CN113085872B - Driving behavior evaluation method, device, equipment and storage medium - Google Patents
Driving behavior evaluation method, device, equipment and storage medium Download PDFInfo
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- CN113085872B CN113085872B CN202110445809.5A CN202110445809A CN113085872B CN 113085872 B CN113085872 B CN 113085872B CN 202110445809 A CN202110445809 A CN 202110445809A CN 113085872 B CN113085872 B CN 113085872B
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- 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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Abstract
The invention discloses a driving behavior evaluation method, a driving behavior evaluation device, driving behavior evaluation equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting the gravity acceleration corresponding to a mobile terminal from the sensor reading information corresponding to the mobile terminal on a moving target vehicle; determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration; when the relative state is a static state, the reading information of the sensor is input into the first driving behavior recognition model to evaluate the driving behavior of the driver, the defect that hardware equipment needs to be additionally arranged on the vehicle to evaluate the driving behavior is overcome, the driving behavior of the driver is evaluated by means of the sensor arranged in the mobile terminal, the driving behavior evaluation accuracy is guaranteed, and meanwhile, the driving behavior evaluation cost is greatly reduced. In addition, the invention also relates to a block chain technology, and the sensor reading information can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a driving behavior evaluation method, a driving behavior evaluation device, driving behavior evaluation equipment and a storage medium.
Background
Automobile driving has important significance to life and social economy of people, however, a few drivers with an unconscious driving habit have serious harm to themselves and other people, and are easy to cause accidents due to violent driving behaviors such as frequent emergency braking, rapid turning, frequent lane change and the like.
The driving behavior habit of a driver is graded by installing additional hardware equipment on a vehicle, but the installation process of the hardware equipment is complex and the cost is high.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a driving behavior evaluation method, a driving behavior evaluation device and a storage medium, and aims to solve the technical problem that in the prior art, extra hardware equipment needs to be installed on a vehicle for evaluating the driving behavior of a driver, so that the cost is high.
In order to achieve the above object, the present invention provides a driving behavior evaluation method, including the steps of:
when a mobile terminal is positioned on a moving target vehicle, acquiring sensor reading information corresponding to the mobile terminal on the target vehicle;
extracting the gravity acceleration corresponding to the mobile terminal from the sensor reading information;
determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration;
when the relative state is a static state, inputting the reading information of the sensor into a first driving behavior recognition model, and acquiring a first driving behavior recognition result output by the first driving behavior recognition model;
and evaluating the driving behavior of the driver according to the first driving behavior recognition result.
Optionally, before obtaining sensor reading information corresponding to the mobile terminal on a target vehicle when the mobile terminal is on the moving target vehicle, the method further includes:
acquiring the running state of a vehicle navigation function in the mobile terminal;
extracting a GPS speed corresponding to the mobile terminal from the sensor reading information, and comparing the GPS speed with a preset speed;
and when the vehicle navigation function is in an on state or the GPS speed is greater than the preset speed, judging that the mobile terminal is on a moving target vehicle, and storing the reading information of the sensor in a block chain.
Optionally, the determining a relative state between the mobile terminal and the target vehicle according to the gravitational acceleration includes:
decomposing the gravitational acceleration into gravitational acceleration components in a plurality of directions;
acquiring a gravity acceleration difference value between a gravity acceleration component at any moment in any direction and a gravity acceleration component at the previous moment;
and comparing the gravity acceleration difference with a preset difference threshold, and determining the relative state between the mobile terminal and the target vehicle according to the difference comparison result.
Optionally, after determining the relative state between the mobile terminal and the target vehicle according to the gravitational acceleration, the method further includes:
when the relative state is a motion state, controlling a camera device on the target vehicle to shoot so as to obtain the in-vehicle image information of the target vehicle;
extracting driving behavior image information corresponding to the driver from the in-vehicle image information based on human motion characteristics;
inputting the driving behavior image information into a second driving behavior recognition model, and acquiring a second driving behavior recognition result output by the second driving behavior recognition model;
and evaluating the driving behavior of the driver according to the second driving behavior recognition result.
Optionally, before the evaluating the driving behavior of the driver according to the first driving behavior recognition result, the method further includes:
acquiring the driving behavior of the driver according to the first driving behavior recognition result;
acquiring surrounding environment information of the target vehicle;
judging whether the driving behavior of the driver belongs to normal driving behavior or not according to the surrounding environment information;
and when the driving behavior belongs to normal driving behavior, executing the step of evaluating the driving behavior of the driver according to the first driving behavior recognition result.
Optionally, after determining whether the driving behavior of the driver belongs to a normal driving behavior according to the ambient environment information, the method further includes:
when the driving behavior belongs to abnormal driving behavior, acquiring reference driving behaviors of other drivers around the target vehicle through Internet of vehicles communication, and judging the reference driving behaviors of the other drivers;
when the reference driving behaviors of other drivers belong to abnormal behaviors, judging that a traffic obstacle exists in the road environment in front of the target vehicle;
and recording position information and time information corresponding to the passing barriers, sending the position information and the time information to other vehicles around the target vehicle through the Internet of vehicles communication, and outputting corresponding danger prompt information.
Optionally, the evaluating the driving behavior of the driver according to the first driving behavior recognition result includes:
determining the driving behavior type of the driver according to the first driving behavior recognition result;
identifying target driving behaviors according to the driving behavior types;
acquiring the occurrence frequency of the target driving behavior within a preset time period;
and determining the occurrence frequency corresponding to the target driving behavior according to the preset time period and the occurrence frequency, and evaluating the driving behavior of the driver according to the occurrence frequency.
Further, to achieve the above object, the present invention also proposes a driving behavior evaluation device including:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring sensor reading information corresponding to a mobile terminal on a target vehicle when the mobile terminal is on the moving target vehicle;
the extraction module is used for extracting the gravity acceleration corresponding to the mobile terminal from the sensor reading information;
the judging module is used for determining the relative state between the mobile terminal and the target vehicle according to the gravity acceleration;
the input module is used for inputting the reading information of the sensor to a first driving behavior recognition model when the relative state is a static state, and acquiring a first driving behavior recognition result output by the first driving behavior recognition model;
and the evaluation module is used for evaluating the driving behavior of the driver according to the first driving behavior recognition result.
Further, to achieve the above object, the present invention also proposes a driving behavior evaluation apparatus including: a memory, a processor and a driving behaviour assessment program stored on the memory and executable on the processor, the driving behaviour assessment program being configured to implement the steps of the driving behaviour assessment method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having stored thereon a driving behavior evaluation program which, when executed by a processor, implements the steps of the driving behavior evaluation method as described above.
The method comprises the steps of extracting the gravity acceleration corresponding to a mobile terminal from the sensor reading information corresponding to the mobile terminal on a moving target vehicle; determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration; when the relative state is a static state, the reading information of the sensor is input into the first driving behavior recognition model to evaluate the driving behavior of the driver, and the driving behavior of the driver is evaluated by the aid of the sensor in the mobile terminal, so that the driving behavior evaluation accuracy is guaranteed, and meanwhile, the driving behavior evaluation cost is greatly reduced.
Drawings
Fig. 1 is a schematic structural diagram of a driving behavior evaluation device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a driving behavior assessment method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a driving behavior assessment method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a driving behavior assessment method according to a third embodiment of the present invention;
fig. 5 is a block diagram showing the structure of the driving behavior evaluation apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a driving behavior evaluation device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the driving behavior evaluation apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the driving behavior evaluation apparatus, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a driving behavior evaluation program.
In the driving behavior evaluation device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the driving behavior evaluation apparatus of the present invention may be provided in the driving behavior evaluation apparatus, which calls the driving behavior evaluation program stored in the memory 1005 through the processor 1001 and executes the driving behavior evaluation method provided by the embodiment of the present invention.
An embodiment of the present invention provides a driving behavior evaluation method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of a driving behavior evaluation method according to the present invention.
In this embodiment, the driving behavior evaluation method includes the steps of:
step S10: when the mobile terminal is located on a moving target vehicle, acquiring sensor reading information corresponding to the mobile terminal on the target vehicle.
It should be noted that the execution subject of the embodiment may be an in-vehicle terminal, and may also be another device having the same or similar function. In this embodiment, the vehicle-mounted terminal is disposed on the target vehicle, and the vehicle-mounted terminal is in a fixed state on the target vehicle, and the vehicle-mounted terminal does not move due to the behavior operation of the user, and the mobile terminal moves along with the behavior operation of the user, and the mobile terminal includes sensors such as an acceleration sensor, a gyroscope, and a GPS, where the mobile terminal may be a smart phone, and this embodiment does not limit this. Further, the vehicle-mounted terminal of this embodiment may be configured to obtain sensor readings of an acceleration sensor, a gyroscope, and a GPS in the mobile terminal, and perform data processing and analysis on the obtained sensor readings, so as to evaluate driving behaviors of a driver, and the vehicle-mounted terminal may also be configured to detect a relative state between the mobile terminal and a vehicle, for example, a relative stationary state or a relative moving state between the mobile terminal and the vehicle. It is emphasized that the sensor reading information may also be stored in a node of a block chain in order to further ensure the privacy and security of the sensor reading information.
In a specific implementation, the in-vehicle terminal may communicate with the mobile terminal to obtain sensor reading information of the mobile terminal, and the in-vehicle terminal may communicate with the mobile terminal in a wired connection manner or a wireless connection manner, where the wired connection manner includes but is not limited to a data line connection or a network line connection, the wireless connection manner includes but is not limited to a wireless network connection manner or a bluetooth connection manner, and the specific connection manner may be set according to actual requirements, which is not limited in this embodiment.
It should be understood that, when the driving behavior of the driver is evaluated through the sensor data of the mobile terminal, the evaluation is based on the premise that the mobile terminal is in a motion state, and the motion state of the mobile terminal is caused by the driving behavior of the driver, because the motion state of the mobile terminal represents the motion state of the target vehicle in this embodiment, and then the driving behavior of the driver on the target vehicle is evaluated according to the reading information of the sensor in the mobile terminal, in order to make the acquired sensor data of the mobile terminal more accurate and reasonable, the motion state of the mobile terminal may be detected first, whether the mobile terminal is on the moving target vehicle may be judged, and then the relative state between the mobile terminal and the target vehicle may be detected, so that the motion state of the mobile terminal may more accurately represent the motion state of the target vehicle.
In a specific implementation, in order to accurately detect whether the mobile terminal is located on a moving target vehicle, before step S10 in this embodiment, the method further includes: acquiring the running state of a vehicle navigation function in the mobile terminal; extracting a GPS speed corresponding to the mobile terminal from the sensor reading information, and comparing the GPS speed with a preset speed; and when the vehicle navigation function is in an on state or the GPS speed is greater than the preset speed, judging that the mobile terminal is on a moving target vehicle.
It should be noted that, in this embodiment, a GPS sensor in the mobile terminal is used to obtain a GPS speed corresponding to the mobile terminal, and then whether the mobile terminal is in a moving vehicle is determined according to the GPS speed, a GPS speed may be preset, where the preset speed represents a maximum speed of a moving body during running under a general condition, and the preset speed may be set according to an actual condition. After the preset speed is set, comparing the acquired GPS speed corresponding to the mobile terminal with the preset speed, if the GPS speed corresponding to the mobile terminal is less than or equal to the preset speed, judging that the mobile terminal is not on a moving vehicle, and if the GPS speed corresponding to the mobile terminal is greater than the preset speed, judging that the mobile terminal is on the moving vehicle. In this embodiment, the GPS speed corresponding to the mobile terminal is extracted from the sensor reading information, and the operating state of the vehicle navigation function in the mobile terminal is also acquired, so that it can be determined that the mobile terminal is in a moving vehicle by acquiring the operating state of the vehicle navigation. Further, in the embodiment, when the vehicle navigation function is in the on state or the GPS speed is greater than the preset speed, it is determined that the mobile terminal is on the moving target vehicle. It should be emphasized that, in order to further ensure the privacy and security of the sensor reading information, the sensor reading information may also be stored in a node of a block chain.
Step S20: and extracting the gravity acceleration corresponding to the mobile terminal from the sensor reading information.
It should be noted that, in this embodiment, the motion state of the vehicle is represented by the motion state of the mobile terminal, and in an actual situation, there may be a situation that a user moves the mobile terminal, so that the motion state of the mobile terminal is changed by a manual operation, if it is obviously unreasonable to use the motion state of the mobile terminal to represent the motion state of the target vehicle at this time, in this embodiment, in order to enable the motion state of the mobile terminal to better represent the motion state of the target vehicle, after the mobile terminal is determined to be on the moving target vehicle, the gravitational acceleration corresponding to the mobile terminal is further obtained through the gravitational acceleration sensor in the mobile terminal, and whether the motion state of the mobile terminal fluctuates greatly is determined according to the gravitational acceleration, so as to avoid interference of the human factor on the motion state of the mobile terminal, specifically, in this embodiment, the gravitational acceleration corresponding to the mobile terminal can be extracted from the sensor reading information,
step S30: and determining the relative state between the mobile terminal and the target vehicle according to the gravity acceleration.
It should be noted that after the gravitational acceleration is obtained, the fluctuation range of the gravitational acceleration may be detected, if the gravitational acceleration does not fluctuate by a relatively large amount, it may be determined that the mobile terminal and the target vehicle are in a relatively stationary state, and if the gravitational acceleration fluctuates by a relatively large amount, it may be determined that the mobile terminal and the target vehicle are in a relatively moving state.
In a specific implementation, in order to accurately determine the relative state between the mobile terminal and the target vehicle, the step S30 in this embodiment includes: decomposing the gravitational acceleration into gravitational acceleration components in a plurality of directions; acquiring a gravity acceleration difference value between a gravity acceleration component at any moment in any direction and a gravity acceleration component at the previous moment; and comparing the gravity acceleration difference with a preset difference threshold, and determining the relative state between the mobile terminal and the target vehicle according to the difference comparison result.
It should be noted that the gravitational acceleration has directionality, and there may be movement in a single direction when the mobile terminal is moved by manual operation, for example, a driver shakes the mobile phone left and right, at this time, the motion state of the mobile terminal in the vertical direction does not fluctuate too much, but the motion state of the mobile terminal in the horizontal direction is in a violent shaking state, which certainly does not represent that the vehicle is also in a violent shaking state, and in order to determine the motion state of the mobile terminal more accurately, in this embodiment, after the gravitational acceleration is obtained, the gravitational acceleration is decomposed into gravitational acceleration components in multiple directions, for example, the gravitational acceleration is decomposed into a gravitational acceleration component in the X direction, a gravitational acceleration component in the Y direction, and a gravitational acceleration component in the Z direction.
It should be noted that, if the mobile terminal is in a relatively stationary state with the vehicle, the motion of the mobile terminal at this time may be used to represent the motion state of the vehicle, and the determination of the mobile terminal may be made by determining whether the gravitational acceleration component in any direction suddenly increases or decreases greatlyWhether the mobile terminal and the target vehicle are in a relatively static state or not is determined, for example, if the acceleration of gravity in the X direction, the acceleration of gravity in the Y direction, or the acceleration of gravity in the Z direction suddenly and greatly increases or decreases, the mobile terminal and the target vehicle are not in a relatively static state. And determine whether there is a situation of substantial increase or decrease, in this embodiment, the gravity acceleration difference between the gravity acceleration component at the moment and the gravity acceleration component at the previous moment may be used as a basis. Assuming that any direction is X direction, the time T is obtained 1 Acceleration of gravity in the X direction is ax 1 Last time T 2 Acceleration of gravity in the X direction is ax 2 Then time T 1 And the last moment T 2 The difference between the gravity accelerations is ax 1 -ax 2 。
In specific implementation, after the gravity acceleration difference is obtained, a difference threshold is further preset in this embodiment, whether the gravity acceleration in a certain direction has a sudden change or not is finally determined through the difference threshold, if the gravity acceleration difference is smaller than the preset difference threshold, it indicates that the gravity acceleration has no sudden change, and if the gravity acceleration difference is greater than or equal to the preset difference threshold, it indicates that the gravity acceleration has no sudden change, for example, the obtaining time T is 1 Acceleration of gravity in the X direction is ax 1 Last moment T 2 Acceleration of gravity in the X direction is ax 2 Then time T 1 And the last moment T 2 The difference between the gravity accelerations is ax 1 -ax 2 Assume ax 1 -ax 2 If the difference value is smaller than the preset difference value threshold value, the situation that the gravity acceleration component in the X direction does not change suddenly is shown, and it can be judged that the mobile terminal and the target vehicle are in a relatively static state in the X direction at the moment, otherwise, if ax is 1 -ax 2 If the difference value is greater than the preset difference value threshold, it indicates that the gravity acceleration component in the X direction changes suddenly, and it may be determined that the mobile terminal and the target vehicle are not in a relatively stationary state in the X direction at this time. Further, when it is determined that the mobile terminal and the vehicle are in a relatively stationary state, the motion state of the mobile terminal can be used to characterize the motion of the vehicleAnd at the moment, the acquired parameters of the mobile terminal, such as the gravity acceleration, the gyroscope data, the GPS speed and the like, can represent the vehicle running parameters when the vehicle is in a motion state. In addition, the resolution mode of the gravitational acceleration and the setting of the preset difference threshold may also be adjusted according to actual requirements, which is not limited in this embodiment.
Step S40: and when the relative state is a static state, inputting the reading information of the sensor into a first driving behavior recognition model, and acquiring a first driving behavior recognition result output by the first driving behavior recognition model.
It should be noted that, in the present embodiment, the driving behavior of the driver is evaluated based on the first driving behavior recognition model, where the first driving behavior recognition model is stored in the vehicle-mounted terminal, and the first driving behavior recognition model includes the corresponding relationship between the sensor reading information and the driving behavior of the driver, and the vehicle-mounted terminal can input the sensor reading information into the built-in processor in the vehicle-mounted terminal to be processed by the first driving behavior recognition model. In addition, it should be further noted that the first driving behavior recognition model may adopt a deep learning model, may also adopt a big data analysis model, and is obtained by training through a training sample set, and may also adopt models in other forms, and may be set according to actual evaluation requirements, which is not limited in this embodiment.
It should be noted that the first driving behavior recognition model is used for recognizing the driving behavior of the driver, and the first driving behavior recognition model includes a corresponding relationship between the sensor reading information and the driving behavior, so that after the sensor reading information is input to the first driving behavior recognition model, the driving behavior corresponding to the input sensor reading information can be determined according to the corresponding relationship, and then the driving behavior identification corresponding to each driving behavior is recognized by a classifier in the first driving behavior recognition model, and each driving behavior and the corresponding driving behavior action identification are the first driving behavior recognition result.
It should be noted that, before the sensor reading information is input into the first driving behavior recognition model, the first driving behavior recognition model needs to be constructed, in this embodiment, the first driving behavior recognition model is constructed based on the historical driving behavior data of the driver and the historical sensor reading information, and in order to improve the accuracy of the first driving behavior recognition model, the historical driving behavior data needs to be under the same timestamp with the historical sensor reading information, that is, the historical driving behavior data and the historical sensor reading information at the same time are acquired.
In a specific implementation, after obtaining the historical driving behavior data and the historical sensor reading information, historical driving behaviors contained in the historical driving behavior data and historical sensor readings contained in the historical sensor reading information may be used as a training sample set, in this embodiment, the historical driving behaviors and the historical sensor readings in a period of time may be used as the training sample set according to timestamps corresponding to the historical driving behavior data and the historical sensor reading information, for example, the historical driving behaviors and the historical sensor readings between time D and time F may be used as the training sample set, or the historical driving behaviors and the historical sensor readings between time Q and time W may be used as the training sample set, which may be changed according to an actual situation, which is not limited in this implementation.
In specific implementation, in order to make the training sample set better meet training requirements, in this embodiment, the historical sensor reading information may be divided into multiple historical sensor reading samples according to preset parameters, corresponding historical driving behavior data is determined according to a timestamp corresponding to each sensor reading sample, a driving behavior identifier is added to each historical driving behavior data, and each sensor reading sample and the corresponding driving behavior identifier are used as the training sample set.
It should be noted that the preset parameter may be set to be a 1 second step, the historical sensor reading information is divided into a plurality of historical sensor reading samples according to the 1 second step, and then historical driving behavior data corresponding to the sensor reading is obtained, where the historical driving behavior data includes a specific driving behavior of the driver, and similarly, in order to improve the accuracy of the first driving behavior recognition model, it is necessary to ensure that the historical sensor reading samples and the historical driving behavior data are under the same timestamp. After the driving behavior data is obtained, corresponding driving behavior identifiers are set for driving behaviors in the driving behavior data, for example, the driving behavior identifier of an aggressive driving behavior is set as B, and the driving behavior identifier of a normal driving behavior is set as N.
In specific implementation, data meeting a preset condition is screened from a training sample and input into a preset neural network model, the data meeting the preset condition may be data with shapes of 8 channels and 60 points, and then the data with the shapes of 8 channels and 60 points are input into the preset neural network model for training, so as to obtain a first driving behavior recognition model, wherein the preset condition and the preset neural network model may be set correspondingly according to actual evaluation requirements, which is not limited in this embodiment.
Step S50: and evaluating the driving behavior of the driver according to the first driving behavior recognition result.
In a specific implementation, the driving behavior recognition includes a driving behavior of the driver recognized by the first driving behavior recognition model, and it is emphasized that the recognized driving behavior includes a plurality of different types of driving behaviors, such as a normal driving behavior or an aggressive driving behavior, instead of one driving behavior. The driving behavior flag may be set according to the actual evaluation requirement, which is not limited in this embodiment.
In the embodiment, the gravity acceleration corresponding to the mobile terminal is extracted from the sensor reading information corresponding to the mobile terminal on the moving target vehicle; determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration; when the relative state is a static state, the reading information of the sensor is input into the first driving behavior recognition model to evaluate the driving behavior of the driver, and the driving behavior of the driver is evaluated by the aid of the sensor in the mobile terminal, so that the driving behavior evaluation accuracy is guaranteed, and meanwhile, the driving behavior evaluation cost is greatly reduced.
Referring to fig. 3, fig. 3 is a flowchart illustrating a driving behavior evaluation method according to a second embodiment of the present invention.
Based on the first embodiment, the driving behavior evaluation method of the present embodiment further includes, after the step S30:
step S301: and when the relative state is a motion state, controlling a camera device on the target vehicle to shoot so as to obtain the in-vehicle image information of the target vehicle.
It should be noted that, when the mobile terminal and the target vehicle are in the relative motion state, the motion state of the target vehicle cannot be represented by the motion state of the mobile terminal, and in this embodiment, the driving behavior of the driver may be photographed to evaluate the driving behavior. The target vehicle is provided with an imaging device, for example, a vehicle-mounted camera or other equipment, and when the relative state is a motion state, a shooting instruction is sent to the imaging device, and when the imaging device receives the shooting instruction, the environment in the target vehicle is shot to obtain the image information in the vehicle.
Step S302: and extracting the driving behavior image information corresponding to the driver from the in-vehicle image information based on the human motion characteristics.
It is easy to understand that the in-vehicle image information captured by the camera device includes image information of various moving objects, and the driving behavior evaluation is directed at the driving behavior of the driver, in this embodiment, the driving behavior image information corresponding to the driver may be extracted from the in-vehicle image information based on the human body motion characteristics, where the human body motion characteristics include human body limb characteristics, human body posture characteristics, and the like.
Step S303: and inputting the driving behavior image information into a second driving behavior recognition model, and acquiring a second driving behavior recognition result output by the second driving behavior recognition model.
In a specific implementation, the extracted driving behavior image information is input into a second driving behavior recognition model, it is emphasized that the first driving behavior recognition model outputs a driving behavior recognition result based on the sensor reading, the second driving behavior recognition model outputs a driving behavior recognition result based on the driving behavior image information, the second driving behavior recognition model comprises different gesture actions of various human bodies, and the driving behavior image is compared with the different gesture actions so as to output the second driving behavior recognition result.
Step S304: and evaluating the driving behavior of the driver according to the second driving behavior recognition result.
In a specific implementation, the second driving behavior recognition result includes a driving behavior of the driver, and the recognized driving behavior does not include one driving behavior but includes multiple different types of driving behaviors, such as a normal driving behavior or an aggressive driving behavior. The driving behavior flag may be set according to the actual evaluation requirement, which is not limited in this embodiment.
In this embodiment, when the relative state is a motion state, the camera device on the target vehicle is controlled to shoot so as to obtain the in-vehicle image information of the target vehicle; extracting driving behavior image information corresponding to the driver from the in-vehicle image information based on human motion characteristics; inputting the driving behavior image information into a second driving behavior recognition model, and acquiring a second driving behavior recognition result output by the second driving behavior recognition model; and evaluating the driving behavior of the driver according to the second driving behavior recognition result, and evaluating the driving behavior of the driver through image information acquired by a camera device when the motion state of the mobile terminal cannot represent the motion state of the target vehicle.
Referring to fig. 4, fig. 4 is a flowchart illustrating a driving behavior evaluation method according to a third embodiment of the present invention.
A third embodiment of a driving behavior evaluation method of the invention is proposed based on the first embodiment or the second embodiment described above.
Taking the first embodiment as an example, in this embodiment, before the step S50, the method further includes:
step S050: and acquiring the driving behavior of the driver according to the first driving behavior recognition result.
It should be noted that after obtaining the first driving behavior recognition result, the driving behavior identifier, such as the driving behavior identifier M, may be extracted from the first driving behavior recognition result 1 、M 2 Or M 3 Then the driving behavior of the driver is determined from the driving behavior signature, e.g. the driving behavior signature M 1 Corresponding to the aggressive driving behavior, the driving behavior mark M 2 Corresponding to safe driving behavior.
Step S051: and acquiring the surrounding environment information of the target vehicle.
In specific implementation, a plurality of environment sensors are further arranged around the body of the target vehicle, the vehicle-mounted terminal can acquire the surrounding environment information of the target vehicle through the plurality of environment sensors, and the surrounding environment information comprises road condition information, other vehicle driving information and the like.
Step S052: and judging whether the driving behavior of the driver belongs to normal driving behavior or not according to the surrounding environment information.
In specific implementation, the historical ambient environment information and the historical driving behavior have a corresponding relationship, after the ambient environment information of the target vehicle is obtained, the ambient environment information is compared with the historical ambient environment information, the historical driving behavior corresponding to the ambient environment information can be found, then the historical driving behavior is compared with the driving behavior, if the driving behavior is consistent with the historical driving behavior, the driving behavior is judged to be a normal driving behavior, and if the driving behavior is inconsistent with the historical driving behavior, the driving behavior is judged to be an abnormal driving behavior. Further, the normal driving behaviors include radical driving behaviors or safe driving behaviors, and when the driving behaviors belong to the normal driving behaviors, the driving behaviors of the driver are evaluated according to the first driving behavior recognition result.
Further, the step S052 specifically includes: when the driving behavior belongs to abnormal driving behavior, acquiring reference driving behaviors of other drivers around the target vehicle through Internet of vehicles communication, and judging the reference driving behaviors of the other drivers; when the reference driving behaviors of other drivers belong to abnormal behaviors, judging that a traffic obstacle exists in the road environment in front of the target vehicle; and recording position information and time information corresponding to the passing barriers, sending the position information and the time information to other vehicles around the target vehicle through the Internet of vehicles communication, and outputting corresponding danger prompt information.
It should be noted that, when it is detected that the driving behavior belongs to the abnormal driving behavior, the reference driving behaviors of other drivers are obtained through the internet of vehicles communication, and the reference driving behaviors of other drivers are judged, it should be emphasized that if the abnormal driving behavior occurs to the driver and the abnormal driving behavior does not occur to the driver driving other vehicles around the target vehicle, it is indicated that the abnormal driving behavior of the driver is caused by the bad driving habit of the driver at this time. However, if the driver has abnormal behavior, the driver driving another vehicle around the target vehicle also has abnormal driving behavior, and at this time, it is described that the abnormal driving behavior of the driver is caused by an unexpected emergency, and not by bad driving behavior of the driver, and when such a situation occurs, the GPS sensor of the mobile terminal may be used to locate the traffic fault existing in the road environment in front, and record the location information and the time information corresponding to the traffic fault, and then send the location information and the time information to another vehicle around the target vehicle through the internet of vehicles communication, and meanwhile, in order to improve the safety of vehicle driving, corresponding danger prompting information may be output.
Further, in order to make the evaluation of the driving behavior more reasonable and accurate, step S50 in this embodiment specifically includes: determining the driving behavior type of the driver according to the first driving behavior recognition result; identifying target driving behaviors according to the driving behavior types; acquiring the occurrence frequency of the target driving behavior within a preset time period; and determining the occurrence frequency corresponding to the target driving behavior according to the preset time period and the occurrence frequency, and evaluating the driving behavior of the driver according to the occurrence frequency.
It should be noted that the driving behavior identifier corresponds to different driving behaviors, and the corresponding driving behavior type may be determined according to the different driving behaviors, for example, the driving behavior type corresponding to the driving behavior N is normal driving, and the driving behavior type corresponding to the driving behavior identifier B is aggressive driving. Further, the driving behavior of the driver may be evaluated according to the driving behavior type, for example, if an aggressive driving behavior is recognized, the driving behavior of the driver is evaluated to be poor, and if a normal driving behavior is recognized, the driving behavior of the driver is evaluated to be good, and the specific evaluation criteria may be set according to actual conditions, which is not limited in this embodiment.
It should be noted that the target driving behavior may be set as an aggressive driving behavior, and then the frequency of occurrence of the aggressive driving behavior within a preset time period is obtained, so as to calculate the frequency of occurrence of the aggressive driving behavior, and then the frequency of occurrence is compared with the preset frequency, and if the frequency of occurrence is greater than the preset frequency, it is determined that the driving behavior of the driver is poor, and the score is low. In this embodiment, the target driving behavior may also be set as a normal driving behavior, and then the number of times of occurrence of the normal driving behavior in a preset time period is obtained, so as to calculate the occurrence frequency of the normal driving behavior, and then the occurrence frequency is compared with the preset frequency, and if the occurrence frequency is greater than the preset frequency, it is determined that the driving behavior of the driver is good, and the score is high, where the target driving behavior, the preset time period, and the preset frequency may all be adjusted accordingly according to actual evaluation requirements, and this implementation is not limited thereto.
The embodiment acquires the driving behavior of the driver according to the first driving behavior recognition result; acquiring surrounding environment information of a target vehicle; judging whether the driving behavior of the driver belongs to normal driving behavior according to the surrounding environment information, acquiring reference driving behaviors of other drivers around the target vehicle through internet of vehicles communication when the driving behavior belongs to abnormal driving behavior, transmitting position information and time information corresponding to a traffic obstacle existing in the road environment in front of the target vehicle to other vehicles around the target vehicle when the reference driving behaviors of the other drivers also belong to abnormal behavior, outputting corresponding danger prompt information, improving the driving safety of the vehicle, and determining the type of the driving behavior of the driver according to a first driving behavior identification result; identifying target driving behaviors according to the driving behavior types; acquiring the occurrence frequency of target driving behaviors in a preset time period; and determining the occurrence frequency corresponding to the target driving behavior according to the preset time period and the occurrence frequency, and evaluating the driving behavior of the driver according to the occurrence frequency, so that the evaluation of the driving behavior is more reasonable and accurate.
Furthermore, an embodiment of the present invention further provides a storage medium, on which a driving behavior evaluation program is stored, and the driving behavior evaluation program, when executed by a processor, implements the steps of the driving behavior evaluation method as described above.
Referring to fig. 5, fig. 5 is a block diagram showing the structure of the driving behavior evaluation device according to the first embodiment of the present invention.
As shown in fig. 5, a driving behavior evaluation device according to an embodiment of the present invention includes:
the acquiring module 10 is configured to acquire sensor reading information corresponding to a mobile terminal on a target vehicle when the mobile terminal is on the target vehicle.
And an extracting module 20, configured to extract a gravitational acceleration corresponding to the mobile terminal from the sensor reading information.
And the judging module 30 is used for determining the relative state between the mobile terminal and the target vehicle according to the gravity acceleration.
And the input module 40 is configured to input the sensor reading information to a first driving behavior recognition model when the relative state is a static state, and acquire a first driving behavior recognition result output by the first driving behavior recognition model.
And the evaluation module 50 is used for evaluating the driving behavior of the driver according to the first driving behavior recognition result.
The method comprises the steps of extracting the gravity acceleration corresponding to a mobile terminal from the sensor reading information corresponding to the mobile terminal on a moving target vehicle; determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration; when the relative state is a static state, the reading information of the sensor is input into the first driving behavior recognition model to evaluate the driving behavior of the driver, and the driving behavior of the driver is evaluated by the aid of the sensor arranged in the mobile terminal, so that the driving behavior evaluation accuracy is guaranteed, and meanwhile, the driving behavior evaluation cost is greatly reduced.
In one embodiment, the driving behavior assessment apparatus further comprises a detection module;
the detection module is used for acquiring the running state of the vehicle navigation function in the mobile terminal; extracting a GPS speed corresponding to the mobile terminal from the sensor reading information, and comparing the GPS speed with a preset speed; and when the vehicle navigation function is in an on state or the GPS speed is greater than the preset speed, judging that the mobile terminal is on a moving target vehicle.
In an embodiment, the determining module 30 is further configured to decompose the gravitational acceleration into gravitational acceleration components in multiple directions; acquiring a gravity acceleration difference value between a gravity acceleration component at any moment in any direction and a gravity acceleration component at the previous moment; and comparing the gravity acceleration difference with a preset difference threshold, and determining the relative state between the mobile terminal and the target vehicle according to the difference comparison result.
In an embodiment, the input module 40 is further configured to control a camera device on the target vehicle to shoot when the relative state is a motion state, so as to obtain in-vehicle image information of the target vehicle; extracting driving behavior image information corresponding to the driver from the in-vehicle image information based on human motion characteristics; inputting the driving behavior image information into a second driving behavior recognition model, and acquiring a second driving behavior recognition result output by the second driving behavior recognition model; and evaluating the driving behavior of the driver according to the second driving behavior recognition result.
In an embodiment, the determining module 30 is further configured to obtain the driving behavior of the driver according to the first driving behavior recognition result; acquiring surrounding environment information of the target vehicle; and judging whether the driving behavior of the driver belongs to normal driving behavior or not according to the surrounding environment information.
In one embodiment, the driving behavior evaluation device further comprises a sending module;
the sending module is used for acquiring reference driving behaviors of other drivers around the target vehicle through internet of vehicles communication and judging the reference driving behaviors of the other drivers when the driving behaviors belong to abnormal driving behaviors; when the reference driving behaviors of other drivers belong to abnormal behaviors, judging that a traffic obstacle exists in the road environment in front of the target vehicle; and recording position information and time information corresponding to the traffic barrier, sending the position information and the time information to other vehicles around the target vehicle through the Internet of vehicles communication, and outputting corresponding danger prompt information.
In an embodiment, the evaluation module 50 is further configured to determine a driving behavior type of the driver according to the first driving behavior recognition result; identifying target driving behaviors according to the driving behavior types; acquiring the occurrence frequency of the target driving behavior within a preset time period; and determining the occurrence frequency corresponding to the target driving behavior according to the preset time period and the occurrence frequency, and evaluating the driving behavior of the driver according to the occurrence frequency.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-mentioned work flows are only illustrative and do not limit the scope of the present invention, and in practical applications, those skilled in the art may select some or all of them according to actual needs to implement the purpose of the solution of the present embodiment, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may be referred to a driving behavior evaluation method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Claims (8)
1. A driving behavior evaluation method characterized by comprising:
when a mobile terminal is positioned on a moving target vehicle, acquiring sensor reading information corresponding to the mobile terminal on the target vehicle;
extracting the gravity acceleration corresponding to the mobile terminal from the sensor reading information;
determining a relative state between the mobile terminal and the target vehicle according to the gravity acceleration;
when the relative state is a static state, inputting the sensor reading information into a first driving behavior recognition model, and acquiring a first driving behavior recognition result output by the first driving behavior recognition model, wherein the first driving behavior recognition model comprises the corresponding relation between the sensor reading information and the driving behavior;
evaluating the driving behavior of the driver according to the first driving behavior recognition result;
wherein the determining the relative state between the mobile terminal and the target vehicle according to the gravitational acceleration comprises:
decomposing the gravitational acceleration into gravitational acceleration components in a plurality of directions;
obtaining the time T in any direction 1 The gravity acceleration component and the last time T 2 The gravity acceleration difference between the gravity acceleration components of (a);
comparing the gravity acceleration difference with a preset difference threshold, and determining the relative state between the mobile terminal and the target vehicle according to the difference comparison result;
before evaluating the driving behavior of the driver according to the first driving behavior recognition result, the method further includes:
acquiring the driving behavior of the driver according to the first driving behavior recognition result;
acquiring surrounding environment information of the target vehicle;
judging whether the driving behavior of the driver belongs to normal driving behavior or not according to the surrounding environment information;
and when the driving behavior belongs to normal driving behavior, executing the step of evaluating the driving behavior of the driver according to the first driving behavior recognition result.
2. The driving behavior assessment method according to claim 1, wherein before obtaining sensor reading information corresponding to the mobile terminal on a target vehicle when the mobile terminal is on the target vehicle, further comprising:
acquiring the running state of a vehicle navigation function in the mobile terminal;
extracting a GPS speed corresponding to the mobile terminal from the sensor reading information, and comparing the GPS speed with a preset speed;
and when the vehicle navigation function is in an on state or the GPS speed is greater than the preset speed, judging that the mobile terminal is on a moving target vehicle, and storing the reading information of the sensor in a block chain.
3. The driving behavior evaluation method according to claim 1, further comprising, after determining the relative state between the mobile terminal and the target vehicle from the gravitational acceleration:
when the relative state is a motion state, controlling a camera device on the target vehicle to shoot so as to obtain the in-vehicle image information of the target vehicle;
extracting driving behavior image information corresponding to the driver from the in-vehicle image information based on human motion characteristics;
inputting the driving behavior image information into a second driving behavior recognition model, and acquiring a second driving behavior recognition result output by the second driving behavior recognition model;
and evaluating the driving behavior of the driver according to the second driving behavior recognition result.
4. The driving behavior evaluation method according to claim 1, wherein after the determination of whether the driving behavior of the driver belongs to a normal driving behavior based on the surrounding environment information, further comprising:
when the driving behaviors belong to abnormal driving behaviors, reference driving behaviors of other drivers around the target vehicle are obtained through internet of vehicles communication, and the reference driving behaviors of the other drivers are judged;
when the reference driving behaviors of other drivers belong to abnormal behaviors, judging that a traffic obstacle exists in the road environment in front of the target vehicle;
and recording position information and time information corresponding to the passing barriers, sending the position information and the time information to other vehicles around the target vehicle through the Internet of vehicles communication, and outputting corresponding danger prompt information.
5. The driving behavior assessment method according to claim 1, characterized in that the assessment of the driving behavior of the driver based on the first driving behavior recognition result comprises:
determining the driving behavior type of the driver according to the first driving behavior recognition result;
identifying target driving behaviors according to the driving behavior types;
acquiring the occurrence frequency of the target driving behavior within a preset time period;
and determining the occurrence frequency corresponding to the target driving behavior according to the preset time period and the occurrence frequency, and evaluating the driving behavior of the driver according to the occurrence frequency.
6. A driving behavior evaluation device characterized by comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring sensor reading information corresponding to a mobile terminal on a target vehicle when the mobile terminal is on the moving target vehicle;
the extraction module is used for extracting the gravity acceleration corresponding to the mobile terminal from the sensor reading information;
the judging module is used for determining the relative state between the mobile terminal and the target vehicle according to the gravity acceleration;
the input module is used for inputting the sensor reading information to a first driving behavior recognition model when the relative state is a static state, and acquiring a first driving behavior recognition result output by the first driving behavior recognition model, wherein the first driving behavior recognition model comprises the corresponding relation between the sensor reading information and the driving behavior;
the evaluation module is used for evaluating the driving behavior of the driver according to the first driving behavior recognition result;
the judging module is further used for decomposing the gravity acceleration into gravity acceleration components in a plurality of directions;
obtaining the time T in any direction 1 The gravitational acceleration component of (2) and the last time T 2 The gravitational acceleration difference between the gravitational acceleration components of (a);
comparing the gravity acceleration difference with a preset difference threshold, and determining the relative state between the mobile terminal and the target vehicle according to the difference comparison result;
the judging module is further used for acquiring the driving behavior of the driver according to the first driving behavior recognition result;
acquiring surrounding environment information of the target vehicle;
judging whether the driving behavior of the driver belongs to normal driving behavior or not according to the surrounding environment information;
and when the driving behavior belongs to normal driving behavior, executing the step of evaluating the driving behavior of the driver according to the first driving behavior recognition result.
7. A driving behavior evaluation apparatus characterized by comprising: memory, a processor and a driving behavior assessment program stored on the memory and executable on the processor, the driving behavior assessment program being configured to implement the steps of the driving behavior assessment method according to any of claims 1 to 5.
8. A storage medium, characterized in that the storage medium has stored thereon a driving behavior assessment program which, when executed by a processor, implements the steps of the driving behavior assessment method according to any one of claims 1 to 5.
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