CN113643512B - Fatigue driving detection method and device, electronic equipment and storage medium - Google Patents
Fatigue driving detection method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113643512B CN113643512B CN202110860154.8A CN202110860154A CN113643512B CN 113643512 B CN113643512 B CN 113643512B CN 202110860154 A CN202110860154 A CN 202110860154A CN 113643512 B CN113643512 B CN 113643512B
- Authority
- CN
- China
- Prior art keywords
- fatigue
- driver
- driving
- characteristic parameter
- fatigue characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a fatigue driving detection method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring driving data, driver image data and driver driving card data of a target vehicle; determining at least one fatigue characteristic parameter meeting the condition according to the driving data, the driver image data and the driver driving card data; calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter; and judging whether to trigger fatigue driving reminding operation according to the fatigue index. The fatigue characteristic parameters which can represent the fatigue driving of the driver are extracted according to three dimensional data of the driving data, the driver image data and the driver driving card data of the vehicle, the fatigue index is comprehensively calculated based on the fatigue characteristic parameters, and further whether the fatigue driving reminding operation is triggered or not is judged by the fatigue index.
Description
Technical Field
The invention relates to the technical field of vehicle monitoring, in particular to a fatigue driving detection method, a device, electronic equipment and a storage medium.
Background
With the increasing number of vehicles, driving accidents are increasing year by year, and fatigue driving is an important factor for inducing road safety accidents. How to quickly and accurately detect the fatigue driving condition of the driver and give a prompt has great significance for reducing accidents caused by fatigue driving.
Most of the existing fatigue driving detection records continuous driving time of a vehicle, if the continuous driving time exceeds a set value, an alarm signal is sent to prompt a driver to rest, or in the driving process of the driver, face images of the driver are collected in real time, and whether the driver is in fatigue driving or not is judged by combining a machine learning method. However, the single fatigue detection criterion has low detection accuracy, and is easy to report by mistake and not report by mistake.
Disclosure of Invention
The invention aims to provide a fatigue driving detection method, a device, electronic equipment and a storage medium aiming at the defects of the prior art, and the aim is achieved through the following technical scheme.
The first aspect of the invention provides a fatigue driving detection method, which comprises the following steps:
acquiring driving data of a target vehicle, driver image data and driver driving card data;
determining at least one fatigue characteristic parameter meeting the condition according to the driving data, the driver image data and the driver driving card data;
calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter;
and judging whether to trigger fatigue driving reminding operation according to the fatigue index.
In some embodiments of the present application, the determining the at least one fatigue characteristic parameter according to the driving data, the driver image data, and the driver driving card data includes:
extracting fatigue characteristic parameters which are related to the state of the vehicle and meet the conditions by utilizing the driving data; extracting fatigue characteristic parameters which are related to the head state of the driver and meet the conditions by utilizing the driver image data; and extracting the card inserting time length exceeding a first threshold value by using the driver driving card data as a fatigue characteristic parameter meeting the condition.
In some embodiments of the present application, the extracting fatigue characteristic parameters related to the vehicle state and conforming to the conditions using the driving data includes:
counting continuous driving time, abnormal change times of vehicle speed and abnormal change times of vehicle head angle according to the driving data; if the continuous driving duration exceeds a second threshold value, taking the continuous driving duration as a fatigue characteristic parameter meeting the condition; if the abnormal change times of the vehicle speed exceeds a third threshold value, taking the abnormal change times of the vehicle speed as fatigue characteristic parameters meeting the conditions; and if the abnormal change times of the head angle exceeds a fourth threshold value, taking the abnormal change times of the head angle as fatigue characteristic parameters meeting the conditions.
In some embodiments of the present application, the extracting fatigue characteristic parameters related to the head state of the driver and conforming to the conditions using the driver image data includes:
counting blink frequency, nodding frequency, yawing frequency and abnormal head inclination frequency according to the driver image data; if the blink frequency exceeds a fifth threshold, taking the blink frequency as a fatigue characteristic parameter meeting the condition; if the nodding frequency exceeds a sixth threshold, taking the nodding frequency as a fatigue characteristic parameter meeting the condition; if the yawing frequency exceeds a seventh threshold value, taking the yawing frequency as a fatigue characteristic parameter meeting the condition; and if the head abnormal inclination frequency exceeds an eighth threshold value, taking the head abnormal inclination frequency as a fatigue characteristic parameter meeting the condition.
In some embodiments of the present application, the calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter comprises:
determining a fatigue grade to which each fatigue characteristic parameter belongs according to each fatigue characteristic parameter, and taking a score corresponding to the fatigue grade as the score of the fatigue characteristic parameter; and calculating the fatigue index of the driver according to the score of each fatigue characteristic parameter and the weight corresponding to the fatigue grade to which each fatigue characteristic parameter belongs.
In some embodiments of the present application, the determining whether to trigger the fatigue driving reminding operation according to the fatigue index includes:
judging whether the fatigue index exceeds a ninth threshold; and if the threshold value exceeds the ninth threshold value, triggering the fatigue relieving device and sending a voice prompt of fatigue driving.
In some embodiments of the present application, the trigger fatigue relief device comprises:
determining the fatigue level of the driver according to the fatigue index; and controlling the fatigue relieving device to execute early warning reminding of the strength corresponding to the fatigue grade.
A second aspect of the present invention proposes a fatigue driving detection device, the device comprising:
the data collection module is used for acquiring driving data of the target vehicle, driver image data and driver driving card data;
the fatigue characteristic extraction module is used for determining at least one fatigue characteristic parameter meeting the conditions according to the driving data, the driver image data and the driver driving card data;
a calculation module for calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter;
and the judging module is used for judging whether to trigger fatigue driving reminding operation according to the fatigue index.
A third aspect of the invention proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method according to the first aspect described above when said program is executed.
A fourth aspect of the invention proposes a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method according to the first aspect described above.
Based on the fatigue driving detection method and device described in the first and second aspects, the invention has at least the following beneficial effects or advantages:
according to the fatigue driving reminding method, the fatigue characteristic parameters which can represent fatigue driving of the driver are extracted according to three dimensional data, wherein the fatigue characteristic parameters are extracted according to the three dimensional data, and the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, so that whether the fatigue driving reminding operation needs to be triggered or not is judged by utilizing the comprehensive fatigue index.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart illustrating an embodiment of a method for detecting fatigue driving according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of a fatigue index calculation flow according to the embodiment of FIG. 1;
FIG. 3 is a schematic diagram of a fatigue driving detection device according to an exemplary embodiment of the present invention;
fig. 4 is a schematic diagram showing a hardware structure of an electronic device according to an exemplary embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a structure of a storage medium according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
In order to solve the problem of low detection accuracy of the existing single fatigue detection criterion, the invention provides an improved fatigue driving detection method, namely, the running data of a target vehicle, the image data of a driver and the driving card data of the driver are obtained, at least one fatigue characteristic parameter meeting the condition is determined according to the running data, the image data of the driver and the driving card data of the driver, then the fatigue index of the driver is calculated based on the at least one fatigue characteristic parameter, and whether the fatigue driving reminding operation is triggered or not is judged according to the fatigue index.
The technical effects which can be achieved based on the above description are as follows:
according to the fatigue driving reminding method, the fatigue characteristic parameters which can represent fatigue driving of the driver are extracted according to three dimensional data, wherein the fatigue characteristic parameters are extracted according to the three dimensional data, and the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, so that whether the fatigue driving reminding operation needs to be triggered or not is judged by utilizing the comprehensive fatigue index.
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.
Embodiment one:
fig. 1 is a flowchart of an embodiment of a fatigue driving detection method according to an exemplary embodiment of the present invention, where the fatigue driving detection method may be applied to any electronic device capable of networking, and the electronic device may acquire driving data of a vehicle, image data of a driver, and driving card data of the driver in real time, and may be built in the vehicle or may be disposed on a comprehensive control platform of the vehicle, which is not limited in this application specifically. As shown in fig. 1, the fatigue driving detection method includes the steps of:
step 101: travel data of the target vehicle, driver image data, and driver driving card data are acquired.
Wherein, the driving data of the vehicle, the image data of the driver and the driving card data of the driver can all reflect the fatigue state of the driver through different modes of processing.
Specifically, the travel data may include GPS data, a head angle, a vehicle speed, a vehicle engine state, and the like. The driver image data refers to a periodically acquired driver face image. The driver driving card data represent the identity information of the driver currently inserted into the driver card, which is periodically reported by a card reader on the vehicle.
It should be noted that, before processing and analyzing the source data, denoising processing may be performed on the source data to preserve the compliance data and improve the detection accuracy.
Alternatively, the denoising process may include: 1. discarding GPS data with longitude and latitude not in a preset range; 2. GPS data with zero longitude or latitude is lost; 3. discarding data with the head angle not in the range of 0-360 degrees; 4. GPS data is lost when the interval between the positioning time of the GPS data and the receiving time of the vehicle exceeds a certain time range.
Step 102: and determining at least one fatigue characteristic parameter meeting the condition according to the driving data, the driver image data and the driver driving card data.
In an alternative embodiment, driver driving card data may be used to extract a card insertion period exceeding a first threshold as a satisfactory fatigue characteristic parameter.
The card inserting duration refers to the duration of inserting the driving card of the current vehicle driver into the card reader, and if the duration exceeds a certain threshold value, the driving behavior of the current driver is indicated to last too long, so that fatigue is easy to occur.
In an alternative embodiment, the driving data may be used to extract fatigue characteristic parameters related to the vehicle state and meeting certain conditions.
Alternatively, the fatigue characteristics related to the vehicle state may include characteristics of continuous driving duration, abnormal change in vehicle speed, abnormal change in vehicle head angle, and the like.
The continuous driving duration refers to the continuous driving time when no parking state, no flameout state, and the driver record is replaced. Abnormal change of the vehicle speed refers to a phenomenon of rapid acceleration and rapid deceleration during running of the vehicle. Abnormal change of the head angle refers to abnormal lane change phenomenon during the running process of the vehicle.
In a specific embodiment, the process of extracting the fatigue characteristic parameter related to the vehicle state and meeting a certain condition may include: counting continuous driving time, abnormal change times of the vehicle speed and abnormal change times of the vehicle head angle according to the driving data, and taking the continuous driving time as a fatigue characteristic parameter meeting the condition if the continuous driving time exceeds a second threshold; if the abnormal change times of the vehicle speed exceeds a third threshold value, taking the abnormal change times as fatigue characteristic parameters meeting the conditions; if the abnormal change times of the head angle exceeds a fourth threshold value, the abnormal change times of the head angle are used as fatigue characteristic parameters meeting the conditions.
In order to exclude driving behaviors of the driver in a waking state, occasional abnormal changes of the vehicle speed and abnormal changes of the vehicle head angle in the waking state of the driver are excluded through some threshold conditions, namely, follow-up fatigue driving detection is not participated.
It should be noted that, in order to promote the accuracy of the continuous driving duration, when the continuous driving duration is counted, whether the situation of inconsistent driver driving card data exists can be determined according to the driver driving card data, if so, a replacement driver record is generated, and the replacement driver record is added at the position of the driver driving card data with inconsistent data, so that when the continuous driving duration is counted, only the duration driving time without the parking state, flameout state and replacement driver record is counted.
Further, in order to ensure the accuracy of the replacement driver record, when the inconsistent driver driving card data exists, acquiring a driver image acquired at a moment near the inconsistent driver driving card data, acquiring a driver image acquired at a moment before the inconsistent driver driving card data appears, comparing the two acquired driver images with each other, and if the two acquired driver images are inconsistent, regenerating the replacement driver record.
In an alternative embodiment, the driver image data may be used to extract fatigue characteristic parameters related to the driver's head state and meeting certain conditions.
Alternatively, the driver keeps sitting and looks ahead under normal conditions during driving, and frequent head-on, blinking, yawing and other actions occur as the fatigue degree increases, so fatigue characteristic parameters related to the head state of the driver may include blinking frequency, head-on frequency, yawing frequency, and head abnormal inclination frequency.
In a specific embodiment, the process of extracting the fatigue characteristic parameter related to the head state of the driver may include: counting blink frequency, nodding frequency, yawing frequency and abnormal head inclination frequency according to the image data of the driver, and then taking the blink frequency as a fatigue characteristic parameter meeting the condition if the blink frequency exceeds a fifth threshold value; if the nodding frequency exceeds a sixth threshold, taking the nodding frequency as a fatigue characteristic parameter meeting the condition; if the yawing frequency exceeds a seventh threshold value, the yawing frequency is used as a fatigue characteristic parameter meeting the condition; if the head abnormal inclination frequency exceeds the eighth threshold value, the head abnormal inclination frequency is taken as a fatigue characteristic parameter meeting the condition.
In order to exclude driving behaviors of the driver in a waking state, actions such as nodding, blinking, yawning, head abnormal inclination and the like which occasionally occur in the waking state of the driver are excluded through some threshold conditions, namely, nodding, blinking, yawning and head abnormal inclination which do not meet the threshold conditions do not participate in subsequent fatigue driving detection.
It will be appreciated in the art that detection of blink, nodding, yawing, head abnormal tilting motion of the driver identified from the driver image data may be implemented using related art, and is not specifically limited in this application. For example, a face keypoint detection and face attribute analysis network implementation may be employed.
Step 103: a fatigue index of the driver is calculated based on the determined at least one fatigue characteristic parameter.
In this embodiment, since different fatigue characteristic parameters may reflect the fatigue state of the driver from different angles, the fatigue degree of the driver is reflected by fusing the extracted multiple fatigue characteristic parameters meeting certain conditions to obtain a comprehensive fatigue index.
It should be noted that, for a specific calculation process of the fatigue index, reference may be made to the related description in the following examples, which are not described in detail herein.
Step 104: judging whether to trigger fatigue driving reminding operation according to the calculated fatigue index.
In an alternative specific embodiment, the fatigue index may be compared with a ninth threshold, if the fatigue index exceeds the ninth threshold, it is indicated that the current driver belongs to fatigue driving, the fatigue relieving device needs to be triggered, and a voice prompt of the fatigue driving is sent to remind the driver to stop for rest as soon as possible, until the reminding is finished when the vehicle is detected to stop.
In an exemplary scenario, a certain vehicle starts from the state of the city at 8 a.10 minutes in the morning, and continuously runs to 11 points at 31 minutes (the continuous running time reaches 3.3 hours) at the high speed and the high speed of Shanghai in the Shanghai, the midway way G15W national road, the Shanghai, no stop rest is performed in the midway, frequent yawns appear on drivers, meanwhile, the vehicle also has rapid acceleration and rapid deceleration, and fatigue characteristic parameters extracted through the scheme are as follows: and (3) yawning and abnormal change of the vehicle speed, wherein the fatigue index obtained according to the fatigue characteristic parameters exceeds a fatigue threshold value, a fatigue driving reminding function is triggered at the moment, a fatigue relieving device is started, a driver finally stops and takes a rest in a service area at 11 points 42, at the moment, the fatigue relieving device automatically stops, and the fatigue driving state of the driver is relieved.
Wherein the fatigue relieving device is arranged on the vehicle. Alternatively, the fatigue relieving device can be a fatigue relieving spray alarm device, an audible and visual alarm device, a seat vibration alarm device and the like.
Optionally, under the condition that the fatigue index exceeds the ninth threshold, the fatigue grade of the driver can be determined according to the fatigue index, so that the fatigue relieving device can set alarm reminding with different intensities according to different fatigue grades, such as a fatigue relieving spray alarm device, the device can release a small amount of relieving spray when in light fatigue, the device can release more relieving spray when in medium fatigue, the device can release a large amount of relieving spray when in heavy fatigue, and meanwhile, the fatigue relieving device can also be combined with a seat vibration alarm device to jointly send out early warning reminding.
The detection flow shown in the figure 1 is completed, three dimensional data of the driving data, the driver image data and the driver driving card data of the vehicle are collected, each fatigue characteristic parameter capable of representing the fatigue driving of the driver is extracted according to the three dimensional data, the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, and further whether the fatigue driving reminding operation needs to be triggered is judged by utilizing the comprehensive fatigue index.
Embodiment two:
fig. 2 is a schematic diagram of a fatigue index calculation flow according to the embodiment shown in fig. 1, and based on the embodiment shown in fig. 1, the fatigue index calculation flow includes the following steps:
step 201: and determining the fatigue grade to which each fatigue characteristic parameter belongs according to each fatigue characteristic parameter, and taking the score corresponding to the fatigue grade as the score of the fatigue characteristic parameter.
Optionally, a threshold range of the fatigue grade corresponding to each fatigue characteristic parameter and scores corresponding to the fatigue grades may be set in advance according to practical experience, and according to these data, a corresponding relationship between each fatigue characteristic parameter and the threshold range of different fatigue grades and a corresponding relationship between different fatigue grades and scores may be generated, so that according to these corresponding relationships, the fatigue grade to which each fatigue characteristic parameter belongs may be found, and then the score corresponding to the fatigue grade may be obtained.
As shown in table 1, the threshold ranges of different fatigue grades corresponding to the three fatigue characteristic parameters and the score corresponding to each fatigue grade are listed, and as can be seen from table 1, the greater the fatigue characteristic parameter is, the higher the fatigue grade, that is, the higher the fatigue degree is, and thus the higher the score is.
TABLE 1
It should be noted that the specific values, fatigue grade and scores of the three different fatigue characteristic parameters given in table 1 are only one exemplary illustration.
Step 202: and calculating the fatigue index of the driver according to the score of each fatigue characteristic parameter and the weight corresponding to the fatigue grade to which each fatigue characteristic parameter belongs.
Optionally, a corresponding weight may be preset for each fatigue level, or different kinds of fatigue characteristic parameters may be differentiated to perform weight setting, that is, each fatigue level related to each fatigue characteristic parameter is correspondingly set with a group of independent weights.
It should be noted that, since a plurality of fatigue characteristic parameters can be obtained from the source data in the three dimensions, but not all the fatigue characteristic parameters meet the conditions involved in calculating the fatigue index, but these fatigue characteristic parameters also belong to the characteristics reflecting the fatigue degree of the driver, when calculating the fatigue index, the total number of the fatigue characteristic parameters can be considered while using the score and the weight of the fatigue characteristic parameters.
In specific implementation, the fatigue index is calculated by the following formula:
wherein m represents the total number of fatigue characteristic parameters meeting the conditions, E i Score, σ, representing the ith eligible fatigue feature parameter j And (5) representing the weight corresponding to the fatigue grade to which the ith qualified fatigue characteristic parameter belongs.
For example, from table 1, assume that the extracted eligible fatigue characteristic parameters are: yawning and abnormal change of vehicle speed, wherein the yawning frequency is 0.07, the corresponding fraction is 2, and the weight is 0.2; the blink frequency is 0.12, the corresponding score is 3, the weight is 0.5, and the total number of the parameters meeting the fatigue characteristic is 2. Substituting these values into the formula:the fatigue index w=0.95 is obtainable, and when the ninth threshold is set to (0.60,1), the fatigue relieving means is triggered because the fatigue index 0.95 exceeds 0.60.
The fatigue index calculation flow shown in fig. 2 is completed, each fatigue characteristic parameter is divided into corresponding fatigue grades, so that a corresponding score is obtained for each fatigue characteristic parameter, and a final fatigue index is obtained according to the score of each fatigue characteristic parameter and the weight of the fatigue grade to which the score belongs, the fatigue index comprehensively reflects the fatigue degree of a driver, and the fatigue index can accurately judge the fatigue driving of the driver.
Corresponding to the embodiment of the fatigue driving detection method, the invention also provides an embodiment of the fatigue driving detection device.
Fig. 3 is a flowchart of an embodiment of a fatigue driving detection apparatus according to an exemplary embodiment of the present invention, where the apparatus is configured to perform the fatigue driving detection method provided in any one of the foregoing embodiments, and as shown in fig. 3, the fatigue driving detection apparatus includes:
a data collection module 310 for acquiring driving data of a target vehicle, driver image data, and driver driving card data;
a fatigue feature extraction module 320, configured to determine at least one fatigue feature parameter that meets a condition according to the driving data, the driver image data, and the driver driving card data;
a calculation module 330 for calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter;
and the judging module 340 is configured to judge whether to trigger a fatigue driving reminding operation according to the fatigue index.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The embodiment of the invention also provides electronic equipment corresponding to the fatigue driving detection method provided by the embodiment of the invention, so as to execute the fatigue driving detection method.
Fig. 4 is a hardware configuration diagram of an electronic device according to an exemplary embodiment of the present invention, the electronic device including: a communication interface 601, a processor 602, a memory 603 and a bus 604; wherein the communication interface 601, the processor 602 and the memory 603 perform communication with each other via a bus 604. The processor 602 may perform the above-described fatigue driving detection method by reading and executing machine-executable instructions in the memory 603 corresponding to the control logic of the fatigue driving detection method, the details of which are referred to in the above-described embodiments and will not be described again here.
The memory 603 referred to herein may be any electronic, magnetic, optical, or other physical storage device that may contain stored information, such as executable instructions, data, or the like. In particular, the memory 603 may be RAM (Random Access Memory ), flash memory, a storage drive (e.g., hard drive), any type of storage disk (e.g., optical disk, DVD, etc.), or a similar storage medium, or a combination thereof. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 601 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 604 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. The memory 603 is configured to store a program, and the processor 602 executes the program after receiving an execution instruction.
The processor 602 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 602. The processor 602 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor.
The electronic device provided by the embodiment of the application and the fatigue driving detection method provided by the embodiment of the application are the same in invention conception, and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
The present embodiment also provides a computer readable storage medium corresponding to the fatigue driving detection method provided in the foregoing embodiment, referring to fig. 5, the computer readable storage medium is shown as an optical disc 30, on which a computer program (i.e. a program product) is stored, where the computer program, when executed by a processor, performs the fatigue driving detection method provided in any of the foregoing embodiments.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application has the same beneficial effects as the method adopted, operated or implemented by the application program stored therein, because of the same inventive concept as the method for detecting fatigue driving provided by the embodiment of the present application.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.
Claims (7)
1. A method for detecting fatigue driving, the method comprising:
acquiring driving data of a target vehicle, driver image data and driver driving card data;
determining at least one fatigue characteristic parameter meeting the condition according to the driving data, the driver image data and the driver driving card data; the driver driving card data represent driver identity information of a currently inserted driver card, which is periodically reported by a card reader on a target vehicle;
calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter, comprising:
determining a fatigue grade to which each fatigue characteristic parameter belongs according to each fatigue characteristic parameter, and taking a score corresponding to the fatigue grade as the score of the fatigue characteristic parameter;
according to the score of each fatigue characteristic parameter and the weight corresponding to the fatigue grade to which each fatigue characteristic parameter belongs, the fatigue index of the driver is calculated, and the calculation formula is as follows:
wherein m represents the total number of the fatigue characteristic parameters meeting the conditions, ei represents the fraction of the fatigue characteristic parameter meeting the conditions, and sigma j represents the weight corresponding to the fatigue grade to which the fatigue characteristic parameter meeting the conditions belongs;
judging whether to trigger fatigue driving reminding operation according to the fatigue index;
wherein the determining at least one fatigue characteristic parameter meeting the condition according to the driving data, the driver image data and the driver driving card data comprises the following steps:
extracting fatigue characteristic parameters which are relevant to the state of the vehicle and meet the conditions by utilizing the driving data, wherein the method comprises the following steps of:
counting continuous driving time, abnormal change times of vehicle speed and abnormal change times of vehicle head angle according to the driving data; wherein, the continuous driving duration refers to the continuous driving time without a parking state, a flameout state and a replacement driver record;
if the continuous driving duration exceeds a second threshold value, taking the continuous driving duration as a fatigue characteristic parameter meeting the condition;
if the abnormal change times of the vehicle speed exceeds a third threshold value, taking the abnormal change times of the vehicle speed as fatigue characteristic parameters meeting the conditions;
if the abnormal change times of the head angle exceeds a fourth threshold value, taking the abnormal change times of the head angle as fatigue characteristic parameters meeting the conditions;
extracting fatigue characteristic parameters which are related to the head state of the driver and meet the conditions by utilizing the driver image data;
extracting card inserting time length exceeding a first threshold value by using the driver driving card data as a fatigue characteristic parameter meeting the condition; the card inserting duration refers to the duration of inserting the driving card of the driver of the current vehicle into the card reader.
2. The method according to claim 1, wherein the extracting, using the driver image data, fatigue characteristic parameters related to a driver head state and conforming to conditions includes:
counting blink frequency, nodding frequency, yawing frequency and abnormal head inclination frequency according to the driver image data;
if the blink frequency exceeds a fifth threshold, taking the blink frequency as a fatigue characteristic parameter meeting the condition;
if the nodding frequency exceeds a sixth threshold, taking the nodding frequency as a fatigue characteristic parameter meeting the condition;
if the yawing frequency exceeds a seventh threshold value, taking the yawing frequency as a fatigue characteristic parameter meeting the condition;
and if the head abnormal inclination frequency exceeds an eighth threshold value, taking the head abnormal inclination frequency as a fatigue characteristic parameter meeting the condition.
3. The method of claim 1, wherein the determining whether to trigger a fatigue driving reminder operation based on the fatigue index comprises:
judging whether the fatigue index exceeds a ninth threshold;
and if the threshold value exceeds the ninth threshold value, triggering the fatigue relieving device and sending a voice prompt of fatigue driving.
4. A method according to claim 3, wherein the triggering fatigue mitigation device comprises:
determining the fatigue level of the driver according to the fatigue index;
and controlling the fatigue relieving device to execute early warning reminding of the strength corresponding to the fatigue grade.
5. A fatigue driving detection device, characterized in that the device comprises:
the data collection module is used for acquiring driving data of the target vehicle, driver image data and driver driving card data;
the fatigue characteristic extraction module is used for determining at least one fatigue characteristic parameter meeting the conditions according to the driving data, the driver image data and the driver driving card data; the driver driving card data represent driver identity information of a currently inserted driver card, which is periodically reported by a card reader on a target vehicle;
the fatigue feature extraction module is further configured to extract, using the driving data, a fatigue feature parameter related to a vehicle state and meeting a condition, and includes:
counting continuous driving time, abnormal change times of vehicle speed and abnormal change times of vehicle head angle according to the driving data; wherein, the continuous driving duration refers to the continuous driving time without a parking state, a flameout state and a replacement driver record;
if the continuous driving duration exceeds a second threshold value, taking the continuous driving duration as a fatigue characteristic parameter meeting the condition;
if the abnormal change times of the vehicle speed exceeds a third threshold value, taking the abnormal change times of the vehicle speed as fatigue characteristic parameters meeting the conditions;
if the abnormal change times of the head angle exceeds a fourth threshold value, taking the abnormal change times of the head angle as fatigue characteristic parameters meeting the conditions;
extracting fatigue characteristic parameters which are related to the head state of the driver and meet the conditions by utilizing the driver image data;
extracting card inserting time length exceeding a first threshold value by using the driver driving card data as a fatigue characteristic parameter meeting the condition; the card inserting duration refers to the duration of inserting a driving card of a current vehicle driver into a card reader;
a calculation module for calculating a fatigue index of the driver based on the at least one fatigue characteristic parameter, comprising: determining a fatigue grade to which each fatigue characteristic parameter belongs according to each fatigue characteristic parameter, and taking a score corresponding to the fatigue grade as the score of the fatigue characteristic parameter;
according to the score of each fatigue characteristic parameter and the weight corresponding to the fatigue grade to which each fatigue characteristic parameter belongs, the fatigue index of the driver is calculated, and the calculation formula is as follows:
wherein m represents the total number of the fatigue characteristic parameters meeting the conditions, ei represents the fraction of the fatigue characteristic parameter meeting the conditions, and sigma j represents the weight corresponding to the fatigue grade to which the fatigue characteristic parameter meeting the conditions belongs;
and the judging module is used for judging whether to trigger fatigue driving reminding operation according to the fatigue index.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1-4 when the program is executed by the processor.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110860154.8A CN113643512B (en) | 2021-07-28 | 2021-07-28 | Fatigue driving detection method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110860154.8A CN113643512B (en) | 2021-07-28 | 2021-07-28 | Fatigue driving detection method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113643512A CN113643512A (en) | 2021-11-12 |
| CN113643512B true CN113643512B (en) | 2023-07-18 |
Family
ID=78418767
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110860154.8A Active CN113643512B (en) | 2021-07-28 | 2021-07-28 | Fatigue driving detection method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113643512B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114360241B (en) * | 2021-12-10 | 2023-05-16 | 斑马网络技术有限公司 | Vehicle interaction method, vehicle interaction device and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| CN108230619A (en) * | 2016-12-14 | 2018-06-29 | 贵港市瑞成科技有限公司 | Method for detecting fatigue driving based on multi-feature fusion |
| CN108961681A (en) * | 2018-07-23 | 2018-12-07 | 奇瑞汽车股份有限公司 | Fatigue drive prompting method, apparatus and storage medium |
| CN109977761A (en) * | 2019-02-01 | 2019-07-05 | 重庆交互科技有限公司 | Fatigue driving awakening method and system based on facial state recognition |
| CN110281944A (en) * | 2019-06-12 | 2019-09-27 | 浙江亚太机电股份有限公司 | Driver status based on multi-information fusion monitors system |
| CN212484555U (en) * | 2020-07-22 | 2021-02-05 | 交通运输部公路科学研究所 | Fatigue driving multi-source information detection system |
| CN112373478A (en) * | 2020-10-10 | 2021-02-19 | 湖南北斗赛格导航科技有限公司 | Fatigue driving early warning method and device |
| CN112590801A (en) * | 2020-12-24 | 2021-04-02 | 吉林大学 | Front collision early warning control method based on fatigue degree of driver |
| CN112686161A (en) * | 2020-12-31 | 2021-04-20 | 遵义师范学院 | Fatigue driving detection method based on neural network |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867327B (en) * | 2014-02-21 | 2017-05-03 | 中国移动通信集团公司 | Driving safety monitoring method and device |
| CN103895649B (en) * | 2014-04-10 | 2016-05-25 | 徐州柏瑞高新技术科技有限公司 | A kind of driver safety driving warning method |
| CN106564429A (en) * | 2016-10-28 | 2017-04-19 | 湖南海翼电子商务股份有限公司 | Fatigue driving determination device and method |
| CN111881799B (en) * | 2020-07-22 | 2024-01-12 | 交通运输部公路科学研究所 | Driver fatigue detection method based on multisource information fusion difference judgment |
| CN111950398A (en) * | 2020-07-27 | 2020-11-17 | 上海仙豆智能机器人有限公司 | Fatigue driving processing method and device and computer storage medium |
-
2021
- 2021-07-28 CN CN202110860154.8A patent/CN113643512B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| CN108230619A (en) * | 2016-12-14 | 2018-06-29 | 贵港市瑞成科技有限公司 | Method for detecting fatigue driving based on multi-feature fusion |
| CN108961681A (en) * | 2018-07-23 | 2018-12-07 | 奇瑞汽车股份有限公司 | Fatigue drive prompting method, apparatus and storage medium |
| CN109977761A (en) * | 2019-02-01 | 2019-07-05 | 重庆交互科技有限公司 | Fatigue driving awakening method and system based on facial state recognition |
| CN110281944A (en) * | 2019-06-12 | 2019-09-27 | 浙江亚太机电股份有限公司 | Driver status based on multi-information fusion monitors system |
| CN212484555U (en) * | 2020-07-22 | 2021-02-05 | 交通运输部公路科学研究所 | Fatigue driving multi-source information detection system |
| CN112373478A (en) * | 2020-10-10 | 2021-02-19 | 湖南北斗赛格导航科技有限公司 | Fatigue driving early warning method and device |
| CN112590801A (en) * | 2020-12-24 | 2021-04-02 | 吉林大学 | Front collision early warning control method based on fatigue degree of driver |
| CN112686161A (en) * | 2020-12-31 | 2021-04-20 | 遵义师范学院 | Fatigue driving detection method based on neural network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113643512A (en) | 2021-11-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111860561B (en) | Abnormal stay behavior identification method, device and equipment of network appointment vehicle and storage medium | |
| CN111062240B (en) | Monitoring method and device for automobile driving safety, computer equipment and storage medium | |
| CN112781887B (en) | Method, device and system for testing vehicle performance | |
| CN109670970B (en) | Driving behavior scoring method and device and computer readable storage medium | |
| CN110576856A (en) | Speed determination method and device under self-adaptive cruise state and automobile | |
| CN113643512B (en) | Fatigue driving detection method and device, electronic equipment and storage medium | |
| CN112373481A (en) | Driving behavior state analysis method and device | |
| CN113468678B (en) | Method and device for calculating accuracy of automatic driving algorithm | |
| CN115107786A (en) | Driving behavior correction system and method for intelligent automobile | |
| CN112863242A (en) | Parking space detection method and device | |
| Yardy et al. | Detecting malicious driving with machine learning | |
| CN110006422B (en) | Method, device, equipment and storage medium for determining safe operation parameters of equipment | |
| CN113192340B (en) | Method, device, equipment and storage medium for identifying highway construction vehicles | |
| CN112115323A (en) | Driving behavior scoring method, cloud server, vehicle-mounted terminal and system | |
| CN114999150A (en) | Road section congestion judging method and device, vehicle and storage medium | |
| CN111612140B (en) | Automatic driving safety judging method, equipment and storage medium based on road side unit | |
| CN111341106B (en) | Traffic early warning method, device and equipment | |
| CN116416756A (en) | Fatigue driving early warning method, device, equipment and computer readable storage medium | |
| CN111382631B (en) | Identification method, identification device, terminal, server and storage medium | |
| CN113420743A (en) | Radar-based target classification method, system and storage medium | |
| CN116279469B (en) | Automatic driving vehicle following method and system | |
| JP6047071B2 (en) | Alerting device and program | |
| CN117719524B (en) | Driving safety risk identification early warning method, device, terminal and storage medium | |
| CN112232158B (en) | Training cheating verification system and method based on driving behavior characteristics | |
| CN117373150A (en) | Driving data acquisition and evaluation method, device and medium based on functional scene library |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |