abnormality detection model, so as to obtain the predicted running state result information corresponding to each of the plurality of sample vehicle running parameter sets. Here, the execution subject may input each sample vehicle running parameter group from the input side of the initialized running parameter abnormality detection model, sequentially perform processing of parameters of each layer in the initialized running parameter abnormality detection model, and output the processed parameters from the output side of the initialized running parameter abnormality detection model, where information output from the output side is predicted running state result information corresponding to the sample vehicle running parameter group. The initialized driving parameter abnormity detection model can be an untrained driving parameter abnormity detection model or an untrained driving parameter abnormity detection model, each layer of the initialized driving parameter abnormity detection model is provided with initialized parameters, and the initialized parameters can be continuously adjusted in the training process of the driving parameter abnormity detection model. The driving parameter abnormality detection model may be an intelligent network such as a deep learning network, and is not described in detail herein.
In the present embodiment, the predicted driving state result information corresponding to each of the plurality of sample vehicle driving parameter sets obtained in step 402 is based on. The execution subject may compare the predicted driving state result information corresponding to each sample vehicle driving parameter group in the plurality of sample vehicle driving parameter groups with the sample driving state result information corresponding to the sample vehicle driving parameter group, so as to obtain the prediction accuracy of the initialized driving parameter abnormality detection model. Specifically, if the predicted driving state result information corresponding to one sample vehicle driving parameter group is the same as the sample driving state result information corresponding to the sample vehicle driving parameter group, the driving parameter abnormality detection model is initialized to predict correctly; and if the predicted driving state result information corresponding to one sample vehicle driving parameter group is different from the sample driving state result information corresponding to the sample vehicle driving parameter group, initializing a driving parameter abnormality detection model to predict errors. Here, the executing entity may calculate a ratio of the number of predicted correctness to the total number of samples, and use the ratio as the prediction accuracy of the initialized driving parameter abnormality detection model.
In this embodiment, based on the prediction accuracy of the initialized driving parameter abnormality detection model obtained in step 403, the execution subject may compare the prediction accuracy of the initialized driving parameter abnormality detection model with a preset accuracy threshold. If the accuracy is greater than the preset accuracy threshold, go to step 405; if not, go to step 406.
And 405, taking the initialized driving parameter abnormity detection model as a trained driving parameter abnormity detection model.
In this embodiment, when the prediction accuracy of the initialized driving parameter abnormality detection model is greater than the preset accuracy threshold, it indicates that the driving parameter abnormality detection model is trained, and at this time, the execution subject may use the initialized driving parameter abnormality detection model as the trained driving parameter abnormality detection model.
And step 406, adjusting the parameters of the initialized driving parameter abnormality detection model.
In this embodiment, in a case that the prediction accuracy of the initialized driving parameter abnormality detection model is not greater than the preset accuracy threshold, the execution subject may adjust the parameters of the initialized driving parameter abnormality detection model, and return to the execution step 402 until the driving parameter abnormality detection model capable of representing the correspondence between the vehicle driving parameter group and the driving state result information is trained.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an apparatus for detecting driving safety, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for detecting driving safety of the present embodiment may include: a parameter obtaining unit 501, a driving state result information obtaining unit 502 and a safety warning unit 503. The parameter acquiring unit 501 is configured to acquire a vehicle running parameter group in real time, where the vehicle running parameters included in the vehicle running parameter group have a correlation; the driving state result information obtaining unit 502 is configured to import the vehicle driving parameter group into a pre-trained driving parameter abnormality detection model, and obtain driving state result information corresponding to the vehicle driving parameter group, wherein the driving parameter abnormality detection model is used for representing a corresponding relationship between the vehicle driving parameter group and the driving state result information, and the driving state result information includes normal driving and abnormal driving; the safety warning unit 503 is configured to issue a warning signal in response to the driving state result information being a driving abnormality.
In some optional implementations of the embodiment, the apparatus 500 for detecting driving safety may include a driving parameter abnormality detection model training unit (not shown in the figure) configured to train a driving parameter abnormality detection model, where the driving parameter abnormality detection model training unit includes: a sample acquisition subunit (not shown in the figure) and a driving parameter abnormality detection model training subunit (not shown in the figure). Wherein the sample acquiring subunit is configured to acquire a plurality of sample vehicle running parameter sets and sample running state result information corresponding to each of the plurality of sample vehicle running parameter sets; the driving parameter abnormality detection model training subunit is configured to train the driving parameter abnormality detection model by taking each of the plurality of sample vehicle driving parameter groups as an input and taking the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter groups as an output.
In some optional implementation manners of this embodiment, the driving parameter abnormality detection model training subunit may include: a training module (not shown in the figures) configured to sequentially input each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups into an initialized running parameter abnormality detection model, obtain predicted running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups, compare the predicted running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups with the sample running state result information corresponding to the sample vehicle running parameter group, obtain a predicted accuracy of the initialized running parameter abnormality detection model, determine whether the predicted accuracy is greater than a preset accuracy threshold, and if so, and taking the initialized driving parameter abnormity detection model as a trained driving parameter abnormity detection model.
In some optional implementation manners of this embodiment, the driving parameter abnormality detection model training subunit includes: and a parameter modification module (not shown) configured to adjust the parameters of the initialized driving parameter anomaly detection model in response to the accuracy not being greater than the preset accuracy threshold, and return to the training module.
In some optional implementation manners of this embodiment, the apparatus 500 for detecting driving safety may include a sample driving state result information obtaining unit (not shown in the figure) configured to obtain sample driving state result information, where the sample driving state result information obtaining unit may include: a sample vehicle running parameter group acquiring subunit (not shown in the figure), a reference parameter range acquiring subunit (not shown in the figure), and a sample running state result information setting subunit (not shown in the figure). Wherein the sample vehicle driving parameter group acquiring subunit is configured to acquire a plurality of sample vehicle driving parameter groups of each of at least one sample driving state, wherein the sample driving state includes at least one of: a sample straight line driving type, a sample curve driving type and a sample climbing driving type; a reference parameter range obtaining subunit, configured to, for a sample driving state in the at least one sample driving state, count parameter ranges of a plurality of sample vehicle driving parameter sets to obtain a reference parameter range corresponding to the sample driving state, where the reference parameter range includes a safety parameter range of each sample vehicle driving parameter in the sample vehicle driving parameter sets; the sample driving state result information setting subunit is configured to set sample driving state result information for the corresponding sample driving state by the reference parameter range.
In some optional implementation manners of this embodiment, the type information of the sample driving state may include a safe driving state type and a dangerous driving state type, and the sample driving state result information setting subunit may include: and a sample driving state result information setting module (not shown in the figure) configured to set the sample driving state result information corresponding to the sample driving state as normal driving in response to the type information of the sample driving state being the safe driving state type, and otherwise, set as abnormal driving.
The present embodiment also provides an electronic device, including: one or more processors; a memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to perform the above-described method for detecting driving safety.
The present embodiment also provides a computer-readable medium, on which a computer program is stored, which when executed by a processor implements the above-described method for detecting driving safety.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use with an electronic device (e.g., server 105 of FIG. 1) to implement an embodiment of the present disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure.
It should be noted that the computer readable medium mentioned above in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, 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, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a vehicle running parameter group in real time, wherein a plurality of vehicle running parameters contained in the vehicle running parameter group have a correlation relationship; importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group, wherein the running parameter abnormity detection model is used for representing the corresponding relation between the vehicle running parameter group and the running state result information, and the running state result information comprises normal running and abnormal running; and sending out an alarm signal in response to the driving state result information being abnormal driving.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The 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 connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises a parameter acquisition unit, a driving state result information acquisition unit and a safety alarm unit. Where the names of the elements do not in some cases constitute a limitation of the elements themselves, for example, a security alarm element may also be described as an "element for issuing an alarm signal".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (14)
1. A method for detecting driving safety, comprising:
acquiring a vehicle running parameter group in real time, wherein a plurality of vehicle running parameters contained in the vehicle running parameter group have a correlation relationship;
importing the vehicle running parameter group into a pre-trained running parameter abnormity detection model to obtain running state result information corresponding to the vehicle running parameter group, wherein the running parameter abnormity detection model is used for representing the corresponding relation between the vehicle running parameter group and the running state result information, and the running state result information comprises normal running and abnormal running;
and sending out an alarm signal in response to the driving state result information being abnormal driving.
2. The method according to claim 1, wherein the driving parameter abnormality detection model is trained by the following steps:
obtaining a plurality of sample vehicle running parameter groups and sample running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups;
and taking each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups as input, taking sample running state result information corresponding to each sample vehicle running parameter group in the plurality of sample vehicle running parameter groups as output, and training to obtain the running parameter abnormality detection model.
3. The method according to claim 2, wherein the training of the driving parameter abnormality detection model by taking each of the plurality of sample vehicle driving parameter sets as an input and taking the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter sets as an output comprises:
the following training steps are performed: sequentially inputting each sample vehicle running parameter group in the multiple sample vehicle running parameter groups into an initialized running parameter abnormity detection model to obtain predicted running state result information corresponding to each sample vehicle running parameter group in the multiple sample vehicle running parameter groups, comparing the predicted running state result information corresponding to each sample vehicle running parameter group in the multiple sample vehicle running parameter groups with the sample running state result information corresponding to the sample vehicle running parameter group to obtain the prediction accuracy of the initialized running parameter abnormity detection model, determining whether the prediction accuracy is greater than a preset accuracy threshold, and if so, taking the initialized running parameter abnormity detection model as a trained running parameter abnormity detection model.
4. The method according to claim 3, wherein the training of the driving parameter abnormality detection model by taking each of the plurality of sample vehicle driving parameter sets as an input and taking the sample driving state result information corresponding to each of the plurality of sample vehicle driving parameter sets as an output comprises:
and responding to the condition that the accuracy is not greater than the preset accuracy threshold, adjusting the parameters of the initialized driving parameter abnormity detection model, and continuing to execute the training step.
5. The method of claim 2, wherein the sample driving state result information is obtained by:
obtaining a plurality of sample vehicle driving parameter sets of each sample driving state of at least one sample driving state, wherein the sample driving state comprises at least one of the following items: a sample straight line driving type, a sample curve driving type and a sample climbing driving type;
for a sample driving state in the at least one sample driving state, counting parameter ranges of a plurality of sample vehicle driving parameter sets to obtain a reference parameter range corresponding to the sample driving state, wherein the reference parameter range comprises a safety parameter range of each sample vehicle driving parameter in the sample vehicle driving parameter sets;
and setting sample driving state result information for the corresponding sample driving state through the reference parameter range.
6. The method of claim 5, wherein the type information of the sample driving state comprises a safe driving state type and a dangerous driving state type, an
Sample driving state result information is set for the corresponding sample driving state through the reference parameter range, and the method comprises the following steps:
and responding to the type information of the sample driving state as a safe driving state type, setting the result information of the sample driving state corresponding to the sample driving state as normal driving, and otherwise, setting as abnormal driving.
7. An apparatus for detecting driving safety, comprising:
the vehicle driving parameter acquisition unit is configured to acquire a vehicle driving parameter group in real time, and a plurality of vehicle driving parameters contained in the vehicle driving parameter group have a correlation relationship;
a driving state result information obtaining unit, configured to import the vehicle driving parameter group into a pre-trained driving parameter abnormality detection model, and obtain driving state result information corresponding to the vehicle driving parameter group, where the driving parameter abnormality detection model is used to represent a correspondence between the vehicle driving parameter group and the driving state result information, and the driving state result information includes driving normal and driving abnormal;
and the safety warning unit is used for responding to the driving state result information as abnormal driving and is configured to send out a warning signal.
8. The apparatus according to claim 7, wherein the apparatus includes a driving parameter abnormality detection model training unit configured to train a driving parameter abnormality detection model, the driving parameter abnormality detection model training unit including:
a sample acquisition subunit configured to acquire a plurality of sample vehicle travel parameter groups and sample travel state result information corresponding to each of the plurality of sample vehicle travel parameter groups;
and the driving parameter abnormality detection model training subunit is configured to take each sample vehicle driving parameter group in the plurality of sample vehicle driving parameter groups as input, take the sample driving state result information corresponding to each sample vehicle driving parameter group in the plurality of sample vehicle driving parameter groups as output, and train to obtain the driving parameter abnormality detection model.
9. The apparatus of claim 8, wherein the driving parameter anomaly detection model training subunit comprises:
a training module configured to sequentially input each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups to an initialized running parameter abnormality detection model, obtain predicted running state result information corresponding to each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups, compare the predicted running state result information corresponding to each sample vehicle running parameter group of the plurality of sample vehicle running parameter groups with the sample running state result information corresponding to the sample vehicle running parameter group, obtain a predicted accuracy of the initialized running parameter abnormality detection model, determine whether the predicted accuracy is greater than a preset accuracy threshold, and if so, and taking the initialized driving parameter abnormity detection model as a trained driving parameter abnormity detection model.
10. The apparatus of claim 9, wherein the driving parameter anomaly detection model training subunit comprises:
and the parameter modification module is used for responding to the condition that the accuracy is not greater than the preset accuracy threshold, adjusting the parameters of the initialized driving parameter abnormity detection model and returning to the training module.
11. The apparatus according to claim 8, wherein the apparatus includes a sample driving state result information acquisition unit configured to acquire sample driving state result information, the sample driving state result information acquisition unit including:
a sample vehicle driving parameter group acquiring subunit configured to acquire a plurality of sample vehicle driving parameter groups of each of at least one sample driving state, wherein the sample driving state includes at least one of: a sample straight line driving type, a sample curve driving type and a sample climbing driving type;
a reference parameter range obtaining subunit, configured to, for a sample driving state in the at least one sample driving state, count parameter ranges of a plurality of sample vehicle driving parameter groups to obtain a reference parameter range corresponding to the sample driving state, where the reference parameter range includes a safety parameter range of each sample vehicle driving parameter in the sample vehicle driving parameter groups;
and the sample driving state result information setting subunit is configured to set sample driving state result information for the corresponding sample driving state through the reference parameter range.
12. The apparatus of claim 11, wherein the type information of the sample driving state comprises a safe driving state type and a dangerous driving state type, and
the sample driving state result information setting subunit includes:
and the sample driving state result information setting module is used for setting the sample driving state result information corresponding to the sample driving state as normal driving in response to the fact that the type information of the sample driving state is the safe driving state type, and otherwise, setting the sample driving state result information as abnormal driving.
13. An electronic device, comprising:
one or more processors;
a memory having one or more programs stored thereon,
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-6.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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