CN113643512A - 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
- CN113643512A CN113643512A CN202110860154.8A CN202110860154A CN113643512A CN 113643512 A CN113643512 A CN 113643512A CN 202110860154 A CN202110860154 A CN 202110860154A CN 113643512 A CN113643512 A CN 113643512A
- Authority
- CN
- China
- Prior art keywords
- fatigue
- driver
- driving
- data
- characteristic parameter
- 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.)
- Granted
Links
Images
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 fatigue driving detection 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 which meets 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 the fatigue driving reminding operation or not according to the fatigue index. The fatigue driving detection method has the advantages that the driving data, the driver image data and the driver driving card data of the vehicle are collected, the fatigue characteristic parameters capable of representing the fatigue driving of the driver are extracted according to the data, the fatigue index is comprehensively calculated based on the fatigue characteristic parameters, and then the fatigue index is used for judging whether to trigger the fatigue driving reminding operation.
Description
Technical Field
The invention relates to the technical field of vehicle monitoring, in particular to a fatigue driving detection method and device, electronic equipment and a storage medium.
Background
With the increasing number of vehicles, driving accidents are increased year by year, and fatigue driving is an important factor for causing road safety accidents. How to rapidly and accurately detect the fatigue driving condition of the driver and give a prompt has great significance for reducing accidents caused by fatigue driving.
The existing fatigue driving detection is mainly used for recording the continuous driving time of a vehicle, if the continuous driving time exceeds a set value, an alarm signal is sent out to prompt a driver to have a rest, or in the driving process of the driver, facial images of the driver are collected in real time, and the driver is judged in advance whether to be fatigue driven or not by combining a machine learning method. However, the single fatigue detection criterion is not high in detection accuracy, and false alarm and missing alarm are easy to occur.
Disclosure of Invention
The present invention is directed to a method, an apparatus, an electronic device, and a storage medium for detecting fatigue driving, which are provided to overcome the above-mentioned disadvantages of the related art.
A first aspect of the present invention provides a method for detecting fatigue driving, the method including:
acquiring driving data, driver image data and driver driving card data of a target vehicle;
determining at least one fatigue characteristic parameter which meets the conditions 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 a fatigue driving reminding operation or not according to the fatigue index.
In some embodiments of the present application, said determining, from said driving data, driver image data and driver card data, at least one fatigue characteristic parameter that meets a condition, comprises:
extracting fatigue characteristic parameters which are related to the vehicle state and meet the conditions by using 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 utilizing the driver driving card data as a fatigue characteristic parameter meeting the condition.
In some embodiments of the present application, the extracting, by using the driving data, a fatigue characteristic parameter that is related to a vehicle state and meets a condition includes:
counting the continuous driving time, the abnormal change times of the vehicle speed and the abnormal change times of the vehicle head angle according to the driving data; if the continuous driving time exceeds a second threshold value, taking the continuous driving time as a fatigue characteristic parameter meeting the condition; if the abnormal change times of the vehicle speed exceed a third threshold value, taking the abnormal change times of the vehicle speed as fatigue characteristic parameters meeting conditions; and if the abnormal change times of the head angle exceed a fourth threshold value, taking the abnormal change times of the head angle as fatigue characteristic parameters meeting conditions.
In some embodiments of the present application, the extracting, by using the driver image data, a fatigue feature parameter related to and satisfying a head state of a driver includes:
counting blink frequency, nodding frequency, yawning frequency and abnormal head inclination frequency according to the driver image data; if the blink frequency exceeds a fifth threshold value, taking the blink frequency as a fatigue characteristic parameter meeting the condition; if the nodding frequency exceeds a sixth threshold value, taking the nodding frequency as a fatigue characteristic parameter meeting the condition; if the frequency of the yawning exceeds a seventh threshold value, taking the frequency of the yawning as a fatigue characteristic parameter meeting the condition; and if the abnormal head tilting frequency exceeds an eighth threshold value, taking the abnormal head tilting frequency as a qualified fatigue characteristic parameter.
In some embodiments of the present application, said calculating a fatigue index of the driver based on said at least one fatigue characteristic parameter comprises:
determining the fatigue grade of each fatigue characteristic parameter, and taking the fraction corresponding to the fatigue grade as the fraction of the fatigue characteristic parameter; and calculating the fatigue index of the driver according to the fraction of each fatigue characteristic parameter and the weight corresponding to the fatigue grade of each fatigue characteristic parameter.
In some embodiments of the present application, said determining whether to trigger a driving fatigue reminding operation according to the fatigue index includes:
judging whether the fatigue index exceeds a ninth threshold value; and if the fatigue driving speed exceeds the ninth threshold value, triggering a fatigue relieving device and sending a voice prompt of fatigue driving.
In some embodiments of the present application, the trigger fatigue mitigation device comprises:
determining the fatigue grade of the driver according to the fatigue index; and controlling the fatigue relieving device to execute early warning reminding with strength corresponding to the fatigue grade.
A second aspect of the present invention proposes a fatigue driving detecting apparatus, the apparatus comprising:
the data collection module is used for acquiring the driving data, the driver image data and the driver driving card data of the target vehicle;
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 the fatigue driving reminding operation according to the fatigue index.
A third aspect of the present invention proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect when executing the program.
A fourth aspect of the present invention proposes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method according to the first aspect as described above.
Based on the above-mentioned first and second aspects, the present invention has at least the following advantages or advantages:
according to the invention, three dimensional data of driving data, driver image data and driver driving card data of the vehicle are collected, fatigue characteristic parameters capable of representing fatigue driving of the driver are extracted according to the three dimensional data, the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, and then whether fatigue driving reminding operation needs to be triggered 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 not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating an embodiment of a method for fatigue driving detection according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a fatigue index calculation process according to the embodiment shown in FIG. 1;
FIG. 3 is a schematic diagram illustrating a fatigue driving detection apparatus according to an exemplary embodiment of the present invention;
FIG. 4 is a diagram illustrating a hardware configuration 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 the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended 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 and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to 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 present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The invention provides an improved fatigue driving detection method for solving the problem of low detection accuracy of the current single fatigue detection criterion, which comprises the steps of obtaining 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 a 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 technical effects that can be achieved based on the above description are:
according to the invention, three dimensional data of driving data, driver image data and driver driving card data of the vehicle are collected, fatigue characteristic parameters capable of representing fatigue driving of the driver are extracted according to the three dimensional data, the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, and then whether fatigue driving reminding operation needs to be triggered is judged by utilizing the comprehensive fatigue index.
In order to make the technical solutions better understood by those skilled in the art, 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.
The first embodiment is as follows:
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 being networked, where the electronic device can acquire driving data, driver image data, and driver driving card data of a vehicle in real time, and the electronic device may be built in the vehicle or set on a comprehensive control platform of the vehicle, which is not specifically limited in this application. As shown in fig. 1, the fatigue driving detection method includes the steps of:
step 101: the driving data, the driver image data, and the driver driving card data of the target vehicle are acquired.
The driving data, the driver image data and the driver driving card data of the vehicle can reflect the fatigue state of the driver through different modes of processing.
Specifically, the travel data may include GPS data, a nose angle, a vehicle speed, a vehicle engine state, and the like. The driver image data refers to periodically acquired images of the face of the driver. The driver driving card data represents the driver identity information of the currently inserted driver card periodically reported by a card reader on the vehicle.
It should be noted that, before the source data are processed and analyzed, denoising processing may be performed on the source data to retain compliance data and improve detection accuracy.
Optionally, the denoising process may include: 1. discarding the GPS data with the longitude and latitude not within the preset range; 2. GPS data with zero longitude or latitude is lost; 3. data with the head angle not within the range of 0-360 degrees is lost; 4. the GPS data with the interval between the positioning time of the GPS data and the vehicle-mounted receiving time exceeding a certain time range is lost.
Step 102: 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, the card insertion time length exceeding the first threshold value can be extracted by using the driver driving card data as the qualified fatigue characteristic parameter.
The card inserting duration refers to the duration of the current vehicle driver's driving card inserted into the card reader, and if the duration exceeds a certain threshold, the current driver's driving behavior lasts too long, so that fatigue is easy to occur.
In an alternative embodiment, the driving data may be used to extract a fatigue characteristic parameter that is related to the vehicle state and meets certain conditions.
Optionally, the fatigue characteristics related to the vehicle state may include characteristics such as a continuous driving time period, an abnormal change in vehicle speed, an abnormal change in a nose angle, and the like.
Wherein, the continuous driving time refers to the continuous driving time without parking state, flameout state and record of replacing driver. The abnormal change of the vehicle speed refers to the phenomena of rapid acceleration and rapid deceleration in the running process of the vehicle. The abnormal change of the head angle refers to the phenomenon of abnormal lane change in the running process of the vehicle.
In a specific embodiment, the extracting process of the fatigue characteristic parameter related to the vehicle state and meeting a certain condition may include: counting the continuous driving time, the abnormal change times of the vehicle speed and the abnormal change times of the vehicle head angle according to the driving data, and then taking the continuous driving time as a fatigue characteristic parameter meeting the conditions 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; and if the abnormal change times of the head angle exceed a fourth threshold value, taking the abnormal change times of the head angle as the fatigue characteristic parameters meeting the conditions.
In order to eliminate the driving behavior of the driver in the waking state, abnormal changes of the vehicle speed and the abnormal changes of the vehicle head angle which occasionally occur in the waking state of the driver are eliminated through some threshold conditions, namely the driver does not participate in the subsequent fatigue driving detection.
It should be noted that, in order to improve the accuracy of the continuous driving duration, when the continuous driving duration is counted, whether the driver card data is inconsistent or not may be determined according to the driver card data, if so, a driver replacement record may be generated, and a driver replacement record may be added to the driver card data position where the data is inconsistent, so that when the continuous driving duration is counted, only the continuous driving duration without the parking state, the flameout state, and the driver replacement record may be counted.
Further, in order to ensure the accuracy of the driver replacement record, when the driver driving card data is inconsistent, the driver image acquired at the moment near the inconsistent driver driving card data is acquired, the driver image acquired at the moment before the inconsistent driver driving card data is acquired, the two acquired driver images are subjected to face comparison, and if the two acquired driver images are inconsistent, the driver replacement record is generated.
In an alternative embodiment, the image data of the driver can be used for extracting fatigue characteristic parameters which are related to the head state of the driver and meet certain conditions.
Optionally, during the driving process of the vehicle, the driver normally keeps a sitting posture and looks ahead, and as the fatigue degree increases, the driver frequently clicks the head, blinks, yawns and the like, so the fatigue characteristic parameters related to the head state of the driver may include a blink frequency, a nod frequency, a yawns frequency and an abnormal head inclination frequency.
In a specific embodiment, the extraction process of the fatigue characteristic parameter related to the head state of the driver may include: counting blink frequency, nodding frequency, yawning 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 value, taking the nodding frequency as a fatigue characteristic parameter meeting the condition; if the frequency of the yawning exceeds a seventh threshold value, the frequency of the yawning is taken as a fatigue characteristic parameter meeting the condition; and if the abnormal head tilting frequency exceeds an eighth threshold value, taking the abnormal head tilting frequency as a qualified fatigue characteristic parameter.
In order to eliminate the driving behavior of the driver in the waking state, the motion of nodding, blinking, yawning, abnormal head inclination and the like which occasionally occurs in the waking state of the driver is eliminated through some threshold conditions, namely the nodding, blinking, yawning and abnormal head inclination which do not meet the threshold conditions do not participate in the subsequent fatigue driving detection.
It is understood in the art that the detection of the blink, nod, yawning and abnormal head tilting motion of the driver identified according to the image data of the driver can be realized by adopting the related technology, and the application is not particularly limited to this. For example, face keypoint detection and face attribute analysis network implementations may be employed.
Step 103: calculating a fatigue index of the driver based on the determined at least one fatigue characteristic parameter.
In this embodiment, since different fatigue characteristic parameters can reflect the fatigue state of the driver from different angles, a comprehensive fatigue index is obtained by fusing a plurality of extracted fatigue characteristic parameters meeting a certain condition to reflect the fatigue degree of the driver.
It should be noted that, for the specific calculation process of the fatigue index, reference may be made to the relevant description in the following embodiments, and the detailed description of the present application is omitted here.
Step 104: and judging whether to trigger the fatigue driving reminding operation or not according to the fatigue index obtained by calculation.
In an optional specific embodiment, the fatigue index may be compared with a ninth threshold, and 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 fatigue driving is sent to remind the driver to stop for a rest as soon as possible, and the reminding is finished until the vehicle is detected to stop.
In an exemplary scene, a certain vehicle starts from the state city at 8 am for 10 minutes, continuously runs to 11G 15W national road, Hu-Kun high speed and Jing Hu high speed in midway and continuously runs to 31 minutes (the continuous running time reaches 3.3 hours), the vehicle does not stop and rest in midway, a driver frequently defaults, and meanwhile, the vehicle can rapidly accelerate and decelerate, and the fatigue characteristic parameters extracted by the scheme are as follows: and (3) yawning and abnormal vehicle speed change are carried out, the fatigue index obtained according to the fatigue characteristic parameters exceeds a fatigue threshold value, the fatigue driving reminding function is triggered at the moment, the fatigue relieving device is started, the driver finally stops in a service area at 11 points 42 for rest, the fatigue relieving device automatically stops at the moment, and the fatigue driving state of the driver is relieved.
Wherein the fatigue relieving device is provided on the vehicle. Optionally, the fatigue relieving device may be a fatigue relieving spray alarm device, an audible and visual alarm device, a seat vibration alarm device, or the like.
Optionally, under the condition that fatigue index exceeds the ninth threshold value, driver's fatigue level can be confirmed according to fatigue index, thereby fatigue is alleviated the device and is reminded according to the fatigue level of difference, set up the warning of different intensity, for example, recover spray alarm device, the device can release a small amount of spraying of alleviating during slight fatigue, the device can release more spraying of alleviating during moderate fatigue, the device can release a large amount of spraying of alleviating during severe fatigue, still can combine seat vibrations alarm device simultaneously, send the early warning jointly and remind.
Therefore, the detection process shown in the figure 1 is completed, the fatigue characteristic parameters capable of representing fatigue driving of the driver are extracted according to the three dimensional data of the driving data, the image data of the driver and the driving card data of the driver, the fatigue index is comprehensively calculated based on each fatigue characteristic parameter, and whether fatigue driving reminding operation needs to be triggered is judged by utilizing the comprehensive fatigue index.
Example two:
fig. 2 is a schematic diagram of a fatigue index calculation process according to the embodiment shown in fig. 1, and based on the embodiment shown in fig. 1, the fatigue index calculation process includes the following steps:
step 201: and determining the fatigue grade of each fatigue characteristic parameter, and taking the score corresponding to the fatigue grade as the score of the fatigue characteristic parameter.
Optionally, the threshold range of the fatigue level corresponding to each fatigue characteristic parameter and the score corresponding to each fatigue level may be set in advance according to practical experience, and the corresponding relationship between each fatigue characteristic parameter and the threshold range of different fatigue levels and the corresponding relationship between different fatigue levels and scores may be generated according to these data, so that the fatigue level to which each fatigue characteristic parameter belongs may be found according to these corresponding relationships, and the score corresponding to the fatigue level is obtained.
As shown in table 1, threshold ranges of different fatigue grades corresponding to three fatigue characteristic parameters and a score corresponding to each fatigue grade are listed, and it can be seen from table 1 that the larger the value of the fatigue characteristic parameter is, the higher the corresponding fatigue grade is, that is, the heavier the fatigue degree is, and thus the higher the corresponding score is.
TABLE 1
It should be noted that the specific values, the fatigue grade divisions and the scores of the three different fatigue characteristic parameters given in table 1 above are only an exemplary illustration.
Step 202: and calculating the fatigue index of the driver according to the fraction of each fatigue characteristic parameter and the weight corresponding to the fatigue grade of each fatigue characteristic parameter.
Optionally, a corresponding weight may be set for each fatigue level in advance, or different types of fatigue characteristic parameters may be distinguished for weight setting, that is, a group of separate weights is set for each fatigue level related to each fatigue characteristic parameter.
It should be noted that, since a plurality of fatigue characteristic parameters can be acquired from the three-dimensional source data, but not all the fatigue characteristic parameters are in accordance with the condition participating in the calculation of the fatigue index, but the fatigue characteristic parameters also belong to the characteristics reflecting the degree of fatigue of the driver, the total number of the fatigue characteristic parameters can be taken into account while utilizing the scores and weights of the fatigue characteristic parameters when calculating the fatigue index.
In specific implementation, the formula for calculating the fatigue index is as follows:
wherein m represents the total number of eligible fatigue characteristic parameters, EiFraction, σ, of the ith qualifying fatigue characteristic parameterjAnd showing the weight corresponding to the fatigue grade of the ith qualified fatigue characteristic parameter.
By way of example in table 1, it is assumed that the extracted fatigue characteristic parameters are: the yawning frequency is 0.07, the corresponding score is 2, and the weight is 0.2; the blink frequency was 0.12, corresponding to a score of 3, and a weight of 0.5The total number of parameters meeting the fatigue characteristics is 2. These values are substituted into the formula:
the available fatigue index W is 0.95, and when the ninth threshold value is set to (0.60, 1), since the fatigue index 0.95 exceeds 0.60, the fatigue relieving means is triggered.
So far, the fatigue index calculation process shown in fig. 2 is completed, each fatigue characteristic parameter is divided into corresponding fatigue grades to obtain a corresponding score for each fatigue characteristic parameter, and then 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 the 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 a fatigue driving detection device.
Fig. 3 is a flowchart illustrating an embodiment of a fatigue driving detection apparatus according to an exemplary embodiment of the present invention, the apparatus is configured to execute the fatigue driving detection method provided in any of the above 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;
the fatigue feature extraction module 320 is used for determining at least one fatigue feature parameter meeting the conditions 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 actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the invention also provides an electronic device corresponding to the fatigue driving detection method provided by the embodiment, so as to execute the fatigue driving detection method.
Fig. 4 is a hardware block 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; the communication interface 601, the processor 602 and the memory 603 communicate with each other via a bus 604. The processor 602 may perform the fatigue driving detection method described above by reading and executing machine executable instructions in the memory 603 corresponding to the control logic of the fatigue driving detection method, and the specific content of the method is described in the above embodiments, which will not be described herein again.
The memory 603 referred to in this disclosure may be any electronic, magnetic, optical, or other physical storage device that can contain stored information, such as executable instructions, data, and so forth. Specifically, the Memory 603 may be a RAM (Random Access Memory), a flash Memory, a storage drive (e.g., a hard disk drive), any type of storage disk (e.g., an optical disk, a DVD, etc.), or similar storage medium, or a combination thereof. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 601 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
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 circuits of hardware or instructions in the form of software in the processor 602. The Processor 602 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed 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 the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.
The electronic equipment provided by the embodiment of the application and the fatigue driving detection method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
Referring to fig. 5, the computer readable storage medium is an optical disc 30, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program may execute the fatigue driving detection method according to 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, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the fatigue driving detection method provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of detecting fatigue driving, the method comprising:
acquiring driving data, driver image data and driver driving card data of a target vehicle;
determining at least one fatigue characteristic parameter which meets the conditions 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 a fatigue driving reminding operation or not according to the fatigue index.
2. The method of claim 1, wherein determining the qualified at least one fatigue characterization parameter from the driving data, the driver image data, and the driver card data comprises:
extracting fatigue characteristic parameters which are related to the vehicle state and meet the conditions by using 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 utilizing the driver driving card data as a fatigue characteristic parameter meeting the condition.
3. The method according to claim 2, wherein the extracting of the fatigue characteristic parameter related to and satisfying with the vehicle state using the traveling data includes:
counting the continuous driving time, the abnormal change times of the vehicle speed and the abnormal change times of the vehicle head angle according to the driving data;
if the continuous driving time exceeds a second threshold value, taking the continuous driving time as a fatigue characteristic parameter meeting the condition;
if the abnormal change times of the vehicle speed exceed a third threshold value, taking the abnormal change times of the vehicle speed as fatigue characteristic parameters meeting conditions;
and if the abnormal change times of the head angle exceed a fourth threshold value, taking the abnormal change times of the head angle as fatigue characteristic parameters meeting conditions.
4. The method according to claim 2, wherein said extracting a fatigue characteristic parameter related to and conforming to a driver's head state using said driver image data comprises:
counting blink frequency, nodding frequency, yawning frequency and abnormal head inclination frequency according to the driver image data;
if the blink frequency exceeds a fifth threshold value, taking the blink frequency as a fatigue characteristic parameter meeting the condition;
if the nodding frequency exceeds a sixth threshold value, taking the nodding frequency as a fatigue characteristic parameter meeting the condition;
if the frequency of the yawning exceeds a seventh threshold value, taking the frequency of the yawning as a fatigue characteristic parameter meeting the condition;
and if the abnormal head tilting frequency exceeds an eighth threshold value, taking the abnormal head tilting frequency as a qualified fatigue characteristic parameter.
5. The method of claim 1, wherein said calculating a fatigue index for the driver based on said at least one fatigue characteristic parameter comprises:
determining the fatigue grade of each fatigue characteristic parameter, and taking the fraction corresponding to the fatigue grade as the fraction of the fatigue characteristic parameter;
and calculating the fatigue index of the driver according to the fraction of each fatigue characteristic parameter and the weight corresponding to the fatigue grade of each fatigue characteristic parameter.
6. The method of claim 1, wherein the determining whether to trigger a driver fatigue reminder operation based on the fatigue index comprises:
judging whether the fatigue index exceeds a ninth threshold value;
and if the fatigue driving speed exceeds the ninth threshold value, triggering a fatigue relieving device and sending a voice prompt of fatigue driving.
7. The method of claim 6, wherein the triggering fatigue mitigation device comprises:
determining the fatigue grade of the driver according to the fatigue index;
and controlling the fatigue relieving device to execute early warning reminding with strength corresponding to the fatigue grade.
8. A fatigue driving detecting apparatus, characterized in that the apparatus comprises:
the data collection module is used for acquiring the driving data, the driver image data and the driver driving card data of the target vehicle;
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 the fatigue driving reminding operation according to the fatigue index.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the processor executes the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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 true CN113643512A (en) | 2021-11-12 |
| CN113643512B 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) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114360241A (en) * | 2021-12-10 | 2022-04-15 | 斑马网络技术有限公司 | Vehicle interaction method, vehicle interaction device and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103895649A (en) * | 2014-04-10 | 2014-07-02 | 徐州柏瑞高新技术科技有限公司 | Driver safety driving warning method |
| CN104867327A (en) * | 2014-02-21 | 2015-08-26 | 中国移动通信集团公司 | Driving safety monitoring method and device |
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| 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 |
| 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 |
| CN111881799A (en) * | 2020-07-22 | 2020-11-03 | 交通运输部公路科学研究所 | Driver fatigue detection method based on multi-source 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 |
| 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 |
-
2021
- 2021-07-28 CN CN202110860154.8A patent/CN113643512B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104867327A (en) * | 2014-02-21 | 2015-08-26 | 中国移动通信集团公司 | Driving safety monitoring method and device |
| CN103895649A (en) * | 2014-04-10 | 2014-07-02 | 徐州柏瑞高新技术科技有限公司 | Driver safety driving warning method |
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| 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 |
| 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 |
| CN111881799A (en) * | 2020-07-22 | 2020-11-03 | 交通运输部公路科学研究所 | Driver fatigue detection method based on multi-source information fusion difference judgment |
| 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 |
| 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 |
| 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 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114360241A (en) * | 2021-12-10 | 2022-04-15 | 斑马网络技术有限公司 | Vehicle interaction method, vehicle interaction device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113643512B (en) | 2023-07-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109670970B (en) | Driving behavior scoring method and device and computer readable storage medium | |
| CN111311914B (en) | Vehicle driving accident monitoring method and device and vehicle | |
| US20170050599A1 (en) | Vehicle event assessment | |
| CN104599545A (en) | Driving status monitoring method and device applied to driving process and navigation device | |
| JP2007072665A (en) | Object discrimination device, object discrimination method and object discrimination program | |
| CN108466616B (en) | Method for automatically identifying collision event, storage medium and vehicle-mounted terminal | |
| CN110576856A (en) | Speed determination method and device under self-adaptive cruise state and automobile | |
| US10571290B2 (en) | Method and system for vehicle speed profile generation | |
| JP2012234293A (en) | Driver state determination device | |
| CN113460062A (en) | Driving behavior analysis system | |
| KR20210065177A (en) | Image acquisition device occlusion detection method, device, device and storage medium | |
| CN113643512B (en) | Fatigue driving detection method and device, electronic equipment and storage medium | |
| CN115107786A (en) | Driving behavior correction system and method for intelligent automobile | |
| CN109774726B (en) | Vehicle performance detection method, device, equipment and storage medium | |
| CN113239827B (en) | Intelligent anti-collision method, system, equipment and storage medium based on deep learning | |
| CN110816544B (en) | Driving behavior evaluation method and device, vehicle and Internet of vehicles cloud platform | |
| CN113468678A (en) | Method and device for calculating accuracy of automatic driving algorithm | |
| CN112309004B (en) | Virtual deceleration strip generation method and device, vehicle and storage medium | |
| CN107644475A (en) | A kind of method and apparatus and OBD boxes of vehicle collision detection | |
| Yardy et al. | Detecting malicious driving with machine learning | |
| CN112115323A (en) | Driving behavior scoring method, cloud server, vehicle-mounted terminal and system | |
| CN113192340B (en) | Method, device, equipment and storage medium for identifying highway construction vehicles | |
| CN111071258A (en) | Driving behavior detection method and device, electronic equipment and readable storage medium | |
| CN111612140B (en) | Automatic driving safety judging method, equipment and storage medium based on road side unit | |
| CN114999150A (en) | Road section congestion judging method and device, vehicle and storage medium |
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 |