CN112735131A - Driving behavior diagnosis method and device and electronic equipment - Google Patents
Driving behavior diagnosis method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a driving behavior diagnosis method, a driving behavior diagnosis device and electronic equipment, wherein the driving behavior diagnosis method comprises the following steps: if a target object meeting preset simulation driving conditions is detected, displaying a virtual environment for the target object, and acquiring driving behaviors of the target object in the virtual environment for a real target vehicle; and diagnosing the normative of the driving behavior to generate a driving behavior diagnosis result of the target object. The invention can conveniently and effectively diagnose the normative of the driving behavior and provide reliable diagnosis results.
Description
Technical Field
The present invention relates to the field of driving behavior diagnosis technologies, and in particular, to a driving behavior diagnosis method, a driving behavior diagnosis device, and an electronic device.
Background
In the traffic field, drivers may have some irregular driving behaviors such as running red light, stopping over the line, riding fast lanes, reversely driving and the like during driving, thereby bringing about potential safety hazards. But the driving behavior of the driver cannot be better monitored in the prior art.
Disclosure of Invention
In view of the above, the present invention provides a driving behavior diagnosis method, a driving behavior diagnosis device, and an electronic device, which can conveniently and effectively diagnose the normative of the driving behavior and provide reliable diagnosis results.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a driving behavior diagnosis method, including: if a target object meeting preset simulation driving conditions is detected, displaying a virtual environment for the target object, and acquiring driving behaviors of the target object in the virtual environment for a real target vehicle; and diagnosing the normative of the driving behavior to generate a driving behavior diagnosis result of the target object.
In one embodiment, the preset simulated driving conditions include: the target object is correctly located on the driving position of the real target vehicle and/or the target object is correctly wearing the safety helmet.
In one embodiment, the method further comprises: detecting a pressure signal by a pressure sensor provided at a driving position of a real target vehicle; judging whether the pressure signal is greater than a preset pressure value or not; if so, the target object is determined to be correctly located at the driving position of the real target vehicle.
In one embodiment, the method further comprises: detecting whether a shielding object exists in the safety helmet or not through an infrared fence arranged in the safety helmet; if so, determining that the target object correctly wears the safety helmet; and/or detecting a temperature value in the safety helmet through a temperature sensor arranged in the safety helmet, and judging whether the temperature value is greater than a preset temperature value or not; if yes, determining that the target object correctly wears the safety helmet; and/or detecting whether a human body bioelectricity signal exists in the safety helmet through a bioelectricity detection device arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
In one embodiment, the step of obtaining the driving behavior of the target object in the virtual environment for the real target vehicle includes: collecting driving behavior data of a target object when the target object takes driving behavior aiming at a real target vehicle; and acquiring the driving behavior of the target object in the virtual environment aiming at the real target vehicle according to the driving behavior data.
In one embodiment, the step of diagnosing the normative of the driving behavior to generate the driving behavior diagnosis result of the target object includes: judging whether the driving behaviors have non-standard behaviors or not; wherein the irregular behaviors comprise one or more of crossing a line to stop, riding a fast lane, driving reversely and running a red light; if the non-standard behaviors exist, the target object is prompted by voice to correct the non-standard behaviors, and a picture of the non-standard behaviors is taken; and generating a driving behavior diagnosis result of the target object according to the picture of the irregular behavior and the acquired driving behavior of the target object.
In one embodiment, the method further comprises: and uploading the driving behavior diagnosis result to a server for storage.
In one embodiment, the virtual environment is constructed and rendered from real road scene images and/or 3D modeling techniques based on real road scene proportions.
In a second aspect, an embodiment of the present invention provides a driving behavior diagnosis apparatus including: the driving behavior acquisition module is used for displaying a virtual environment for a target object and acquiring the driving behavior of the target object in the virtual environment aiming at a real target vehicle if the target object meeting the preset simulation driving condition is detected; and the diagnosis module is used for diagnosing the normative of the driving behavior and generating a driving behavior diagnosis result of the target object.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a processor and a memory, where the memory stores computer-executable instructions capable of being executed by the processor, and the processor executes the computer-executable instructions to implement the steps of any one of the methods provided in the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of any one of the methods provided in the first aspect.
The embodiment of the invention has the following beneficial effects:
according to the driving behavior diagnosis method, the driving behavior diagnosis device and the electronic equipment provided by the embodiment of the invention, if the target object meeting the preset simulated driving condition is detected, the virtual environment is displayed for the target object, and the driving behavior of the target object in the virtual environment aiming at the real target vehicle is obtained; and diagnosing the normative of the driving behavior to generate a driving behavior diagnosis result of the target object. The method can firstly detect whether the target object which accords with the simulation condition exists, and if the target object which accords with the simulation driving condition is detected, the driving behavior of the target object which is adopted by the target object under the virtual environment aiming at the real target vehicle is obtained, the normalization of the driving behavior is diagnosed, and the driving behavior diagnosis result of the target object is generated, so that the normalization of the driving behavior can be conveniently and effectively diagnosed, and a reliable diagnosis result is provided.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a driving behavior diagnosis method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another driving behavior diagnostic method provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a driving behavior diagnosis apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The prior art can not meet the standard diagnosis of the driving behavior of a driver at present. Therefore, the driving behavior diagnosis method, the driving behavior diagnosis device and the electronic equipment provided by the embodiment of the invention can conveniently and effectively diagnose the normative of the driving behavior, provide reliable diagnosis results and have strong universality.
To facilitate understanding of the present embodiment, a driving behavior diagnosis method disclosed in the present embodiment is first described in detail, referring to a flowchart of the driving behavior diagnosis method shown in fig. 1, where the method may be executed by an electronic device and mainly includes the following steps S102 to S104:
step S102: and if the target object meeting the preset simulated driving condition is detected, displaying a virtual environment for the target object, and acquiring the driving behavior of the target object in the virtual environment for the real target vehicle.
In one embodiment, the virtual environment may be constructed and rendered by real road scene images and/or 3D modeling techniques according to real road scene proportions. Before simulating the driving behavior of the driver in the virtual environment, it is required to first detect whether there is a target object (i.e. the driver) that meets the preset simulated driving condition, when a target object meeting the preset simulated driving condition is detected, the virtual environment can be displayed for the target object through the display screen, then the driving behavior of the target object in the virtual environment aiming at the real target vehicle is obtained, the specific real target vehicle can be an electric bicycle conforming to the national standard 5, and can also be a real automobile, a bicycle and other vehicles, taking an electric bicycle as an example, the target object can be obtained by taking driving action on the real electric bicycle, and then, acquiring the driving behavior of the target object in real time by using main control software on the electric bicycle, uploading the driving behavior data to a workstation through a serial port communication technology, and simulating the received driving behavior by the workstation and displaying the driving behavior data to the target object through a display screen.
Step S104: and diagnosing the normative of the driving behavior to generate a driving behavior diagnosis result of the target object.
In an implementation manner, some irregular driving behaviors may exist in a driving process of a target object, and based on this, in this embodiment, normalization of the driving behaviors may be diagnosed in a simulation process, so as to generate a diagnosis result of the target object, the diagnosis result may include evaluation and scoring of the driving behaviors of the target object, and traffic safety awareness of the target object may be improved according to the diagnosis result.
The driving behavior diagnosis method provided by the embodiment of the invention can firstly detect whether the target object meeting the simulation condition exists, and if the target object meeting the simulation driving condition is detected, the driving behavior of the target object in the virtual environment aiming at the real target vehicle is obtained, the normalization of the driving behavior is diagnosed, and the driving behavior diagnosis result of the target object is generated, so that the normalization of the driving behavior can be conveniently and effectively diagnosed, a reliable diagnosis result is provided, and the universality is strong.
Further, the preset simulated driving conditions in this embodiment include: the target object is correctly located on the driving position of the real target vehicle and/or the target object is correctly wearing the safety helmet. Based on this, it can be detected that the target object is correctly located at the driving position of the real target vehicle according to the following steps 1 to 3:
step 1: the pressure signal is detected by a pressure sensor provided at a driving position of the real target vehicle.
Step 2: and judging whether the pressure signal is larger than a preset pressure value or not.
And step 3: if so, the target object is determined to be correctly located at the driving position of the real target vehicle.
In one embodiment, the target object may generate pressure on the driving position when the target object is located on the driving position of the real target vehicle, so that the pressure signal may be detected by a pressure sensor disposed at the driving position of the real target vehicle in this embodiment, and in order to avoid that the driving position generates the pressure signal only due to the object being placed, and the target object is not located at the driving position, the determination is made by setting a threshold value in this embodiment, that is, when the detected pressure signal is greater than a preset pressure value, it is determined that the target object is correctly located at the driving position of the real target vehicle.
After the target object is detected to be correctly located at the driving position of the real target vehicle, whether the target object correctly wears the safety helmet needs to be detected, so that the interference generated when the safety helmet is placed on the bracket is avoided, and the recognition rate of the safety helmet (helmet) worn by the target object is improved, the embodiment of the invention can detect whether the target object correctly wears the safety helmet through an infrared fence, a temperature sensor and/or a bioelectricity detection device, and the method specifically comprises the following three modes:
the first method is as follows: detecting whether a shielding object exists in the safety helmet or not through an infrared fence arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
The infrared fence is also called as an infrared railing or an infrared grating, is one of active infrared correlation, adopts a plurality of beams of infrared light correlation, the emitter emits infrared light to the receiver in a low-frequency emission and time division detection mode, and once personnel or objects block more than 30ms of light emitted by any two adjacent beams of the emitter, the receiver immediately outputs an alarm signal, so that whether shielding objects exist in the safety helmet or not can be detected through the infrared fence, and whether the target object correctly wears the safety helmet or not can be preliminarily judged. Specifically, if a shelter is detected in the safety helmet, the target object is determined to be correctly wearing the safety helmet.
The second method comprises the following steps: detecting a temperature value in the safety helmet through a temperature sensor arranged in the safety helmet, and judging whether the temperature value is greater than a preset temperature value or not; if so, the target object is determined to be wearing the safety helmet correctly.
Considering that the safety helmet is placed on the bracket, the infrared fence can be used for detecting that a shielding object is arranged in the safety helmet, so that interference is generated on a detection result, therefore, a temperature sensor arranged in the safety helmet can be used for detecting a temperature value in the safety helmet, and when the temperature value is larger than a preset temperature value, a target object is determined to correctly wear the safety helmet. In a specific application, the temperature sensor can be an infrared non-contact temperature sensor which can detect the temperature in the safety helmet but is ineffective to detect the bracket, so that various interferences can be effectively removed, and the recognition rate of the safety helmet worn by a person is improved.
The third method comprises the following steps: detecting whether a human body bioelectricity signal exists in the safety helmet through a bioelectricity detection device arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
Further, in order to distinguish whether the safety helmet is placed on the support or worn on the head of the target object, a human body bioelectricity detection mode can be adopted for distinguishing, and specifically, whether a human body bioelectricity signal exists in the safety helmet or not can be detected through a bioelectricity detection device arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
It is to be understood that, for the three determination manners, in a specific application, the determination may be performed in three manners at the same time, or may be performed in any combination of one or two manners, which is not limited herein.
In order to more accurately simulate the driving behavior of the driver, an embodiment of the present invention further provides a specific example of obtaining the driving behavior of the target object in the virtual environment for the real target vehicle, where the specific example includes: collecting driving behavior data of a target object when the target object takes driving behavior aiming at a real target vehicle; and acquiring the driving behavior of the target object in the virtual environment aiming at the real target vehicle according to the driving behavior data.
In an embodiment, the driving behavior of the target object may be steering, horn pressing, electric door twisting, front and back braking, and the like, and specifically, a sensor may be installed on a real target vehicle, such as an electric bicycle, and the sensor may collect an electric door signal of a driver when twisting an electric door, a brake signal when braking, a steering signal when steering, and the like, respectively, and then upload driving behavior data to a workstation through a serial port communication technology, and the workstation may process and model the received driving behavior data, and display a corresponding driving behavior in a virtual environment.
Further, taking the target vehicle as a non-motor vehicle as an example, the process modeling process for the driving behavior data may include the following steps a1 to a 6:
step a 1: and carrying out normalization processing on the driving behavior data acquired by the sensor.
In one embodiment, the electric gate signal, the braking signal and the steering signal can be normalized separately and converted into the range of [ -1,1 ].
Specifically, the electric gate signal may be normalized according to the following formula:
ESwitch＝(x-xmin)/(xmax-xmin)
wherein ESwitch represents a normalized value of the electric gate signal, x represents a value of the electric gate signal detected by the sensor while twisting the electric gate, xminRepresenting the maximum value, x, of the electric gate signalmaxRepresenting the minimum value of the electric gate signal.
Further, the braking signal includes left braking signal and right braking signal, corresponds back braking action and preceding braking action respectively, can carry out normalization processing to the braking signal according to following formula:
Brakel＝(y-ylmin)/(ylmax-ylmin)
Braker＝(y-yrmin)/(yrmax-yrmin)
Brake＝(Brakel+Braker)/2
wherein y represents the braking signal value detected by the sensor during braking, ylminIndicating the value of the brake signal, y, at the time of complete release of the left brakelmaxIndicating the value of the Brake signal at the end of the left Brake pinch, BrakelIs a normalized value of the left brake signal, yrminRepresenting the value of the braking signal, y, at the time of complete release of the right brakermaxRepresenting the value of the Brake signal at the time of the right Brake pinch to the end, BrakerThe normalized values of the right Brake signal are shown and the Brake indicates the normalized values of the total Brake signal.
Further, the steering signal may be normalized according to the following formula:
when z is<z0The method comprises the following steps: steering ═ z0-z)/(z0-zl)；
When z is more than or equal to z0The method comprises the following steps: steering ═ z (z-z)0)/(zr-z0)；
Where Steering represents the normalized value of the Steering signal, z represents the value of the Steering signal as monitored by the angle sensor during non-motor vehicle handlebar rotation, z0Angle sensor value, z, indicating the zero position of the handle of the non-motor vehicle (handle swing timing)lAngle sensor value, z, indicating the left extreme position of the non-motor vehicle handle (handle left-hand dead)rAngle sensor values indicating a non-motor vehicle handle right extreme position (handle dead right).
Step a 2: and determining the switch moment and the brake moment according to the normalized driving behavior data.
Specifically, the electric gate torque may be calculated according to the following formula:
wherein, TESwitchIndicating the electric gate moment.
Calculating the braking torque according to the following formula:
wherein, TBrakeIndicating the electric gate moment.
Step a 3: and determining the longitudinal angular velocity according to the electric door moment, the brake moment and the rotational inertia.
Specifically, the longitudinal angular velocity may be calculated according to the following formula:
Wvertical(t+dt)＝(TESwitch-TBrake)/I*dt+Wvertical(t)
wherein I represents the moment of inertia, according to which I ═ mR2Where m represents the non-motor vehicle front wheel tire mass, R represents the non-motor vehicle front wheel tire radius, and both m and R can be calculated from the real vehicle 1:1 is obtained by modeling, WverticalIndicating the longitudinal angular velocity.
Step a 4: and determining the linear speed of the target vehicle in the advancing direction according to the longitudinal angular speed.
Specifically, the linear velocity can be calculated according to the following formula:
V＝Wvertical(t+dt)*R
wherein V represents the linear velocity and R represents the radius of the front wheel tire of the non-motor vehicle.
Step a 5: and determining the yaw angle when the target vehicle turns according to the normalized steering signal.
Specifically, the yaw angle may be calculated according to the following formula:
where θ represents a yaw angle.
Step a 6: the driving behavior is simulated in the virtual environment according to the linear velocity and the yaw angle of the target vehicle forward direction.
In summary, the method provided by the embodiment of the invention can collect the driving behavior data of the driver when the driver takes the driving behavior aiming at the real target vehicle through the sensor, and model the driving behavior data, so that the driving behavior of the driver can be simulated more truly in the virtual environment, the non-standard behavior of the driver can be corrected in time, and the traffic safety civilization awareness of the driver can be improved.
For convenience of understanding, the embodiment of the present invention further provides a specific implementation manner of diagnosing the normative of the driving behavior and generating the driving behavior diagnosis result of the target object, that is, for the above step S104, the following steps (1) to (3) may be implemented:
step (1): and judging whether the driving behaviors have irregular behaviors or not.
Wherein the irregular behavior comprises one or more of crossing a line to stop, riding a fast lane, driving in reverse, and running a red light.
Step (2): and if the irregular behaviors exist, prompting the target object to correct the irregular behaviors through voice, and taking a picture of the irregular behaviors.
In specific application, if the irregular behaviors appear in the simulation process, the workstation can snapshot the irregular behaviors of the target object in the virtual environment, the target object is prompted by the unmanned aerial vehicle through voice in the virtual environment to correct the irregular behaviors in time, and the voice prompt is stopped after the target object corrects the irregular behaviors in time. Such as: when the driver has the behavior of running the red light, the information of 'you have run the red light and please correct in time' and the like can be reported through voice to remind the driver of the irregular behavior and correct in time, and the voice prompt is stopped after the driver corrects the irregular behavior in time.
And (3): and generating a driving behavior diagnosis result of the target object according to the picture of the irregular behavior and the acquired driving behavior of the target object.
In an implementation mode, the diagnosis result can be embodied in the form of a driving behavior report, the driving behavior report can include scores of driving behaviors of a target object in the whole simulation process, photos of non-standard behaviors can be attached, the report can provide experience feedback for the target object, the traffic safety civilization awareness of the target object is improved, the report can be used as a proof for checking whether the target object is a qualified non-motor vehicle driver or not and used as a pass before the target object is on the road, and meanwhile, the data can also be used for big data analysis and key treatment of the high-incidence non-standard behaviors.
In addition, the method provided by the embodiment of the invention can also upload the driving behavior diagnosis result to the server for storage. Data analysis can be performed based on the stored diagnosis results, and key treatment can be performed aiming at high-incidence irregular behaviors.
The driving behavior diagnosis method provided by the embodiment of the invention can firstly detect whether a target object meeting a simulation condition exists, and if the target object meeting the simulation driving condition is detected, the driving behavior of the target object in a virtual environment aiming at a real target vehicle is obtained, the normative of the driving behavior is diagnosed, and the driving behavior diagnosis result of the target object is generated, so that the driving behavior normative can be conveniently and effectively diagnosed, a reliable diagnosis result is provided, and the universality is strong; in addition, the embodiment of the invention can correct the error of the target object during the driving behavior simulation period, correct the irregular behavior of the target object in time and improve the traffic safety civilization awareness of the target personnel.
The embodiment of the present invention further provides another driving behavior diagnosis method, referring to a flowchart of another driving behavior diagnosis method shown in fig. 2, in which an electric bicycle conforming to national standard 5 is used as a real target vehicle, and driving behaviors of non-motor vehicle drivers are detected by main control software on the electric bicycle, and the method mainly includes the following steps S202 to S222:
step S202: and constructing and rendering a virtual environment based on the real road scene proportion through the real road scene image and/or the 3D modeling technology. Specifically, a real road scene image can be obtained by photographing, and then a virtual environment (i.e., a virtual scene) based on the real road scene is constructed and rendered in a ratio of 1:1 by the real road scene image and/or a 3D modeling technology.
Step S204: and detecting a pressure signal through a pressure sensor arranged at the driving position of the real target vehicle, and uploading the pressure signal to a workstation.
In one embodiment, the pressure sensor disposed at the driving position of the electric bicycle can detect the pressure signal and upload the pressure signal to the workstation, which can be a highly configured computer for storing various detected data during the driving behavior simulation process and monitoring the simulation process.
Step S206: judging whether the pressure signal is greater than a preset pressure value or not; if yes, go to step S208, otherwise return to step S204.
Specifically, whether the driver (target object) is correctly located at the driving position of the real target vehicle may be determined through the pressure signal, when the pressure sensor detects the pressure signal and the pressure signal is greater than the preset pressure value, it is determined that the driver is correctly located at the driving position of the real target vehicle, and the step S208 is continuously executed, otherwise, the step S204 is returned to continuously detect the pressure signal.
Step S208: and acquiring a safety helmet wearing signal, and uploading the safety helmet wearing signal to a workstation.
In one embodiment, the helmet wearing signal can be obtained through a detection device arranged in the helmet, the detection device can comprise an infrared fence, a temperature sensor and a bioelectricity detection device, and particularly, the infrared fence can detect the change of infrared light; and/or detecting a temperature value in the safety helmet through a temperature sensor; and/or detecting whether a human body bioelectric signal exists in the safety helmet through the bioelectric detection device, and uploading the obtained safety helmet wearing signal (such as an infrared signal, a temperature value, a human body bioelectric signal and the like) to a workstation.
Step S210: judging whether the driver correctly wears the safety helmet according to the safety helmet wearing signal; if yes, go on to step S212, otherwise return to step S208.
In one embodiment, whether a shielding object exists in the safety helmet or not can be judged through the change of the infrared signal of the infrared fence, namely whether a driver correctly wears the safety helmet or not is judged, when the shielding object exists in the safety helmet, the temperature value in the safety helmet is determined through the temperature sensor and/or whether a bioelectricity signal exists in the safety helmet or not is detected through the bioelectricity detection device, and when the temperature value is larger than a preset temperature value and/or the bioelectricity signal exists in the safety helmet, the driver correctly wears the safety helmet is determined.
Step S212: and displaying the virtual environment for the driver, and simulating the driving behavior of the driver in real time.
Specifically, the real-time simulation of steering, loudspeaker and switch twisting of a driver and the behaviors of front and rear brake lamps can be realized.
Step S214: judging whether the driver has irregular behaviors or not; if so, the process continues to step S216, otherwise, the process proceeds to step S220. The non-canonical behaviors include: off-line parking, fast lane riding, reverse driving, incorrect wearing of safety helmets, red light running, etc.
Step S216: and prompting the non-standard behaviors of the driver through voice, and acquiring a picture of the non-standard behaviors of the driver.
In a specific application, when the driver has irregular behaviors, the unmanned aerial vehicle voice prompt can be performed in a virtual environment, such as: when the driver has the behavior of running the red light, the information of 'you have run the red light and please correct in time' and the like can be reported through voice to remind the driver of the irregular behavior and correct in time, and the voice prompt is stopped after the driver corrects the irregular behavior in time. Meanwhile, the non-standard behaviors of the driver can be shot in the virtual environment, and the picture of the non-standard behaviors of the driver is obtained.
Step S218: and uploading the photos of the irregular behaviors of the driver to a workstation for storage and recording.
Step S220: and uploading the behavior data of the driver in the simulation process to a workstation.
Step S222: and generating a driving behavior report of the driver according to the behavior data.
Specifically, the driving behavior report can include the scoring of the driving behavior of the driver in the whole simulation process, and photos of the non-standard behavior can be attached to the driving behavior report, so that the report can provide experience feedback for the driver, promote the civilized awareness of the traffic safety of the driver, and can be used as a proof for checking whether the driver is qualified non-motor vehicle driver or not, and meanwhile, the data can be used for big data analysis to perform key treatment on the high-frequency non-standard behavior.
The driving behavior diagnosis method provided by the embodiment of the invention adopts a real electric bicycle to collect the driving behaviors of non-motor vehicle driving crowds, and compared with an immersive spinning system in the prior art, namely, the immersive spinning system realizes the dynamic scene experience based on a fitness scene by utilizing a virtual reality technology and an embedded technology, so that the effects of fitness and entertainment are achieved, the real driving behaviors or habits of non-motor vehicle drivers can be reflected better, a diagnosis report with higher credibility can be provided, and potential safety hazards are reduced; meanwhile, the embodiment of the invention adopts the detection scheme of the infrared fence and the temperature sensor to judge whether the driver correctly wears the safety helmet, so that the interference signal of simulating the helmet wearing behavior by the head of a non-human body can be effectively eliminated; in addition, the driving behaviors of the non-motor vehicle drivers are diagnosed and corrected in real time through a real scene of 1:1 and a real-time rule detection method, and online driving behavior reports of the non-motor vehicle drivers are formed, so that the traffic safety civilization awareness of the non-motor vehicle drivers can be improved.
As for the driving behavior diagnosis method provided by the foregoing embodiment, an embodiment of the present invention further provides a driving behavior diagnosis apparatus, referring to a schematic structural diagram of a driving behavior diagnosis apparatus shown in fig. 3, which may include the following parts:
the driving behavior obtaining module 301 is configured to, if a target object meeting a preset simulated driving condition is detected, display a virtual environment for the target object, and obtain a driving behavior of the target object in the virtual environment for a real target vehicle.
The diagnosis module 302 is configured to diagnose the normative of the driving behavior and generate a driving behavior diagnosis result of the target object.
The driving behavior diagnosis device provided by the embodiment of the invention can firstly detect whether a target object meeting the simulation condition exists, and if the target object meeting the simulation driving condition is detected, the driving behavior of the target object in the virtual environment aiming at the real target vehicle is obtained, the normative of the driving behavior is diagnosed, and the driving behavior diagnosis result of the target object is generated, so that the normative of the driving behavior can be conveniently and effectively diagnosed, and a reliable diagnosis result is provided.
In one embodiment, the preset simulated driving conditions include: the target object is correctly located on the driving position of the real target vehicle and/or the target object is correctly wearing the safety helmet. The device also comprises a driving condition detection module, a pressure detection module and a control module, wherein the driving condition detection module is used for detecting a pressure signal through a pressure sensor arranged at the driving position of the real target vehicle; judging whether the pressure signal is greater than a preset pressure value or not; if so, the target object is determined to be correctly located at the driving position of the real target vehicle.
In one embodiment, the driving condition detecting module is further configured to detect whether a shelter is present in the safety helmet through an infrared barrier disposed in the safety helmet; if so, determining that the target object correctly wears the safety helmet; and/or detecting a temperature value in the safety helmet through a temperature sensor arranged in the safety helmet, and judging whether the temperature value is greater than a preset temperature value or not; if yes, determining that the target object correctly wears the safety helmet; and/or detecting whether a human body bioelectricity signal exists in the safety helmet through a bioelectricity detection device arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
In one embodiment, the driving behavior obtaining module 301 is further configured to collect driving behavior data of a target object when the target object takes a driving behavior with respect to a real target vehicle; and acquiring the driving behavior of the target object in the virtual environment aiming at the real target vehicle according to the driving behavior data.
In one embodiment, the diagnostic module 302 is further configured to determine whether the driving behavior is irregular; wherein the irregular behaviors comprise one or more of crossing a line to stop, riding a fast lane, driving reversely and running a red light; if the non-standard behaviors exist, the target object is prompted by voice to correct the non-standard behaviors, and a picture of the non-standard behaviors is taken; and generating a driving behavior diagnosis result of the target object according to the picture of the irregular behavior and the acquired driving behavior of the target object.
In one embodiment, the device further includes an uploading module, configured to upload the driving behavior diagnosis result to a server for storage.
In one embodiment, the virtual environment in the device is constructed and rendered by real road scene images and/or 3D modeling technology according to real road scene proportions.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.
The embodiment of the invention also provides electronic equipment, which specifically comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the above embodiments.
Fig. 4 is a schematic structural diagram of an electronic device 100 according to an embodiment of the present invention, where the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected through the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The Memory 41 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
The bus 42 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The memory 41 is used for storing a program, the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40, or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 40. The Processor 40 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the method in combination with the hardware thereof.
The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the foregoing method embodiment, which is not described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A driving behavior diagnosis method characterized by comprising:
if a target object meeting preset simulated driving conditions is detected, displaying a virtual environment for the target object, and acquiring driving behaviors of the target object in the virtual environment for a real target vehicle;
and diagnosing the normativity of the driving behavior to generate a driving behavior diagnosis result of the target object.
2. The method of claim 1, wherein the preset simulated driving conditions comprise: the target object is correctly located on the driving position of the real target vehicle and/or the target object is correctly wearing a safety helmet.
3. The method of claim 2, further comprising:
detecting a pressure signal by a pressure sensor provided at a driving position of the real target vehicle;
judging whether the pressure signal is greater than a preset pressure value or not;
and if so, determining that the target object is correctly positioned at the driving position of the real target vehicle.
4. The method of claim 2, further comprising:
detecting whether a shielding object exists in the safety helmet through an infrared fence arranged in the safety helmet; if so, determining that the target object correctly wears a safety helmet;
and/or the presence of a gas in the gas,
detecting a temperature value in the safety helmet through a temperature sensor arranged in the safety helmet, and judging whether the temperature value is greater than a preset temperature value or not; if yes, determining that the target object correctly wears a safety helmet;
and/or the presence of a gas in the gas,
detecting whether a human body bioelectric signal exists in the safety helmet through a bioelectric detection device arranged in the safety helmet; and if so, determining that the target object correctly wears the safety helmet.
5. The method of claim 1, wherein the step of obtaining the driving behavior of the target object in the virtual environment with respect to the real target vehicle comprises:
collecting driving behavior data of the target object when the target object takes driving behavior aiming at a real target vehicle;
and acquiring the driving behavior of the target object aiming at the real target vehicle in the virtual environment according to the driving behavior data.
6. The method according to claim 1, wherein the step of diagnosing the normative of the driving behavior to generate the driving behavior diagnosis result of the target object includes:
judging whether the driving behaviors have irregular behaviors or not; wherein the irregular behavior comprises one or more of an off-line stop, a fast-riding lane, a reverse drive, and a red light running;
if the irregular behaviors exist, prompting the target object to correct the irregular behaviors through voice, and taking a picture of the irregular behaviors;
and generating a driving behavior diagnosis result of the target object according to the picture of the irregular behavior and the acquired driving behavior of the target object.
7. The method of claim 1, wherein the virtual environment is constructed and rendered from real road scene images and/or 3D modeling techniques based on real road scene proportions.
8. A driving behavior diagnosis device characterized by comprising:
the driving behavior acquisition module is used for displaying a virtual environment for a target object and acquiring the driving behavior of the target object in the virtual environment for a real target vehicle if the target object meeting preset simulated driving conditions is detected;
and the diagnosis module is used for diagnosing the normative of the driving behavior and generating a driving behavior diagnosis result of the target object.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to perform the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006044086A1 (en) * | 2006-09-20 | 2008-04-10 | Audi Ag | Traffic situation e.g. accident-critical driving situation, simulating system, has reference vehicle, where behavior of vehicle temporally takes place based on information detected by detection unit during simulated traffic situation |
| CN103303196A (en) * | 2013-06-17 | 2013-09-18 | 开平市中铝实业有限公司 | Vehicle passenger presence sensing system |
| CN104605820A (en) * | 2015-01-21 | 2015-05-13 | 清华大学 | Driver risk preference behavior diagnosis system |
| TWM516331U (en) * | 2015-11-06 | 2016-02-01 | Yu-Hsuan Sun | Device with automatic safety detection |
| CN106647420A (en) * | 2017-02-22 | 2017-05-10 | 穆特科技（武汉）股份有限公司 | Dynamic seat control method and control system for household entertainment |
| CN107569819A (en) * | 2017-11-02 | 2018-01-12 | 泉州创景视迅数字科技有限公司 | A kind of bicycle virtual emulation riding simulation system and its application method |
| CN108766098A (en) * | 2018-07-26 | 2018-11-06 | 杭州拓叭吧科技有限公司 | A kind of generation system of drive simulating environment and road |
| CN109887372A (en) * | 2019-04-16 | 2019-06-14 | 北京中公高远汽车试验有限公司 | Driving training analogy method, electronic equipment and storage medium |
| TWI670022B (en) * | 2018-05-26 | 2019-09-01 | 南開科技大學 | Two-wheeled motor vehicle safety helmet wear detection interlocking device |
| CN209984854U (en) * | 2019-05-28 | 2020-01-24 | 南昌泰豪虚拟现实科技有限公司 | Spinning that multi-functional VR was experienced |
| CN111700334A (en) * | 2020-06-16 | 2020-09-25 | 唐延智 | Safety helmet wearing detection device based on electroencephalogram |
| CN111724650A (en) * | 2020-07-21 | 2020-09-29 | 青岛海科虚拟现实研究院 | Motor car simulation teaching display device based on VR technique |
-
2020
- 2020-12-28 CN CN202011584428.7A patent/CN112735131A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006044086A1 (en) * | 2006-09-20 | 2008-04-10 | Audi Ag | Traffic situation e.g. accident-critical driving situation, simulating system, has reference vehicle, where behavior of vehicle temporally takes place based on information detected by detection unit during simulated traffic situation |
| CN103303196A (en) * | 2013-06-17 | 2013-09-18 | 开平市中铝实业有限公司 | Vehicle passenger presence sensing system |
| CN104605820A (en) * | 2015-01-21 | 2015-05-13 | 清华大学 | Driver risk preference behavior diagnosis system |
| TWM516331U (en) * | 2015-11-06 | 2016-02-01 | Yu-Hsuan Sun | Device with automatic safety detection |
| CN106647420A (en) * | 2017-02-22 | 2017-05-10 | 穆特科技（武汉）股份有限公司 | Dynamic seat control method and control system for household entertainment |
| CN107569819A (en) * | 2017-11-02 | 2018-01-12 | 泉州创景视迅数字科技有限公司 | A kind of bicycle virtual emulation riding simulation system and its application method |
| TWI670022B (en) * | 2018-05-26 | 2019-09-01 | 南開科技大學 | Two-wheeled motor vehicle safety helmet wear detection interlocking device |
| CN108766098A (en) * | 2018-07-26 | 2018-11-06 | 杭州拓叭吧科技有限公司 | A kind of generation system of drive simulating environment and road |
| CN109887372A (en) * | 2019-04-16 | 2019-06-14 | 北京中公高远汽车试验有限公司 | Driving training analogy method, electronic equipment and storage medium |
| CN209984854U (en) * | 2019-05-28 | 2020-01-24 | 南昌泰豪虚拟现实科技有限公司 | Spinning that multi-functional VR was experienced |
| CN111700334A (en) * | 2020-06-16 | 2020-09-25 | 唐延智 | Safety helmet wearing detection device based on electroencephalogram |
| CN111724650A (en) * | 2020-07-21 | 2020-09-29 | 青岛海科虚拟现实研究院 | Motor car simulation teaching display device based on VR technique |
Non-Patent Citations (1)
| Title |
|---|
| 井行学车: "电动自行车", 《微博》 * |
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