CN110648075A - Driving safety evaluation method and device - Google Patents
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Abstract
The application discloses a driving safety assessment method and device, wherein the driving safety assessment model is input with basic information, safe driving record data and driving violation and accident data of a driver together to obtain a driving safety level. The method comprises the following steps: acquiring basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, wherein the basic information comprises psychological data, physiological data and driving skill data; inputting basic information, safe driving record data and driving violation and accident data into a pre-trained driving safety evaluation model; and adopting a trained driving safety evaluation model to figure a driver to be evaluated, so as to obtain the driving safety level of the driver to be evaluated, and giving an alarm according to the driving safety level.
Description
Technical Field
The application relates to the field of safety assessment, in particular to a driving safety assessment method and device.
Background
In the transportation industry, the management of driver safety is an extremely important part. The core of safety management is to find out a very small part of drivers who are easy to find out problems so as to take corresponding measures.
In the prior art, there are various methods for judging the behavior of the driver. For example, whether each index of the psychological assessment of the driver meets the corresponding requirement is judged. Or judging whether each physiological index meets the corresponding requirement according to the physiological condition of the driver. Or judging the driving safety of the driver according to the indexes of various driving skills of the driver.
In the prior art, the judgment method only judges the driving safety of the driver independently according to various indexes of the psychology, the physiology and the driving skill of the driver, and in the mode, the influence of the relevance among different indexes on the driving behavior safety of the driver is not considered, so that the judgment result of the prior art on the driving safety is not accurate, and the condition of the driver cannot be known accurately.
Disclosure of Invention
In order to overcome at least the above-mentioned deficiencies in the prior art, it is an object of the present application to provide a driving safety evaluation method, the method comprising:
acquiring basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, wherein the basic information comprises psychological data, physiological data and driving skill data, the psychological data is information representing the psychological state of the driver, the physiological data is information representing the physiological health state of the driver, and the driving skill data comprises the time when the driver acquires a driving license, the driving age, whether the driver regularly audits the driving license, a scholarship and the age; the safe driving record data is used for representing whether the driver drives safely, and the driving violation and accident data is used for representing the safety accident of the driver;
inputting the basic information, the safe driving record data and the driving violation and accident data, or inputting the basic information and the safe driving record data into a pre-trained driving safety evaluation model to portray the driver to be evaluated;
obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated, wherein the driving safety level is the driving safety degree of the driver;
and giving an alarm according to the driving safety level.
Optionally, before the step of obtaining basic information, safe driving record data and driving violation and accident data of the driver to be assessed, the method further comprises:
the method comprises the steps of obtaining a training sample set comprising a plurality of training samples, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the safety degree of driving;
inputting the training sample set into a pre-configured initial training model for model training;
and obtaining a driving safety evaluation model.
In the step of inputting the training samples into a pre-trained initial training model for model training, a method for training the initial training model is a classification clustering or correlation analysis method, and the classification clustering or correlation analysis method comprises at least one of a neural network algorithm, an Aprori algorithm, an FP-growth algorithm and a K-means algorithm.
Optionally, the method further comprises:
acquiring new basic information, new safe driving record data and new driving violation and accident data of a driver corresponding to the training sample or new basic information, safe driving record data and driving violation and accident data of the driver;
updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver;
and adjusting the driving safety evaluation model according to the updated training sample set.
Optionally, the mental data comprises a mental disease assessment scale comprising a depression assessment scale or a manic-depressive disorder assessment scale and a mental assessment report;
the physiological data comprises historical illness information, physical examination information, reaction speed and real-time physiological information of the driver, wherein the real-time physiological information comprises pulse, blood sugar, heart rate, blood pressure and alcohol test results;
the safe driving record data comprises overspeed information, driving information, behavior information and vehicle state information;
the overspeed information comprises historical overspeed times and severity of each overspeed;
the driving information comprises emergency braking information, emergency acceleration information, emergency steering information, door opening sliding information, red light running information and information of avoiding pedestrians;
the behavior information comprises fatigue driving information, mobile phone calling information, information that the front of the vehicle is not seen by eyes for a long time, information that the steering wheel is separated by two hands, information that the safe vehicle distance is not kept and information of the distance between the stop stations.
Optionally, the specific steps of obtaining basic information, safe driving record data, and driving violation and accident data of the driver to be assessed include:
acquiring a psychological disease evaluation scale, a psychological evaluation report, historical illness information and physical examination information from a third-party platform;
acquiring real-time physiological information of the driver to be evaluated from the intelligent wearable device;
acquiring overspeed information of the driver to be evaluated from a GPS device;
acquiring the driving information of the driver to be evaluated from the CAN bus device;
behavior information of a driver to be evaluated of the driver is acquired from an intelligent video analysis device including an ADAS device and/or a DSM device.
Another object of the present application is to provide a driving safety evaluation device, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, the basic information comprises psychological data, physiological data and driving skill data, the psychological data is information representing the psychological state of the driver, the physiological data is information representing the physiological health state of the driver, the driving skill data comprises the time for the driver to acquire a driving license, the driving age, whether to regularly check the driving license, a scholarly and an age, the safe driving record data is information representing whether the driver drives safely, and the driving violation and accident data is information representing the safety accident of the driver;
the input module is used for inputting the basic information, the safe driving record data and the driving violation and accident data, or inputting the basic information and the safe driving record data into a pre-trained driving safety evaluation model so as to portray the driver to be evaluated;
the evaluation module is used for obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated, and the driving safety level is the driving safety degree of the driver;
and the alarm module is used for giving an alarm according to the driving safety level.
Optionally, the device further comprises a training module, and before the step of obtaining basic information, safe driving record data and driving violation and accident data of the driver to be evaluated, the training module is specifically configured to:
the method comprises the steps of obtaining a training sample set comprising a plurality of training samples, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the safety degree of driving;
inputting the training sample set into a pre-configured initial training model for model training;
and obtaining a driving safety evaluation model.
In the step of inputting the training samples into a pre-trained initial training model for model training, a method for training the initial training model is a classification clustering or correlation analysis method, and the classification clustering or correlation analysis method comprises at least one algorithm of a neural network algorithm, an Aprori algorithm, an FP-growth algorithm and a K-means algorithm.
Optionally, the device further comprises an adjusting module, configured to obtain new basic information, new safe driving record data, and new driving violation and accident data of the driver corresponding to the training sample, or new basic information, safe driving record data, and driving violation and accident data of the driver;
updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver;
and adjusting the driving safety evaluation model according to the updated training sample set.
Compared with the prior art, the method has the following beneficial effects:
in the application, the basic information of the driver to be evaluated, the safe driving record data and the driving violation and accident data or the basic information and the safe driving record data are simultaneously input into the driving safety evaluation model to figure the driver to be evaluated, so that the driving safety level of the driver to be evaluated is obtained. The driving safety evaluation model can be used for simultaneously evaluating the driving safety of the driver by integrating various data such as psychological, physiological and driving skill data, safe driving record data, driving violation and accident data and the like of the driver, so that the evaluation result is more accurate. And giving an alarm according to the judgment result, so that a driver with high driving risk can be found in time, and accidents are reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram schematically illustrating a structure of a driving safety evaluation apparatus provided in an embodiment of the present application;
FIG. 2 is a first schematic flow chart of a driving safety assessment method provided in an embodiment of the present application;
FIG. 3 is a second schematic flowchart of a driving safety assessment method according to an embodiment of the present application;
fig. 4 is a block diagram schematically illustrating the structure of a driving safety evaluation device according to an embodiment of the present application.
Icon: 100-driving safety evaluation equipment; 110-driving safety evaluation means; 111-an acquisition module; 112-an input module; 113-an evaluation module; 114-an alarm module; 115-a training module; 116-an adjustment module; 120-a memory; 130-a processor.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present application, it is further noted that, unless expressly stated or limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In the transportation industry, safety management of driving behavior of drivers is an extremely important ring. The core of safety management is to find 5% of drivers who are easy to have problems in time, so that subsequent actions such as targeted education, treatment or shift are facilitated. For a driver, driving safety behavior is affected by a variety of factors, such as psychological factors, physiological factors and age, driving age, and records of safe driving.
In the prior art, when judging the driving behavior safety of a driver, generally, one kind of data in the aspects of psychology, physiology or driving skill data and the like is separately acquired, and then a separate safety management system judges according to the acquired data, even gives an alarm and the like. For example, for the GPS overspeed, a general, severe overspeed, etc. limit is given according to the speed limit value of the road section, so that corresponding measures are taken. The inventor finds that psychological factors, physiological factors and driving skill factors of a driver can influence each other when the driving safety of the driver is judged, and the judgment result is not accurate due to the fact that various data are simply separated to judge according to the scheme in the prior art.
Referring to fig. 1, the driving safety assessment apparatus 100 includes a driving safety assessment device 110, a memory 120 and a processor 130, wherein the memory 120 and the processor 130 are directly or indirectly electrically connected to each other for data interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The driving safety evaluation device 110 includes at least one software function module that may be stored in the memory 120 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the driving safety evaluation apparatus 100. The processor 130 is used for executing executable modules stored in the memory 120, such as software functional modules and computer programs included in the driving safety evaluation device 110.
Referring to fig. 2, fig. 2 is a flow chart of a driving safety evaluation method applicable to the driving safety evaluation apparatus 100, and the method includes steps S110 to S140. Each step is explained in detail below.
Step S110, obtaining basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, wherein the basic information comprises psychological data, physiological data and driving skill data, the psychological data is information representing the psychological state of the driver, the physiological data is information representing the physiological health state of the driver, the safe driving record data is information representing whether the driver drives safely, and the driving violation and accident data is information representing the safety accident of the driver.
Optionally, in this embodiment, the psychological data includes a psychological disease assessment table and a psychological assessment report, the psychological disease assessment table is a table for recording whether the driver has a psychological disease, the psychological disease assessment table may include a depression assessment table or a manic-depressive disorder assessment table, the psychological disease assessment table may also include data records of three diseases, namely insomnia, anxiety and depression, and the psychological assessment report is a data record table of assessment results of psychological assessment methods such as brain functional area activity, brain wave and the like; the physiological data comprises historical illness information, physical examination information, reaction speed and real-time physiological information of the driver, the historical illness information is information for recording the historical illness state of the driver, the historical illness information can be an electronic medical record, and can be manually input when being specifically acquired, and can also be acquired from a third-party platform, such as a hospital. The physical examination information is information for recording the physical examination result of the driver, and may include various indexes of the driver, such as blood pressure, blood lipid concentration, and blood glucose concentration, for example, and the physical examination information may be manually input or may be acquired from a third-party platform, such as a hospital. Real-time physiological information includes pulse, rhythm of the heart, blood sugar, blood pressure and alcohol test result, and real-time physiological information can be gathered by the equipment that can gather pulse, blood sugar rhythm of the heart, blood pressure and alcohol test result, for example the intelligent wearing equipment that the driver wore, intelligent wearing equipment can be healthy bracelet.
The driving skill data comprises the time when the driver obtains the driving license, the driving age, whether the driving license is checked regularly, a scholarly calendar and the age; the safe driving record data comprises overspeed information, driving information, behavior information and vehicle state information.
The overspeed information is used for representing information related to overspeed of a driver, and the overspeed information comprises historical overspeed times and severity of each overspeed; the overspeed information can also comprise a speed limit value of a road section where the current vehicle is located, and the current speed and overspeed proportion. The severity of the overspeed may be a proportion of the actual driving speed exceeding the speed limit, with a higher proportion being more severe overspeed. The driving information comprises emergency braking information, emergency acceleration information, emergency steering information, door opening sliding information, red light running information and pedestrian avoidance-free information, and the emergency braking information is used for representing whether the driver has emergency braking behavior or not, namely whether the change rate of the vehicle speed exceeds a first preset change rate or not in the braking process of the driver. The rapid acceleration information is information for indicating whether the driver has rapid acceleration, that is, whether the rate of change of the vehicle speed exceeds a second preset rate of change during acceleration of the driver. The sharp steering information is information for indicating whether there is sharp steering by the driver, that is, whether the change rate of the steering angle of the vehicle exceeds a third preset change rate during the steering process of the driver. The door opening slide information is information representing whether the driver opens the door during the slide process. The red light running information represents whether the driver runs the red light and/or the number of times of running the red light. The information of the non-avoidance pedestrians is information representing whether the driver has the non-avoidance pedestrians and/or the number of times of the non-avoidance pedestrians and the like. Of course, in this embodiment, the driving information may further include an opening degree of an accelerator, an opening degree of a brake, and a steering angle. The behavior information comprises fatigue driving information, mobile phone calling information, information that the front of the vehicle is not seen by eyes for a long time, information that the steering wheel is separated by two hands, information that the safe vehicle distance is not kept and information of the distance between the stop stations. Wherein the driver fatigue information may include at least one of data characterizing whether the driver has yawn, data characterizing a degree of eye closure of the driver, data characterizing whether the driver is speaking, and data whether the driver is off of the steering wheel. The information of the mobile phone includes the representation of whether the driver takes the mobile phone or not. The binocular long-time non-visual forward information is used for representing whether the driver does not visually see the forward part within the preset time length. The two-hands-off steering wheel information is used for representing whether the driver has two hands off the steering wheel. And the information of the safe distance is not kept and is used for representing whether the distance between the driver and the front vehicle is less than the safe distance. The stop distance information is used for representing whether the distance between the driver and the stop exceeds a preset distance when the driver stops. In this embodiment, the safe driving record data may further include the vehicle state information, where the vehicle state information includes lane departure information, collision information, and road condition information, the lane departure information is information of a lane departure direction or a lane sideline direction of the vehicle, and the collision information is information of whether the vehicle collides with another object. The traffic information refers to information such as vehicle density on a road.
In this embodiment, the current physiological data may further include an alcohol test result of the driver, and the alcohol test result may be measured by an alcohol tester.
In this embodiment, the overspeed information of the driver may be acquired from the GPS device, and the step of acquiring the behavior information of the driver may be as follows: the driver behavior information is acquired by an intelligent video analysis device provided in the vehicle, and for example, the intelligent video analysis device may include an ADAS (Advanced Driving assistance System) device and/or a DSM (driver monitoring System) device. Image information is collected from the interior of the cab and processed by processor 130 to obtain behavioral information about the driver, such as driver fatigue information, cellular phone information, information about the eyes not viewing the front, information about whether to speak, information about whether to disengage the hands from the steering wheel, information about whether to quarrel, etc.
The vehicle state information may further include distance information between the vehicle in which the driver is located and the preceding vehicle. The vehicle state information may be obtained by acquiring image information outside the vehicle by a camera device provided on the vehicle and then processing the image information by the processor 130. The camera may be a camera on a smart video analytics device.
In this embodiment, the driver basic information, the safe driving record data, and the driving violation and accident data all include a variety of information, including static information (information corresponding to an index that has been generated and does not change in a period of time before the acquisition, such as basic information) and dynamic information (data related to a real-time driving process of the driver, such as part of data in the safe driving record data and the driving violation and accident data), so that the data used for evaluating the driving safety of the driver, that is, the driving safety level, is more complete.
Optionally, in this embodiment, the specific steps of obtaining the basic information, the safe driving record data, and the driving violation and accident data of the driver to be assessed include the following steps: acquiring a psychological disease evaluation scale, a psychological evaluation report, historical illness information and physical examination information from a third-party platform; acquiring real-time physiological information of the driver to be evaluated from the intelligent wearable device; acquiring overspeed information of the driver to be evaluated from a Global Positioning System (GPS) device; acquiring driving information of the driver to be evaluated from a Controller Area Network (CAN) bus device; acquiring behavior information of a driver to be evaluated from an intelligent video analysis device including an ADAS (Advanced Driving assistance System) device and/or a DSM (driver monitoring System) device; vehicle state information is acquired from an Advanced Driving assistance System (Advanced Driving assistance System).
In the embodiment, the relevant data of the driver is acquired through the GPS device, the CAN bus device, the DSM device and the Adas device, so that the existing architecture CAN be fully utilized, and part of relevant information of the driver to be evaluated CAN be acquired in time.
And step S120, inputting data to the driving safety evaluation model trained in advance.
Specifically, the basic information, the safe driving record data and the driving violation and accident data are input into a pre-trained driving safety evaluation model, so that the driving safety evaluation model can be used for representing the driver to be evaluated.
And step S130, obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated.
And step S140, giving an alarm according to the driving safety level.
The driving safety level represents the driving safety degree of the driver, and the higher the driving safety level is, the higher the driving safety of the driver is.
In this embodiment, a safety level threshold may be set, and when the driving safety level is lower than the safety level threshold, an alarm may be given. In this embodiment, the driving safety level may be a probability of an accident, and when the probability of the accident is higher, it indicates that the driving safety level is lower.
In the embodiment, the basic information of the driver, the safe driving record data and the driving violation and accident data, or the basic information and the safe driving record data are input into a pre-trained driving safety evaluation model, so that the driving safety level of the driver can be accurately evaluated, the potential driver with possible accident can be positioned, the driver can be managed in a targeted manner, and the accident can be reduced.
Referring to fig. 3, optionally, before the step of obtaining the basic information, the safe driving record data, and the driving violation and accident data of the driver to be evaluated, big data analysis training may be performed according to the known basic information, the safe driving record data, and the driving violation and accident data of the driver, so as to obtain a driving safety evaluation model. When big data analysis training is carried out, known basic information, safe driving record data and driving violation and accident data of a driver are input into a big data platform, then the big data platform carries out classification clustering or correlation analysis to form a violation and accident portrait of the driver, the portrait contains relevant strongly-associated attributes of dangerous driving, such as severe overspeed, violent splenic qi, hypertension and the like, when the driver has severe overspeed or violent symptoms or hypertension, the driver is easy to have dangerous driving, namely, the driving safety grade of the driver is lower than that of the driver without the attributes.
Specifically, the method for training the initial training model may be classification clustering or correlation analysis. And obtaining the statistical significance correlation degree (positive correlation and negative correlation) of the multi-source data and dangerous driving behaviors through classification clustering or correlation analysis so as to obtain the driving safety level. The classification clustering or correlation analysis method may be at least one of a decision tree, a rule-based classification algorithm, a support vector machine and naive Bayes classification method, or a neural network algorithm, an Aprori algorithm, an FP-growth algorithm, or a K-means algorithm (K-means clustering algorithm), etc., wherein the neural network algorithm may be a C neural network algorithm. It is understood that one skilled in the art may select from various classification clustering or correlation analysis algorithms as needed to obtain a more accurate driving safety assessment model.
To assist understanding, the following describes a method for training a driving safety assessment model by taking a neural network as an example, and the method further includes steps S210 to S220.
Step S210, a training sample set comprising a plurality of training samples is obtained, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the driving safety degree.
And S220, performing model training according to the training sample set to obtain a driving safety evaluation model.
Specifically, the training sample set is input into a pre-configured initial training model for model training, and a driving safety evaluation model is obtained.
In the scheme, a plurality of training samples are adopted to train a driving safety evaluation model, so that when the driving safety of a driver needs to be evaluated, only basic information of the driver to be evaluated, safe driving record data and driving violation and accident data are needed, or the basic information and the safe driving record data are needed to be input into the driving safety evaluation model, and the driving safety evaluation model figures the driver needing to be evaluated so as to quickly evaluate the driving safety level of the driver.
It should be noted that, in this embodiment, the training samples are all from the traffic industry, and in this embodiment, the driving violation and accident data may include static data such as information about unsafe driving of the driver for a long period of time (e.g., violation or accident information recorded inside an enterprise, violation or traffic accident information recorded by a traffic police, and the like). The acquisition objects of the training samples can be various drivers in the whole traffic industry and can also be drivers in the subdivision industry in the traffic industry. The transportation industry may include, but is not limited to, the bus industry, the two passenger and one dangerous industry, the freight industry, the rail drive industry, and other segments. That is, the driving safety evaluation model may be trained based on basic information of drivers in a certain segment industry, safe driving record data, and driving violation and accident data.
For example, the driving safety evaluation model may be trained according to basic information, safe driving record data and driving violation and accident data of a driver in the bus industry, may also be trained according to basic information, safe driving record data and driving violation and accident data of a driver in the freight industry, may also be trained according to basic information, safe driving record data and driving violation and accident data of a driver in the passenger industry, and may also be trained according to basic information, safe driving record data and driving violation and accident data of a driver in the rail driving industry, such as a subway and a motor train.
When the training samples are from drivers in a specific industry, the obtained driving safety evaluation model can be used for evaluating the driving safety level of the drivers in the specific industry. That is, at this time, the basic information, the safe driving record data, and the driving violation and accident data input to the driving safety evaluation model come from one driver of the industry corresponding to the training sample. For example, when the training sample is taken from a driver in the bus industry, the driver to be evaluated also belongs to the driver in the bus industry; when the training sample is collected from a driver in the two-passenger one-danger industry, the driver to be evaluated also belongs to the driver in the two-passenger one-danger industry; when the training sample is taken from a driver in the freight industry, the driver to be evaluated also belongs to the driver in the freight industry; when the training samples are taken from drivers in the rail driving industry, the drivers to be evaluated also belong to drivers in the rail driving industry. In the embodiment, one driving safety assessment model is trained for the drivers in the specific subdivision industry, so that the assessment accuracy of the driving safety level of the driver to be assessed can be further improved, and the driver who is potentially easy to have an accident can be more accurately positioned.
Therefore, when the driving safety level of the driver to be evaluated is judged, static data such as unsafe driving information (such as violation or accident information recorded in an enterprise, violation or traffic accident information recorded by a traffic police and the like) of the driver to be evaluated is input at the same time, so that the driving safety of the driver to be evaluated can be acquired more accurately, and driving safety accidents are further reduced.
Optionally, in this embodiment, the training samples may further include driving behavior that the driver needs to improve, a physiological index that needs to be adjusted, and a psychological index that needs to be adjusted.
In addition, in this embodiment, when the driving safety assessment model is trained, the training sample may further include driving violation and accident data of the driver, where the driving violation and accident data includes violation information and accident information of the driver recorded by the transportation department, and may also include violation information and accident information of the driver recorded by the unit where the driver is located.
In the embodiment, the driving behavior of the driver to be improved, the physiological index to be adjusted and the psychological index to be adjusted are set in the label of the training sample, so that the driving safety assessment model can obtain the driving behavior of the driver to be assessed, the physiological index to be adjusted and the psychological index to be adjusted according to the psychological data, the physiological data and the driving skill data of the driver to be assessed, and the method has the characteristics of convenience and intuition.
Optionally, in this embodiment, the method further includes acquiring new basic information, new safe driving record data, and new driving violation and accident data of the driver or new basic information, safe driving record data, and driving violation and accident data of the driver corresponding to the training sample; updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver; and adjusting the driving safety evaluation model according to the updated training sample set.
In this embodiment, the driving safety assessment model may be adjusted according to the updated training sample set, so that the accuracy of the driving safety assessment model may be further improved.
Referring to fig. 4, the driving safety evaluation device 110 provided by the present application includes an obtaining module 111, an input module 112, and an evaluation module 113. The driving safety evaluation device 110 includes a software function module which may be stored in the memory 120 in the form of software or firmware or solidified in an Operating System (OS) of the driving safety evaluation apparatus 100.
The obtaining module 111 is configured to obtain basic information, safe driving record data, and driving violation and accident data of a driver to be assessed, where the basic information includes psychological data, physiological data, and driving skill data, the psychological data is information representing a psychological state of the driver, the physiological data is information representing a physiological health state of the driver, and the driving skill data includes time when the driver obtains a driving license, a driving age, whether the driver regularly reviews the driving license, a scholarship, and an age; the safe driving record data is used for representing whether the driver drives safely, and the driving violation and accident data is used for representing the safety accident of the driver.
The obtaining module 111 in this embodiment is configured to execute step S110, and for a detailed description of the obtaining module 111, reference may be made to the description of step S110.
And the input module 112 is configured to input the basic information, the safe driving record data, and the driving violation and accident data, or input the basic information and the safe driving record data into a pre-trained driving safety assessment model, so as to portray the driver to be assessed.
The input module 112 in this embodiment is used to execute step S120, and the detailed description about the input module 112 may refer to the description about step S120.
And the evaluation module 113 is used for obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated.
The evaluation module 113 in this embodiment is configured to perform the step S130, and the detailed description about the evaluation module 113 may refer to the description about the step S130.
And the alarm module 114 is used for giving an alarm according to the driving safety level.
Optionally, the device further includes a training module 115, and before the step of obtaining basic information, safe driving record data, and driving violation and accident data of the driver to be evaluated, the training module 115 is specifically configured to: the method comprises the steps of obtaining a training sample set comprising a plurality of training samples, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the safety degree of driving; inputting the training sample set into a pre-trained initial training model for model training; and obtaining a driving safety evaluation model.
Optionally, the apparatus further includes an adjusting module 116, specifically configured to:
acquiring new basic information, new safe driving record data and new driving violation and accident data of a driver corresponding to the training sample or new basic information, safe driving record data and driving violation and accident data of the driver;
updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver;
and adjusting the driving safety evaluation model according to the updated training sample set.
The above description is only for various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and all such changes or substitutions are included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A driving safety evaluation method, characterized by comprising:
acquiring basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, wherein the basic information comprises psychological data, physiological data and driving skill data, the psychological data is information representing the psychological state of the driver, the physiological data is information representing the physiological health state of the driver, the driving skill data comprises the time when the driver acquires a driving license, the driving age, whether the driving license is checked regularly, the driving history and the driving age are included, the safe driving record data is information representing whether the driver drives safely, and the driving violation and accident data is information representing the safety accident of the driver;
inputting the basic information, the safe driving record data and the driving violation and accident data, or inputting the basic information and the safe driving record data into a pre-trained driving safety evaluation model to portray the driver to be evaluated;
obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated, wherein the driving safety level is the driving safety degree of the driver;
and giving an alarm according to the driving safety level.
2. The method of claim 1, wherein prior to the step of obtaining driver-to-be-assessed basic information, safe-driving record data, and driving violation and accident data, the method further comprises:
the method comprises the steps of obtaining a training sample set comprising a plurality of training samples, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the safety degree of driving;
inputting the training sample set into a pre-configured initial training model for model training;
and obtaining a driving safety evaluation model.
3. The method of claim 2, wherein in the step of inputting the training samples into a pre-trained initial training model for model training, the method for training the initial training model is a classification clustering or correlation analysis method, and the classification clustering or correlation analysis method includes at least one of a neural network algorithm, an Aprori algorithm, an FP-growth algorithm, and a K-means algorithm.
4. The method of claim 2, further comprising:
acquiring new basic information, new safe driving record data and new driving violation and accident data of a driver corresponding to the training sample or new basic information, safe driving record data and driving violation and accident data of the driver;
updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver;
and adjusting the driving safety evaluation model according to the updated training sample set.
5. The method of any one of claims 1-4, wherein the mental data comprises a mental disease assessment scale comprising a depression assessment scale or a manic-depressive assessment scale, and a mental disease assessment report;
the physiological data comprises historical illness information, physical examination information, reaction speed and real-time physiological information of the driver, wherein the real-time physiological information comprises pulse, blood sugar, heart rate, blood pressure and alcohol test results;
the safe driving record data comprises overspeed information, driving information, behavior information and vehicle state information;
the overspeed information comprises historical overspeed times and severity of each overspeed;
the driving information comprises emergency brake information, emergency acceleration information, emergency steering information, door opening sliding information, red light running information and information of non-avoidance pedestrians;
the behavior information comprises fatigue driving information, mobile phone calling information, information that the front of the vehicle is not seen by eyes for a long time, information that the steering wheel is separated by two hands, information that the safe vehicle distance is not kept and information that the distance between the stop stations is too far.
6. The method of claim 5, wherein the specific steps of obtaining basic information, safe driving record data and driving violation and accident data of the driver to be assessed comprise:
acquiring a psychological disease evaluation scale, a psychological evaluation report, historical illness information and physical examination information from a third-party platform;
acquiring real-time physiological information of the driver to be evaluated from the intelligent wearable device;
acquiring overspeed information of the driver to be evaluated from a GPS device;
acquiring the driving information of the driver to be evaluated from the CAN bus device;
behavior information of a driver to be evaluated of the driver is acquired from an intelligent video analysis device including an ADAS device and/or a DSM device.
7. A driving safety evaluation device characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring basic information, safe driving record data and driving violation and accident data of a driver to be evaluated, the basic information comprises psychological data, physiological data and driving skill data, the psychological data is information representing the psychological state of the driver, the physiological data is information representing the physiological health state of the driver, the driving skill data comprises the time for the driver to acquire a driving license, the driving age, whether to regularly check the driving license, a scholarly and an age, the safe driving record data is information representing whether the driver drives safely, and the driving violation and accident data is information representing the safety accident of the driver;
the input module is used for inputting the basic information, the safe driving record data and the driving violation and accident data, or inputting the basic information and the safe driving record data into a pre-trained driving safety evaluation model so as to portray the driver to be evaluated;
the evaluation module is used for obtaining the driving safety level of the driver to be evaluated according to the image of the driver to be evaluated, and the driving safety level is the driving safety degree of the driver;
and the alarm module is used for giving an alarm according to the driving safety level.
8. The device of claim 7, further comprising a training module, prior to the step of obtaining driver-to-be-assessed basic information, safe driving record data, and driving violation and accident data, the training module being specifically configured to:
the method comprises the steps of obtaining a training sample set comprising a plurality of training samples, wherein the training samples comprise basic information of a driver, safe driving record data, driving violation and accident data and a label for representing the safety degree of driving;
inputting the training sample set into a pre-configured initial training model for model training;
and obtaining a driving safety evaluation model.
9. The apparatus of claim 8, wherein in the step of inputting the training samples into a pre-trained initial training model for model training, the method for training the initial training model is a classification clustering or correlation analysis method, and the classification clustering or correlation analysis method includes at least one of a neural network algorithm, an Aprori algorithm, an FP-growth algorithm, and a K-means algorithm.
10. The device of claim 8, further comprising an adjustment module for obtaining new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or new basic information, safe driving record data and driving violation and accident data of the driver;
updating the training sample set according to new basic information, new safe driving record data and new driving violation and accident data of the driver corresponding to the training sample or the new basic information, the new safe driving record data and the driving violation and accident data of the driver;
and adjusting the driving safety evaluation model according to the updated training sample set.
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|---|---|---|---|---|
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
| CN108647708A (en) * | 2018-04-28 | 2018-10-12 | 清华-伯克利深圳学院筹备办公室 | Driver evaluation's method, apparatus, equipment and storage medium |
| CN108860157A (en) * | 2018-07-16 | 2018-11-23 | 深圳市元征科技股份有限公司 | A kind of methods of risk assessment violating the regulations, system, equipment and storage medium |
| CN110120153A (en) * | 2019-05-24 | 2019-08-13 | 广州公交集团第三公共汽车有限公司 | A kind of public transport drives accident risk assessment system and its method |
-
2019
- 2019-09-27 CN CN201910923281.0A patent/CN110648075A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
| CN108647708A (en) * | 2018-04-28 | 2018-10-12 | 清华-伯克利深圳学院筹备办公室 | Driver evaluation's method, apparatus, equipment and storage medium |
| CN108860157A (en) * | 2018-07-16 | 2018-11-23 | 深圳市元征科技股份有限公司 | A kind of methods of risk assessment violating the regulations, system, equipment and storage medium |
| CN110120153A (en) * | 2019-05-24 | 2019-08-13 | 广州公交集团第三公共汽车有限公司 | A kind of public transport drives accident risk assessment system and its method |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN111523766B (en) * | 2020-03-27 | 2020-11-13 | 中国平安财产保险股份有限公司 | Driving risk assessment method and device, electronic equipment and readable storage medium |
| CN111914687A (en) * | 2020-07-15 | 2020-11-10 | 深圳民太安智能科技有限公司 | Method for actively identifying accident based on Internet of vehicles |
| CN111914687B (en) * | 2020-07-15 | 2023-11-17 | 深圳民太安智能科技有限公司 | Method for actively identifying accidents based on Internet of vehicles |
| US11798321B2 (en) | 2020-08-28 | 2023-10-24 | ANI Technologies Private Limited | Driver score determination for vehicle drivers |
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| CN113288148B (en) * | 2021-06-02 | 2023-03-07 | 华南师范大学 | Driving psychological quality classification method |
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