CN114670848A - Dangerous driving prediction method and device, terminal equipment and storage medium - Google Patents
Dangerous driving prediction method and device, terminal equipment and storage medium Download PDFInfo
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- CN114670848A CN114670848A CN202210203959.XA CN202210203959A CN114670848A CN 114670848 A CN114670848 A CN 114670848A CN 202210203959 A CN202210203959 A CN 202210203959A CN 114670848 A CN114670848 A CN 114670848A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0872—Driver physiology
Abstract
The application is applicable to the technical field of signal processing, and provides a prediction method, a prediction device, terminal equipment and a storage medium for dangerous driving, wherein the prediction method comprises the following steps: acquiring a bioelectricity signal acquired by a bioelectricity signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system, wherein the bioelectricity signal is a bioelectricity signal of a driver in a driving process, and the first vehicle environment information is environment information around a position where a vehicle is located; determining a signal instruction corresponding to the bioelectric signal according to the signal characteristics of the bioelectric signal, wherein the signal instruction refers to a vehicle driving action instruction intended by the driver in real time; and if the first vehicle environment information is matched with the signal instruction, determining that the vehicle has a dangerous driving tendency. The scheme can predict the dangerous driving tendency of the vehicle in time before the dangerous driving action of the vehicle occurs.
Description
Technical Field
The application belongs to the technical field of signal processing, and particularly relates to a dangerous driving prediction method and device, terminal equipment and a storage medium.
Background
As the number of automobiles increases, traffic accidents caused by dangerous driving of drivers are also increasing year by year. In order to realize safe driving, a driver not only needs to concentrate on controlling the automobile, but also needs to observe road conditions and instrument panel display contents in time and the like in the process of driving the automobile, so that the driver is always in a nervous state, the reaction speed of the driver is easy to slow when the driver is in the nervous state for a long time, and traffic accidents are easy to happen under the condition of complex road conditions once the reaction speed of the driver is slow.
In order to reduce the driving burden of the driver, in recent years, there have been increasing automobile driving assistance systems, which mainly display the surrounding environment state and the road condition on the automobile by accurately sensing and predicting the surrounding environment state, such as the application of a head-up display on the automobile. However, when the driver is in a stressed state for a long time, the driver is easy to ignore the road condition information displayed by the head-up display, and further dangerous driving behaviors occur, so that unexpected traffic accidents occur, and remedial measures are taken after dangerous driving actions occur to the vehicle, so that the traffic accidents are often not prevented in time.
Therefore, how to predict the dangerous driving action of the vehicle in time before the dangerous driving action of the vehicle occurs becomes a problem which needs to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a dangerous driving prediction method, a dangerous driving prediction device, terminal equipment and a storage medium, and the dangerous driving tendency of a vehicle in the driving process can be predicted in time before a dangerous driving action occurs.
A first aspect of an embodiment of the present application provides a prediction method for dangerous driving, where the prediction method includes:
acquiring a bioelectricity signal acquired by a bioelectricity signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system, wherein the bioelectricity signal is a bioelectricity signal of a driver of a vehicle in a driving process, and the first vehicle environment information is environment information around the position where the vehicle is located;
determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal, wherein the signal instruction refers to a vehicle driving action instruction intended by the driver in real time;
and if the first vehicle environment information is matched with the signal instruction, determining that the vehicle has a dangerous driving tendency, wherein the matching of the first vehicle environment information and the signal instruction means that under the condition of the first vehicle environment information, if the vehicle executes the signal instruction, the risk of traffic accidents exists.
A second aspect of an embodiment of the present application provides a prediction apparatus for dangerous driving, including:
the acquisition module is used for acquiring a bioelectricity signal acquired by the bioelectricity signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system, wherein the bioelectricity signal refers to a bioelectricity signal of a driver of the vehicle in the driving process, and the first vehicle environment information refers to environment information around the position where the vehicle is located;
the instruction determining module is used for determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal, wherein the signal instruction refers to a vehicle driving action instruction intended by the driver in real time;
the prediction module is used for determining that the vehicle has a dangerous driving tendency if the first vehicle environment information is matched with the signal instruction, and the fact that the first vehicle environment information is matched with the signal instruction means that under the condition that the first vehicle environment information is matched with the signal instruction, the vehicle has a risk of traffic accidents if the vehicle executes the signal instruction.
A third aspect of an embodiment of the present application provides a terminal device, including: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the method for predicting dangerous driving according to the first aspect.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method for predicting dangerous driving according to the first aspect.
A fifth aspect of embodiments of the present application provides a computer program product, which, when running on a terminal device, causes the terminal device to execute the method for predicting dangerous driving according to the first aspect.
Compared with the prior art, the embodiment of the application has the beneficial effects that: according to the embodiment of the application, firstly, a bioelectrical signal acquired by a bioelectrical signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system are acquired, wherein the bioelectrical signal refers to a bioelectrical signal of a driver in a driving process, the bioelectrical signal records electric wave change of real-time movement intention of the driver of the vehicle, and the first vehicle environment information refers to environment information around a position where the vehicle is located. And secondly, determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal. Since the signal command is a vehicle driving action command intended by the driver in real time, if the first vehicle environment information is matched with the signal command, it can be determined that the vehicle has a dangerous driving tendency, because the first vehicle environment information is matched with the signal command, in the case of the first vehicle environment information, there is a risk of a traffic accident if the vehicle executes the signal command. Because there is time delay between the dangerous driving action that is formed to the transmission of human four limbs after the bioelectricity signal produces, the dangerous driving tendency of vehicle can be predicted in time before the dangerous driving action appears in the vehicle to this application.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting dangerous driving according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of the acquisition of signal characteristics of a target brain electrical signal;
fig. 3 is a schematic flowchart of a dangerous driving prediction method according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of a prediction apparatus for dangerous driving according to a third embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device according to a fourth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing a relative importance or importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather mean "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically stated.
According to the method, time delay exists between the generated bioelectric signals and the actions formed by the four limbs of the human body, so that dangerous driving tendencies of the vehicle in the driving process can be predicted in time before dangerous driving behaviors occur, and the time delay between the generation of the bioelectric signals and the actions formed by the four limbs of the human body can be utilized to analyze the bioelectric signals so as to predict the dangerous driving tendencies of the vehicle in the driving process.
Specifically, the bioelectric signal of the vehicle driver is analyzed to predict the dangerous driving tendency of the vehicle, and a signal instruction corresponding to the bioelectric signal is determined according to the signal characteristics of the bioelectric signal, wherein the bioelectric signal refers to the bioelectric signal of the driver in the driving process, the bioelectric signal records the electric wave change of the real-time movement intention of the driver of the vehicle, the signal instruction refers to the vehicle driving action instruction of the real-time intention of the driver, and then the environmental information around the position where the vehicle is located is determined by acquiring the first vehicle environmental information acquired by the vehicle driving assistance system. Since the signal command is a vehicle driving operation command intended by the driver in real time, and in the case of the first vehicle environment information, there is a risk of a traffic accident occurring if the vehicle executes the signal command, it is possible to determine that the vehicle has a dangerous driving tendency if the first vehicle environment information matches the signal command. And because there is time delay between the dangerous driving behaviors that the bioelectric signal is transmitted to four limbs of the human body after being generated, the dangerous driving tendency of the vehicle can be predicted in time before the dangerous driving action of the vehicle occurs by utilizing the time delay.
It should be understood that, the sequence numbers of the steps in this embodiment do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation to the implementation process of the embodiment of the present application.
In order to explain the technical solution of the present application, the following description is given by way of specific examples.
Referring to fig. 1, a schematic flow chart of a prediction method for dangerous driving according to a first embodiment of the present application is shown. As shown in fig. 1, the prediction method of dangerous driving may include the steps of:
The bioelectric signal refers to a bioelectric signal generated by a driver during driving, and may include an electroencephalogram signal, a surface electromyogram signal, and the like. The first vehicle environment information refers to environment information around a position where the vehicle is located, and the first vehicle environment information is acquired by the terminal device from the vehicle driving assistance system.
In the embodiment of the application, if a bioelectric signal is taken as an electroencephalogram signal as an example, acquiring the bioelectric signal acquired by the bioelectric signal acquisition device may refer to acquiring the electroencephalogram signal acquired by the electroencephalogram acquisition device, and acquiring the electroencephalogram signal acquired by the electroencephalogram acquisition device may first acquire the electroencephalogram signal of a driver in a driving process in real time by the electroencephalogram acquisition device, and then send the acquired electroencephalogram signal to the terminal device by the electroencephalogram acquisition device. The electroencephalogram signal can be acquired by a signal acquisition device which is composed of an electrode plate and a processor in communication connection with the electrode plate, and the process of acquiring the electroencephalogram signal by the electroencephalogram signal acquisition device in real time is specifically that the electrode plate is placed on the surface of the scalp of a driver according to the international standard electrode placement rule to acquire the electroencephalogram signal, and then the processor in communication connection with the electrode plate stores the electroencephalogram signal.
It should be understood that the electrode pads in the brain electrical acquisition device may be dry or wet electrode pads. When the dry electrode plate is adopted, medical conductive paste does not need to be coated, and the dry electrode plate has higher requirements on electrode materials; and the wet electrode sheet is required to be coated with conductive paste, so that the application is wide. The user can select the material of electrode slice according to the demand, and this application does not do the restriction to this.
It should also be understood that the acquisition of the bioelectrical signal acquired by the bioelectrical signal acquisition device may also refer to the acquisition of a surface electromyographic signal acquired by a surface electromyographic signal acquisition device. The surface myoelectricity acquisition device can be a signal acquisition device consisting of a dry and wet electrode plate and a processor in communication connection with the dry and wet electrode plate, wherein the dry and wet electrode plate is placed on the skin surface of a driver according to the international standard electrode placement rule.
In the embodiment of the application, the first vehicle environment information acquired by the vehicle driving assistance system can be acquired by the vehicle driving assistance system firstly acquiring the environment information around the position of the vehicle in the driving process in real time, and then the acquired first vehicle environment information is sent to the terminal device by the vehicle driving assistance system, so that the terminal device can acquire the first vehicle environment information. The vehicle driving assistance system mainly monitors surrounding people, non-motor vehicles, motor vehicles and the like in the process of parking or driving of the vehicle so as to obtain environmental information around the vehicle, and the first vehicle environmental information generally comprises road condition information and vehicle information.
And 102, determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal.
In the embodiment of the present application, taking the bioelectric signal as the electroencephalogram signal as an example, the signal instruction may refer to a vehicle driving action instruction of a brain motor imagery of a driver, for example: the electroencephalogram signal processing method comprises the steps of turning instructions, braking instructions, left-turning instructions, right-turning instructions and the like, wherein the electroencephalogram signal characteristics corresponding to each signal instruction are different, the electroencephalogram signal characteristics can be obtained by extracting through a time-frequency domain characteristic extraction method, and after the electroencephalogram signal characteristics are extracted, the signal characteristics can be classified and identified through a classifier so as to analyze the signal instructions corresponding to the electroencephalogram signal. The signal characteristics of the electroencephalogram signals refer to characteristic vectors corresponding to the electroencephalogram signals.
In a possible implementation, before determining the signal instruction corresponding to the bioelectrical signal according to the signal feature of the bioelectrical signal, the prediction method further includes:
filtering and denoising the bioelectrical signal to obtain a target bioelectrical signal;
extracting signal characteristics of the target bioelectrical signal;
correspondingly, according to the signal characteristics of the bioelectrical signal, determining the signal instruction corresponding to the bioelectrical signal comprises:
And determining a signal instruction corresponding to the target bioelectrical signal according to the signal characteristics of the target bioelectrical signal.
In the embodiment of the application, the bioelectric signal is taken as the electroencephalogram signal as an example, the bioelectric signal is filtered and denoised, the target electroencephalogram signal can be obtained, and the signal characteristics of the target electroencephalogram signal can be extracted. The electroencephalogram signals are the characteristics of consciousness and behaviors of the brain, and in the acquisition process, the weak amplitude of the electroencephalogram signals is easily influenced by the outside world, and some interference signals are inevitably introduced. Therefore, before a signal instruction corresponding to the electroencephalogram signal is determined according to the signal characteristics of the electroencephalogram signal, filtering and denoising processing are carried out on the electroencephalogram signal to improve the effectiveness and the authenticity of the electroencephalogram signal, wherein the noise in the electroencephalogram signal can be filtered out by carrying out filtering processing, the noise filtered out by filtering in the electroencephalogram signal can be removed by carrying out denoising processing, and a cleaner electroencephalogram signal, namely a target electroencephalogram signal, can be obtained after the electroencephalogram signal is filtered and denoised. And then, a signal instruction corresponding to the target electroencephalogram signal is determined by extracting the signal characteristics of the target electroencephalogram signal, and the signal instruction can better reflect the brain movement of a driver, so that the accuracy of determining the signal instruction is improved.
Illustratively, a wavelet threshold method can be selected for filtering and denoising the electroencephalogram signal, the wavelet threshold method has good time-frequency characteristics and can better analyze non-stationary signals, the noise signal is decomposed and filtered by Independent Component Analysis (ICA), a threshold function is constructed to obtain a wavelet threshold, and denoising is performed by combining the wavelet threshold and the threshold to obtain a cleaner electroencephalogram signal.
In one possible implementation, extracting the signal feature of the target brain electrical signal comprises:
decomposing the target brain electrical signal, and selecting a signal in a frequency range corresponding to the target rhythm wave as a signal component of the target brain electrical signal;
calculating an instantaneous energy value, a boundary energy value and complexity of the signal component;
determining an instantaneous energy spectrum characteristic vector of the target electroencephalogram signal according to the instantaneous energy value;
determining a boundary energy spectrum characteristic vector of the target electroencephalogram signal according to the boundary energy value;
determining a complexity characteristic vector of the target electroencephalogram signal according to the complexity of the signal component;
and determining a target feature vector corresponding to the target electroencephalogram signal according to the instantaneous energy spectrum feature vector, the boundary energy spectrum feature vector and the complexity feature vector.
The target feature vector is used for representing the signal features of the target electroencephalogram signal.
In the embodiment of the present application, because time and frequency are important bases for analyzing signals, a time-frequency domain feature extraction method is usually adopted to extract signal features, a target electroencephalogram signal is decomposed first to obtain a plurality of signal components, and signal components more matched with an event-related synchronization (ERS)/event-related de-synchronization (ERD) phenomenon are obtained by screening. Secondly, respectively calculating the instantaneous energy value, the boundary energy value and the complexity of each moment for the screened signal components, respectively determining three characteristics of an instantaneous energy spectrum, a boundary energy spectrum and the complexity corresponding to the target electroencephalogram according to the instantaneous energy value, the boundary energy value and the complexity of each moment, and constructing and obtaining a target characteristic vector corresponding to the target electroencephalogram according to the three characteristics by using a nonlinear characteristic vector method.
Exemplarily, as shown in fig. 2, which is an acquisition schematic diagram of signal characteristics of a target electroencephalogram, first, a dual-tree complex wavelet transform is performed on the target electroencephalogram, and a signal component more matched with the ERS/ERD phenomenon is obtained by screening, that is, a signal within a frequency range corresponding to a target rhythm wave is screened and used as a signal component of the target electroencephalogram, where the target rhythm wave may be a rhythm wave such as an α wave and a β wave, and the obtained signal components are a first component, a second component, a third component, and a fourth component, respectively. Secondly, performing Hilbert spectrum analysis on the first component, the second component, the third component and the fourth component to obtain an instantaneous energy value of the first component at each moment, an instantaneous energy value of the second component at each moment, an instantaneous energy value of the third component at each moment, an instantaneous energy value of the fourth component at each moment, a boundary energy value of the first component at each moment, a boundary energy value of the second component at each moment, a boundary energy value of the third component at each moment and a boundary energy value of the fourth component at each moment, arranging the instantaneous energy value of the first component at each moment, the instantaneous energy value of the second component at each moment, the instantaneous energy value of the third component at each moment and the instantaneous energy value of the fourth component at each moment according to the sequence of corresponding time of signals to obtain an instantaneous energy spectrum characteristic vector which is a row vector of 1 xk, k is an integer greater than zero.
Similarly, the boundary energy value of the first component at each moment, the boundary energy value of the second component at each moment, the boundary energy value of the third component at each moment, and the boundary energy value of the fourth component at each moment are arranged according to the sequence of the corresponding time of the signals to obtain a boundary energy spectrum feature vector, wherein the vector is also a 1 × k row vector. And calculating the complexity of the first component, the second component, the third component and the fourth component at each moment, and arranging the complexity of the first component, the second component, the third component and the fourth component at each moment according to the sequence of the corresponding time of the signals to obtain a complexity feature vector of the target electroencephalogram signal, wherein the vector is a 1 xk row vector in the same way. And finally, reconstructing the instantaneous energy spectrum characteristic vector, the boundary energy spectrum characteristic vector and the complexity characteristic vector by using a nonlinear characteristic vector method to construct a target characteristic vector corresponding to the target electroencephalogram signal.
It should be understood that, the signal features extracted by the time-frequency domain feature extraction method may also be implemented by empirical mode decomposition, wavelet transform, variational mode decomposition, and the like, which is not limited in this application.
In one possible implementation, determining, according to the signal characteristic of the brain electrical signal, a signal instruction corresponding to the brain electrical signal includes:
And classifying and identifying the signal characteristics by adopting a classifier, and determining a signal instruction corresponding to the electroencephalogram signal.
In the embodiment of the application, because the corresponding signal characteristics of each electroencephalogram signal are different, the different signal characteristics reflect different brain imagination actions of drivers, and the corresponding signal instructions are different. The signal feature recognition can adopt a classifier, and different classifiers can be selected according to the number of the signal instructions, for example, if the number of the signal instructions is two (namely, a turning instruction and a braking instruction), a classifier of two classifications can be adopted; if the number of the signal instructions is 5 (namely, a right-turning instruction, a left-turning instruction, a reversing instruction, a braking instruction and a straight-going instruction), a classifier with five classifications can be adopted.
In a possible implementation manner, the step of classifying and identifying the signal features by using a classifier to obtain a signal instruction corresponding to the electroencephalogram signal includes:
projecting the characteristic vectors corresponding to the signal characteristics to acquire the position information of each characteristic quantity in the characteristic vectors in the projection area;
and acquiring a signal instruction corresponding to the target area according to the position information of each characteristic quantity, and determining that the signal instruction corresponding to the target area is a signal instruction corresponding to the electroencephalogram signal.
The feature quantity refers to an element in the feature vector, the projection area comprises at least two sub-areas, any two sub-areas in the at least two sub-areas are not overlapped, one sub-area corresponds to one signal instruction, and the target area is the sub-area with the largest number of feature quantities.
In the embodiment of the application, the linear discriminant classifier can be adopted to classify and identify the signal characteristics, the method can minimize the distance variance among the characteristic quantities belonging to the same signal instruction and maximize the distance variance among the characteristic quantities belonging to different signal instructions, and the method can make the projection points belonging to the same signal instruction gather together as much as possible.
The signal characteristics are classified and identified by adopting a linear discriminant classifier, specifically, the characteristic vectors are projected to a projection area in a coordinate system, the position information (namely position coordinates) of each characteristic quantity in the characteristic vectors is obtained through the coordinates of the coordinate system, the projection area comprises at least two sub-areas, any two of the at least two sub-areas are not overlapped, and because each sub-area corresponds to one signal instruction, if all the characteristic quantities fall in the same sub-area after projection, the signal instruction corresponding to the sub-area can be determined as the signal instruction corresponding to the electroencephalogram signal, and if all the characteristic quantities fall in different sub-areas after projection, the signal instruction corresponding to the sub-area with the most characteristic quantity in all the sub-areas can be determined as the signal instruction corresponding to the electroencephalogram signal.
It should be understood that, for the above-mentioned signal characteristics according to the bioelectric signal, the signal instruction corresponding to the bioelectric signal is all described by taking the bioelectric signal as the electroencephalogram signal, and for other signals included in the bioelectric signal, such as the surface electromyogram signal, the method adopted when determining the signal instruction corresponding to the bioelectric signal and extracting the signal characteristics corresponding to the bioelectric signal is consistent with the method adopted when extracting the signal characteristics of the electroencephalogram signal and determining the signal instruction corresponding to the electroencephalogram signal, and the application does not limit the type of the bioelectric signal.
And 103, if the first vehicle environment information is matched with the signal instruction, determining that the vehicle has a dangerous driving tendency.
In the embodiment of the application, the fact that the first vehicle environment information is matched with the signal command means that under the condition that the first vehicle environment information is obtained, if a vehicle executes the signal command, a risk of traffic accidents exists.
Exemplarily, taking a bioelectrical signal as an electroencephalogram signal as an example, if the acquired first vehicle environment information is that a running vehicle is detected at the rear right side, the acquired signal instruction corresponding to the electroencephalogram signal is a right lane changing instruction, and at this time, under the condition that the running vehicle is detected at the rear right side, if the vehicle executes the right lane changing instruction, a risk of traffic accidents exists; if the first vehicle environment information is that a pedestrian is detected in front, the acquired electroencephalogram signal is an acceleration instruction, and under the condition that the pedestrian is detected in front, the risk of traffic accidents exists if the vehicle executes the acceleration instruction.
In the embodiment of the application, whether the first vehicle environment information is matched with the signal instruction is judged, and if the acquired first vehicle environment information and the acquired signal instruction accord with the mapping relation between the second vehicle environment information and the dangerous driving instruction stored in the terminal device, the first vehicle environment information is determined to be matched with the signal instruction, and the vehicle has a tendency of dangerous driving; and if the acquired first vehicle environment information and the acquired signal instruction do not accord with the mapping relation between the second vehicle environment information and the dangerous driving instruction stored in the terminal equipment, determining that the first vehicle environment information is not matched with the signal instruction, and the vehicle does not have a dangerous driving tendency.
It should be understood that the second vehicle environment information refers to the vehicle environment information in the mapping relationship between the second vehicle environment information and the dangerous driving instruction stored in advance in the terminal device.
Exemplarily, taking a bioelectric signal as an electroencephalogram signal as an example, assuming that a first mapping relationship between second vehicle environment information and a dangerous driving instruction stored in a terminal device is a mapping relationship between a detected right rear traveling vehicle and a right lane change instruction, if the acquired first vehicle environment information is that the detected right rear traveling vehicle is present, and the acquired signal instruction corresponding to the electroencephalogram signal is the right lane change instruction, determining that the first vehicle environment information is matched with the signal instruction, and the vehicle has a tendency of dangerous driving; if the acquired first vehicle environment information is that a running vehicle is detected on the rear right side, and the acquired signal instruction corresponding to the electroencephalogram signal is a lane-changing-to-left instruction, because the mapping relation between the second vehicle environment information and the dangerous driving instruction stored in the terminal device does not include the mapping relation between the running vehicle on the rear right side and the lane-changing-to-left instruction, the first vehicle environment information is not matched with the signal instruction, and the vehicle does not have a tendency of dangerous driving.
Exemplarily, taking a bioelectric signal as an electroencephalogram signal as an example, assuming that a second mapping relationship between second vehicle environment information and a dangerous driving instruction stored in a terminal device is a mapping relationship between a detected pedestrian in front and an acceleration instruction, and if the acquired first vehicle environment information is that a pedestrian in front is detected and the acquired signal instruction corresponding to the electroencephalogram signal is an acceleration instruction, determining that the first vehicle environment information is matched with the signal instruction and the vehicle has a tendency of dangerous driving; if the acquired first vehicle environment information indicates that a pedestrian is detected in front, and the acquired signal instruction corresponding to the electroencephalogram signal is a brake instruction, because the mapping relation between the second vehicle environment information stored in the terminal device and the dangerous driving instruction does not include the mapping relation between the detected pedestrian in front and the brake instruction, the first vehicle environment information is not matched with the signal instruction, and the vehicle does not have the tendency of dangerous driving.
In the embodiment of the application, firstly, a bioelectrical signal acquired by a bioelectrical signal acquisition device and first vehicle environment information acquired by a vehicle driving assistance system are acquired, wherein the bioelectrical signal refers to a bioelectrical signal of a driver in a driving process, the bioelectrical signal records electric wave changes of real-time movement intention of the driver of the vehicle, and the first vehicle environment information refers to environment information around a position where the vehicle is located. And secondly, determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal. Since the signal command is a vehicle driving action command intended by the driver in real time, if the first vehicle environment information is matched with the signal command, it can be determined that the vehicle has a dangerous driving tendency, because the first vehicle environment information is matched with the signal command, in the case of the first vehicle environment information, there is a risk of a traffic accident if the vehicle executes the signal command. Because there is time delay between the dangerous driving action that is formed to the transmission of human four limbs after the bioelectricity signal produces, the dangerous driving tendency of vehicle can be predicted in time before the dangerous driving action appears in the vehicle to this application.
Referring to fig. 3, a schematic flow chart of a prediction method for dangerous driving according to the second embodiment of the present application is shown. As shown in fig. 3, the prediction method of dangerous driving may include the steps of:
The steps 301-302 of this embodiment are the same as the steps 101-102 of the previous embodiment, and reference may be made to these steps, which are not described herein again.
The corresponding relation comprises a mapping relation between M pieces of second vehicle environment information and N pieces of dangerous driving instructions, wherein M is an integer larger than zero, and N is an integer larger than zero.
In the embodiment of the application, because there is usually more than one signal instruction corresponding to a traffic accident occurring in the vehicle under the condition of the first vehicle environment information, for example, when it is obtained that there is a pedestrian in front of the first vehicle environment information, the signal instruction corresponding to the traffic accident occurring in the vehicle may be an acceleration instruction or an advance instruction, that is, when the first vehicle environment information is a pedestrian in front of the first vehicle environment information, there is a risk of a traffic accident occurring when the vehicle executes the advance instruction or the acceleration instruction, and the risk of a traffic accident occurring in the two instructions is different.
In the embodiment of the application, the terminal device stores the corresponding relationship, and whether the first vehicle environment information is matched with the signal instruction can be judged by judging whether a mapping relationship between the first vehicle environment information and the signal instruction exists in the corresponding relationship.
And step 304, if the mapping relation between the first vehicle environment information and the signal instruction exists in the corresponding relation, determining that the first vehicle environment information is matched with the signal instruction.
In the embodiment of the present application, if a mapping relationship between the first vehicle environment information and the signal command exists in the corresponding relationship, it is described that, in the case of the first vehicle environment information, there is a risk of a traffic accident when the vehicle executes the signal command.
And step 305, if the first vehicle environment information is matched with the signal instruction, determining that the vehicle has a dangerous driving tendency.
Step 305 of this embodiment is the same as step 103 of the previous embodiment, and may refer to it, which is not described herein again.
And step 306, acquiring the danger level corresponding to the signal instruction under the condition of the first vehicle environment information.
In the embodiment of the application, under the condition that the first vehicle environment information is matched with the signal instruction, it is determined that a risk of a traffic accident occurs when the vehicle executes the signal instruction, but in a corresponding relation stored in the terminal, a mapping relation exists between M pieces of second vehicle environment information and N pieces of dangerous driving instructions, that is, a mapping relation exists between one piece of second vehicle environment information and a plurality of dangerous driving instructions or between one piece of dangerous driving instructions, when a mapping relation exists between one piece of second vehicle environment information and a plurality of dangerous driving instructions, the risk of the traffic accident occurring after the vehicle executes each dangerous driving instruction and the personal injury caused by the vehicle are different, so that the mapping relation between each piece of second vehicle environment information and each dangerous driving instruction corresponds to one dangerous level, and the larger the personal injury caused by the vehicle is, the higher the corresponding dangerous level is.
For example, the second vehicle environment information is that a pedestrian is detected ahead, the dangerous driving command corresponding to the second vehicle environment information is a forward command and an acceleration command, and personal injury caused by the acceleration command is greater, so that the danger level corresponding to the mapping relationship between the detected pedestrian and the acceleration command is set to 2, the danger registration corresponding to the mapping relationship between the detected pedestrian and the forward command is set to 1, and the danger degree of the 2 level is greater than the 1 level.
And 307, performing biostimulation on the driver of the vehicle according to the danger level.
In the embodiment of the application, the electrode plates related to the arms of the driver can be used for biostimulating the driver so as to warn the driver of the danger of the action to be executed by the driver, biostimulation with different intensities is performed according to different danger levels, and the higher the danger level is, the higher the biostimulation intensity is.
Compared with the first embodiment, the first embodiment sets different danger levels according to the mapping relation between different second vehicle environment information and dangerous driving instructions, sets corresponding danger levels for each pair of mapping relations on the basis of personal injury caused by execution of corresponding dangerous driving instructions, and performs biostimulation with corresponding strength on a driver according to the danger levels so as to better warn the driver and improve driving safety in the driving process.
Referring to fig. 4, a schematic structural diagram of a prediction apparatus for dangerous driving according to a third embodiment of the present application is shown, and for convenience of description, only the portions related to the third embodiment of the present application are shown.
The prediction device for dangerous driving specifically comprises the following modules:
the obtaining module 401 is configured to obtain a bioelectrical signal acquired by the bioelectrical signal acquiring device and first vehicle environment information acquired by the vehicle driving assistance system, where the bioelectrical signal is a bioelectrical signal of a driver of a vehicle during driving, and the first vehicle environment information is environment information around a position where the vehicle is located.
The instruction determining module 402 is configured to determine a signal instruction corresponding to the bioelectrical signal according to the signal feature of the bioelectrical signal, where the signal instruction is a vehicle driving action instruction intended by the driver in real time.
The prediction module 403 is configured to determine that the vehicle has a dangerous driving tendency if the first vehicle environment information matches the signal instruction, where the matching of the first vehicle environment information and the signal instruction means that a risk of a traffic accident exists if the vehicle executes the signal instruction under the condition of the first vehicle environment information.
In this embodiment, the prediction apparatus for dangerous driving may further include:
The judging module is used for judging whether a mapping relation between the first vehicle environment information and the signal command exists in a corresponding relation, wherein the corresponding relation comprises mapping relations between M pieces of second vehicle environment information and N pieces of dangerous driving commands, M is an integer larger than zero, and N is an integer larger than zero;
the matching determination module is used for determining that the first vehicle environment information is matched with the signal instruction if the mapping relation between the first vehicle environment information and the signal instruction exists in the corresponding relation;
and the mismatching determining module is used for determining that the first vehicle environment information is not matched with the signal instruction if the mapping relation between the first vehicle environment information and the signal instruction does not exist in the corresponding relation.
In this embodiment, the prediction apparatus for dangerous driving may further include:
the grade acquisition module is used for acquiring the danger grade corresponding to the signal instruction under the condition of the first vehicle environment information;
and the stimulation module is used for performing biological stimulation on a driver of the vehicle according to the danger level.
In the embodiment of the present application, the prediction apparatus for dangerous driving may further include the following modules:
the preprocessing module is used for filtering and denoising the bioelectricity signal to obtain a target bioelectricity signal;
The extraction module is used for extracting the signal characteristics of the target bioelectrical signal;
correspondingly, the instruction determining module 402 may specifically include the following sub-modules:
and the target corresponding sub-module is used for determining a signal instruction corresponding to the target bioelectrical signal according to the signal characteristics of the target bioelectrical signal.
In this embodiment, the extraction module may specifically include the following sub-modules:
the decomposition submodule is used for decomposing the target bioelectricity signal and selecting a signal in a frequency range corresponding to the target rhythm wave as a signal component of the target bioelectricity signal;
a computation submodule for computing instantaneous energy values, boundary energy values and complexity of the signal components;
the instantaneous energy determination submodule is used for determining an instantaneous energy spectrum characteristic vector of the target bioelectrical signal according to the instantaneous energy value;
the boundary energy determining submodule is used for determining a boundary energy spectrum characteristic vector of the target bioelectricity signal according to the boundary energy value;
the complexity determining submodule is used for determining a complexity characteristic vector of the target bioelectricity signal according to the complexity of the signal component;
and the target characteristic determining submodule is used for determining a target characteristic vector corresponding to the target bioelectrical signal according to the instantaneous energy spectrum characteristic vector, the boundary energy spectrum characteristic vector and the complexity characteristic vector, and the target characteristic vector is used for representing the signal characteristic of the target bioelectrical signal.
In this embodiment, the instruction determining module 402 may specifically include the following sub-modules:
and the classification and identification submodule is used for classifying and identifying the signal characteristics by adopting a classifier and determining a signal instruction corresponding to the bioelectricity signal.
In the embodiment of the present application, the classification and identification subunit may specifically include the following units:
the projection unit is used for projecting the characteristic vectors corresponding to the signal characteristics to acquire the position information of each characteristic quantity in the characteristic vectors in a projection area, wherein the characteristic quantity refers to an element in the characteristic vectors, the projection area comprises at least two sub-areas, any two sub-areas in the at least two sub-areas are not overlapped, and one sub-area corresponds to one signal instruction;
and the target determining unit is used for acquiring the signal instruction corresponding to the target area according to the position information of each characteristic quantity, and determining that the signal instruction corresponding to the target area is the signal instruction corresponding to the bioelectricity signal, and the target area is the subarea with the largest number of characteristic quantities.
The prediction device for dangerous driving provided by the embodiment of the application can be applied to the method embodiments, and details are referred to the description of the method embodiments and are not repeated herein.
Fig. 5 is a schematic structural diagram of a terminal device according to a fifth embodiment of the present application. As shown in fig. 5, the terminal device 500 of this embodiment includes: at least one processor 510 (only one shown in fig. 5), a memory 520, and a computer program 521 stored in the memory 520 and executable on the at least one processor 510, the processor 510 implementing the steps in the above-described embodiments of the method for predicting dangerous driving when executing the computer program 521.
The terminal device 500 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 510, a memory 520. Those skilled in the art will appreciate that fig. 5 is only an example of the terminal device 500, and does not constitute a limitation to the terminal device 500, and may include more or less components than those shown, or combine some components, or different components, such as an input/output device, a network access device, and the like.
The Processor 510 may be a Central Processing Unit (CPU), and the Processor 510 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 520 may in some embodiments be an internal storage unit of the terminal device 500, such as a hard disk or a memory of the terminal device 500. The memory 520 may also be an external storage device of the terminal device 500 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 500. Further, the memory 520 may also include both an internal storage unit and an external storage device of the terminal device 500. The memory 520 is used for storing an operating system, an application program, a Boot Loader (Boot Loader), data, and other programs, such as program codes of the computer programs. The memory 520 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of 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 be in an electrical, mechanical or other form.
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 multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application 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 may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated module/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, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
When the computer program product runs on a terminal device, the terminal device can implement the steps in the method embodiments.
The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A prediction method of dangerous driving, characterized by comprising:
acquiring a bioelectricity signal acquired by a bioelectricity signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system, wherein the bioelectricity signal is a bioelectricity signal of a driver of a vehicle in a driving process, and the first vehicle environment information is environment information around a position where the vehicle is located;
Determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal, wherein the signal instruction refers to a vehicle driving action instruction intended by the driver in real time;
and if the first vehicle environment information is matched with the signal instruction, determining that the vehicle has a dangerous driving tendency, wherein the matching of the first vehicle environment information and the signal instruction means that under the condition of the first vehicle environment information, if the vehicle executes the signal instruction, the risk of traffic accidents exists.
2. The prediction method of claim 1, further comprising:
judging whether a mapping relation between the first vehicle environment information and the signal command exists in a corresponding relation, wherein the corresponding relation comprises mapping relations between M pieces of second vehicle environment information and N pieces of dangerous driving commands, M is an integer larger than zero, and N is an integer larger than zero;
if the mapping relation between the first vehicle environment information and the signal instruction exists in the corresponding relation, determining that the first vehicle environment information is matched with the signal instruction;
and if the mapping relation between the first vehicle environment information and the signal instruction does not exist in the corresponding relation, determining that the first vehicle environment information is not matched with the signal instruction.
3. The prediction method of claim 2, after determining that the vehicle has a dangerous driving tendency, further comprising:
acquiring a danger level corresponding to the signal instruction under the condition of the first vehicle environment information;
biostimulation is performed on a driver of the vehicle according to the hazard level.
4. The prediction method according to claim 1, wherein before determining the signal instruction corresponding to the bioelectric signal based on the signal characteristic of the bioelectric signal, the prediction method further comprises:
filtering and denoising the bioelectrical signal to obtain a target bioelectrical signal;
extracting signal characteristics of the target bioelectrical signal;
correspondingly, the determining the signal instruction corresponding to the bioelectrical signal according to the signal feature of the bioelectrical signal includes:
and determining a signal instruction corresponding to the target bioelectrical signal according to the signal characteristic of the target bioelectrical signal.
5. The prediction method of claim 4, wherein the extracting the signal feature of the target bioelectric signal comprises:
decomposing the target bioelectric signal, and selecting a signal in a frequency range corresponding to the target rhythm wave as a signal component of the target bioelectric signal;
Calculating an instantaneous energy value, a boundary energy value and a complexity of the signal component;
determining an instantaneous energy spectrum feature vector of the target bioelectrical signal according to the instantaneous energy value;
determining a boundary energy spectrum feature vector of the target bioelectrical signal according to the boundary energy value;
determining a complexity feature vector of the target bioelectrical signal according to the complexity of the signal component;
and determining a target feature vector corresponding to the target bioelectrical signal according to the instantaneous energy spectrum feature vector, the boundary energy spectrum feature vector and the complexity feature vector, wherein the target feature vector is used for representing the signal feature of the target bioelectrical signal.
6. The prediction method of claim 1, wherein determining the signal instruction corresponding to the bioelectric signal based on the signal characteristic of the bioelectric signal comprises:
and classifying and identifying the signal characteristics by adopting a classifier, and determining a signal instruction corresponding to the bioelectricity signal.
7. The prediction method of claim 6, wherein the classifying and identifying the signal features by using a classifier to obtain the signal instruction corresponding to the bioelectrical signal comprises:
Projecting the feature vectors corresponding to the signal features to obtain position information of each feature quantity in the feature vectors in a projection area, wherein the feature quantity refers to an element in the feature vectors, the projection area comprises at least two sub-areas, any two of the at least two sub-areas are not overlapped, and one sub-area corresponds to one signal instruction;
and acquiring a signal instruction corresponding to a target area according to the position information of each characteristic quantity, and determining that the signal instruction corresponding to the target area is the signal instruction corresponding to the bioelectrical signal, wherein the target area is a sub-area with the largest number of characteristic quantities.
8. A prediction apparatus for dangerous driving, characterized by comprising:
the acquisition module is used for acquiring a bioelectricity signal acquired by the bioelectricity signal acquisition device and first vehicle environment information acquired by a vehicle driving auxiliary system, wherein the bioelectricity signal refers to a bioelectricity signal of a driver of the vehicle in the driving process, and the first vehicle environment information refers to environment information around the position where the vehicle is located;
the instruction determining module is used for determining a signal instruction corresponding to the bioelectrical signal according to the signal characteristics of the bioelectrical signal, wherein the signal instruction refers to a vehicle driving action instruction intended by the driver in real time;
The prediction module is used for determining that the vehicle has a dangerous driving tendency if the first vehicle environment information is matched with the signal instruction, and the fact that the first vehicle environment information is matched with the signal instruction means that under the condition that the first vehicle environment information is matched with the signal instruction, the vehicle has a risk of traffic accidents if the vehicle executes the signal instruction.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in 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 7.
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