CN114771545A - Intelligent safe driving system - Google Patents
Intelligent safe driving system Download PDFInfo
- Publication number
- CN114771545A CN114771545A CN202210408461.7A CN202210408461A CN114771545A CN 114771545 A CN114771545 A CN 114771545A CN 202210408461 A CN202210408461 A CN 202210408461A CN 114771545 A CN114771545 A CN 114771545A
- Authority
- CN
- China
- Prior art keywords
- driver
- driving
- detecting
- state
- safety
- 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.)
- Pending
Links
- 238000001514 detection method Methods 0.000 claims abstract description 70
- 230000006399 behavior Effects 0.000 claims abstract description 41
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims description 21
- 210000003128 head Anatomy 0.000 claims description 19
- 230000008921 facial expression Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 14
- 238000005516 engineering process Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 201000010099 disease Diseases 0.000 claims description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 6
- 230000006996 mental state Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 206010039203 Road traffic accident Diseases 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 206010000117 Abnormal behaviour Diseases 0.000 abstract description 3
- 230000015654 memory Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000036544 posture Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0818—Inactivity or incapacity of driver
- B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Human Computer Interaction (AREA)
- Mathematical Physics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention is suitable for the automatic driving field, has provided a kind of intellectual safe driving system, including: the internal safety detection module is used for detecting whether the driving state of the driver meets an ideal driving standard or not; the external safety detection module is used for detecting the road condition information around the vehicle and the vehicle condition; the intelligent safety processing module is used for integrating the detection results of the internal safety detection module and the external safety detection module, reminding a driver of the driving state in a slight fatigue state, warning severe interference behaviors around the driver and carrying out forced safety protection processing on the severe fatigue driving condition of the driver, and has the advantages that: early warning is performed when abnormal behaviors happen to the environment or the subjectivity of a driver, and the accident occurrence frequency is effectively reduced.
Description
Technical Field
The invention belongs to the field of automatic driving, and particularly relates to an intelligent safe driving system.
Background
Automatic driving, also known as unmanned driving, computer driving or wheeled mobile robot, is a leading-edge technology that relies on computers and artificial intelligence technology to complete, safe and effective driving without artificial manipulation, and in the 21 st century, the problems of congestion, safety accidents and the like faced by road traffic are more and more serious due to the continuous increase of automobile users. The automatic driving technology can coordinate the travel route and the planning time under the support of the car networking technology and the artificial intelligence technology, so that the travel efficiency is greatly improved, and the energy consumption is reduced to a certain extent. Automatic driving can help avoid the hidden danger such as drunk driving, driver's mistake, promotes the security simultaneously, and automatic driving also becomes a research and development focus in recent years of each country consequently, but in the prior art, especially manual driving to the transition stage of automatic driving, because driver's subjective and the objective factor of environment, the driving accident easily produces.
Disclosure of Invention
The embodiment of the invention aims to provide an intelligent safe driving system, aims to solve the problem that in the transition period from traditional driving to automatic driving, aims to guarantee driving safety and realize safe and intelligent driving, designs an intelligent safe driving detection system, and effectively reduces road traffic accidents caused by fatigue driving and environmental factors by applying relevant technologies and knowledge in the field of Internet of things.
The embodiment of the invention is realized in such a way, and the intelligent safe driving system comprises:
the internal safety detection module is used for detecting whether the driving state of the driver meets an ideal driving standard or not;
the external safety detection module is used for detecting road condition information around the vehicle and vehicle conditions;
and the intelligent safety processing module is used for integrating detection results of the internal safety detection module and the external safety detection module, reminding a driver of the driving state in a slight fatigue state, warning severe interference behaviors around the driver, and carrying out forced safety protection processing on severe fatigue driving conditions of the driver.
As a further aspect of the present invention, the internal security detection module includes:
driver's own condition detection unit: the device is used for detecting the mental state of a driver in the driving process, avoiding the occurrence of traffic accidents caused by sudden diseases and fatigue driving of the driver in the driving process, and detecting the driving behavior of the driver in real time by a computer vision technology to realize the judgment and detection of the current driving state of the driver;
external interference detection unit: the method is used for detecting external interference to normal driving behaviors, which may occur in the driving process of a driver, and the external interference detection comprises detecting whether the interference of a person irrelevant to the driving behaviors to limbs of the driver exists in a target limited driving area of the driver, and if so, judging that the external interference exists.
As a further aspect of the present invention, the detecting the mental state of the driver during driving specifically includes:
reading face information of a driver;
after an original frame image is obtained, image processing is carried out through OpenCV, and a characteristic value is extracted for logic analysis;
inputting the extracted characteristic values into a pre-established Openpos skeleton model, and then performing limb posture calculation to obtain a limb behavior detection result;
preliminarily detecting the fatigue degree of the driver by the extracted characteristic value through a PERCLOS algorithm, calculating the aspect ratio of eyes and the aspect ratio of a mouth, and judging the facial expression;
performing attitude calculation on the head of the driver based on the extracted characteristic value, solving the Euler angle of the head, and performing head angle detection;
comparing the limb behavior detection result, the facial expression judgment result and the head angle detection result with a first threshold and a second threshold;
when one of the body behaviors, the facial expressions and the head angles exceeds a preset corresponding first threshold value and is smaller than a second threshold value, the driving state of the driver is indicated to be in a slight fatigue state, and when the one of the body behaviors, the facial expressions and the head angles is not smaller than the second threshold value, the driving state of the driver is indicated to be in a severe fatigue state.
The high-performance image processing embedded development gradually becomes the trend of the times in the field of unmanned driving, the fatigue driving detection system is re-planned and distributed, the Internet of things control is added, and the fatigue driving detection system is deployed in a RISC-V framework to carry out the continuation development and use of the Internet of things.
The fatigue degree of a driver is preliminarily detected through a PERCLOS algorithm, the aspect ratio of eyes and the aspect ratio of a mouth are calculated, and preliminary judgment is carried out. In addition, the invention can also judge the ordinary operation action of the driver, classify the ordinary operation action and early warn when the abnormal behavior occurs, thereby effectively reducing the accident occurrence frequency.
As still further aspect of the present invention, the external security detection module includes:
vehicle surrounding road condition detection unit: the road bed sensor is used for monitoring road conditions in real time based on roads and infrastructure, feeding back the road conditions to nearby running vehicles through a traffic management center, and carrying out safety detection on the current driving road conditions of the vehicles;
a vehicle condition detection unit: the method is used for ensuring unsafe conditions caused by the objective factors of the automobile in the driving process through detecting the basic performance devices of the automobile before the driving behavior starts.
As a further aspect of the present invention, the intelligent security processing module includes:
the reminding unit is used for taking voice and light reminding measures for unreasonable driving conditions which do not cause serious consequences on normal driving behaviors, such as a slight fatigue state of a driver, a poor current road condition and the like;
the warning unit is used for taking language warning and data uploading and archiving serious measures when the more serious interference behavior around the driver affects the normal driving behavior of the driver;
and the mandatory safety protection processing unit is used for adopting mandatory safety protection processing when the driver has serious fatigue driving conditions, sudden diseases and poor vehicle safety conditions.
As a further aspect of the present invention, the comparing the limb behavior detection result, the facial expression judgment result, and the head angle detection result with the first threshold and the second threshold specifically includes:
calling whether a current driver is in a face library which is input in advance or not in the face library, and calling threshold data which is stored in advance in the face library if the current driver is in the face library;
if not, threshold data is calculated by the associated algorithm.
As a further scheme of the present invention, the mandatory safety protection processing includes stopping the vehicle, and uploading to a cloud via the internet of vehicles to remind other surrounding vehicles of emergency avoidance.
According to the intelligent safe driving system provided by the embodiment of the invention, the detection results of the internal safety detection module and the external safety detection module are integrated, the driving state of the driver is reminded when in a slight fatigue state, the driver is warned of more serious interference behaviors around the driver, the driver is subjected to forced safety protection treatment when in serious fatigue driving, early warning is carried out when abnormal behaviors occur in the environment or the subjectively of the driver, and the accident occurrence frequency is effectively reduced.
Drawings
Fig. 1 is a schematic diagram of a main structure of an intelligent safe driving system.
Fig. 2 is a schematic structural diagram of an internal safety detection module in an intelligent safety driving system.
Fig. 3 is a schematic diagram of a real-time process in an intelligent safe driving system.
Fig. 4 is a schematic diagram of eye calculation in an intelligent safe driving system.
Fig. 5 is a schematic diagram of a smart safe driving system mouth calculation.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Specific implementations of the present invention are described in detail below with reference to specific embodiments.
The invention provides an intelligent safe driving system, which solves the technical problem in the background technology.
As shown in fig. 1 to 5, a main structure of an intelligent safety driving system according to an embodiment of the present invention is schematically illustrated, and the intelligent safety driving system includes:
the internal safety detection module is used for detecting whether the driving state of the driver meets an ideal driving standard or not;
the external safety detection module is used for detecting road condition information around the vehicle and vehicle conditions;
and the intelligent safety processing module is used for integrating detection results of the internal safety detection module and the external safety detection module, reminding when the driving state of the driver is in a slight fatigue state, warning relatively serious interference behaviors around the driver, and carrying out forced safety protection processing on the serious fatigue driving condition of the driver.
As a preferred embodiment of the present invention, the internal security detection module includes:
driver's own condition detection means: the device is used for detecting the mental state of a driver in the driving process, avoiding the occurrence of traffic accidents caused by sudden diseases and fatigue driving of the driver in the driving process, and detecting the driving behavior of the driver in real time by a computer vision technology to realize the judgment and detection of the current driving state of the driver;
external interference detection unit: the method is used for detecting external interference to normal driving behaviors, which may occur in the driving process of a driver, and the external interference detection comprises detecting whether the interference of a person irrelevant to the driving behaviors to limbs of the driver exists in a target limited driving area of the driver, and if so, judging that the external interference exists.
As shown in fig. 2-5: as another preferred embodiment of the present invention, the detecting the mental state of the driver during driving specifically includes:
reading face information of a driver;
after obtaining an original frame image, performing image processing through OpenCV, and extracting a characteristic value to perform logic analysis;
inputting the extracted characteristic values into a pre-established Openpos skeleton model, and then performing limb posture calculation to obtain a limb behavior detection result;
preliminarily detecting the fatigue degree of the driver by the extracted characteristic value through a PERCLOS algorithm, calculating the aspect ratio of eyes and the aspect ratio of a mouth, and judging the facial expression;
wherein the eye aspect ratio
Aspect ratio of mouth
Calculating the posture of the head of the driver based on the extracted characteristic value, solving the Euler angle of the head, and detecting the head angle;
comparing the limb behavior detection result, the facial expression judgment result and the head angle detection result with a first threshold value and a second threshold value;
when one of the limb behaviors, the facial expressions and the head angles exceeds a preset corresponding first threshold and is smaller than a second threshold, the driving state of the driver is indicated to be in a slight fatigue state, and when the driving state of the driver is not smaller than the second threshold, the driving state of the driver is indicated to be in a severe fatigue state:
as another preferred embodiment of the present invention, the external security detection module includes:
vehicle surrounding road condition detection unit: the road bed sensor is used for monitoring road conditions in real time based on road and infrastructure, feeding back the road conditions to nearby running vehicles through a traffic management center, and performing safety detection on the current driving road conditions of the vehicles;
a vehicle condition detection unit: the method is used for ensuring unsafe conditions caused by the objective factors of the automobile in the driving process through detecting basic performance devices of the automobile before the driving behavior starts:
as another preferred embodiment of the present invention, the intelligent security processing module includes:
the reminding unit is used for taking voice and light reminding measures for unreasonable driving conditions, such as slight fatigue state of a driver, poor current road conditions and the like, which do not cause serious consequences on normal driving behaviors;
the warning unit is used for taking language warning and data uploading and archiving serious measures when the more serious interference behavior around the driver affects the normal driving behavior of the driver;
and the mandatory safety protection processing unit is used for adopting mandatory safety protection processing when the driver has serious fatigue driving conditions, sudden diseases and poor vehicle safety conditions.
The mandatory safety protection processing comprises stopping the vehicle, uploading the cloud end to the Internet of vehicles to remind other vehicles around to avoid emergently
As another preferred embodiment of the present invention, the comparing the limb behavior detection result, the facial expression judgment result, and the head angle detection result with the first threshold and the second threshold specifically includes:
calling whether a current driver is in a face library which is input in advance or not in the face library, and calling threshold data which is stored in advance in the face library if the current driver is in the face library;
if not, threshold data is calculated by the associated algorithm.
The invention provides an intelligent safe driving system which comprises the following components: has the following advantages:
1. and the camera is used for monitoring the expression, behavior and the like of the driver in real time, and the driver information is acquired in real time. The whole image is processed through OpenCV, characteristic values are extracted for logic analysis, and algorithm sampling is carried out.
2. And in the later stage of development, block modeling function selection is carried out, other open source libraries are adopted for human body modeling, including facial expressions and driver postures, and a reasonable analysis is carried out on the conditions in the vehicle.
3. The system is communicated with the driving recorder in the vehicle in an internet of things way, effectively connected with each large real-condition traffic map and used for carrying out comprehensive logic processing on the conditions outside the vehicle, so that the system can obtain overall grasp on the whole road conditions and the conditions in the vehicle.
4. Data connection with the upstream of an operation mechanism is well carried out, and a proper database and a client side are built, so that the behavior of a driver is more specific and observable, and the management is convenient.
In order to load the above method and system to operate successfully, the system may include more or less components than those described above, or combine some components, or different components, in addition to the various modules described above, for example, input/output devices, network access devices, buses, processors, memories, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, 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 component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center of the system and which is connected to the various parts using various interfaces and lines.
The memory may be used to store computer and system programs and/or modules, and the processor may implement the various functions by operating or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, an application program required by at least one function (such as an information collection template presentation function, a product information distribution function, and the like), and the like. The storage data area may store data created according to the use of the berth status display system (such as product information acquisition templates corresponding to different product categories, product information that needs to be issued by different product providers, and the like). In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash memory card (F l ashCard), at least one disk storage device, a flash memory device, or other volatile solid state storage device.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of sub-steps or stages of other steps.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (7)
1. An intelligent safe driving system, comprising:
the internal safety detection module is used for detecting whether the driving state of the driver meets an ideal driving standard or not;
the external safety detection module is used for detecting road condition information around the vehicle and vehicle conditions;
and the intelligent safety processing module is used for integrating detection results of the internal safety detection module and the external safety detection module, reminding when the driving state of the driver is in a slight fatigue state, warning relatively serious interference behaviors around the driver, and carrying out forced safety protection processing on the serious fatigue driving condition of the driver.
2. The smart safe driving system according to claim 1, wherein the internal safety detection module comprises:
driver's own condition detection unit: the device is used for detecting the mental state of a driver in the driving process, avoiding the occurrence of traffic accidents caused by sudden diseases and fatigue driving of the driver in the driving process, and detecting the driving behavior of the driver in real time by a computer vision technology to realize the judgment and detection of the current driving state of the driver;
external interference detection unit: the method is used for detecting the external interference to the normal driving behaviors, which may occur in the driving process of a driver, wherein the external interference detection comprises detecting whether the limb interference of a person irrelevant to the driving behaviors to the driver exists in a target limited driving area of the driver, and if so, judging that the external interference exists.
3. The intelligent safe driving system according to claim 2, wherein the detecting of the mental state of the driver during driving specifically comprises:
reading face information of a driver;
after an original frame image is obtained, image processing is carried out through OpenCV, and a characteristic value is extracted for logic analysis;
inputting the extracted characteristic values into a pre-established Openpos skeleton model, and then performing limb posture calculation to obtain a limb behavior detection result;
preliminarily detecting the fatigue degree of the driver by the extracted characteristic value through a PERCLOS algorithm, calculating the aspect ratio of eyes and the aspect ratio of a mouth, and judging the facial expression;
calculating the posture of the head of the driver based on the extracted characteristic value, solving the Euler angle of the head, and detecting the head angle;
comparing the limb behavior detection result, the facial expression judgment result and the head angle detection result with a first threshold and a second threshold;
when any one of the limb behaviors, the facial expressions and the head angles exceeds a preset corresponding first threshold and is smaller than a second threshold, the driving state of the driver is in a slight fatigue state, and when the driving state of the driver is not smaller than the second threshold, the driving state of the driver is in a severe fatigue state.
4. The smart safe driving system according to claim 1 or 2, wherein the external safety detection module comprises:
vehicle surrounding road condition detection unit: the road bed sensor is used for monitoring road conditions in real time based on roads and infrastructure, feeding back the road conditions to nearby running vehicles through a traffic management center, and carrying out safety detection on the current driving road conditions of the vehicles;
a vehicle condition detection unit: the method is used for ensuring unsafe conditions caused by the objective factors of the automobile in the driving process through detecting the basic performance devices of the automobile before the driving behavior starts.
5. The intelligent safe driving system according to any one of claims 1-3, wherein the intelligent safe processing module comprises:
the reminding unit is used for taking voice and light reminding measures for the unreasonable driving situation which has a slight fatigue state of a driver and does not cause serious consequences on normal driving behaviors due to poor current road conditions;
the warning unit is used for taking language warning and data uploading and archiving serious measures when the more serious interference behavior around the driver affects the normal driving behavior of the driver;
and the forced safety protection processing unit is used for adopting forced safety protection processing when a driver has severe fatigue driving conditions, sudden diseases and poor vehicle safety conditions.
6. The intelligent safe driving system according to claim 3, wherein the comparing the limb behavior detection result, the facial expression judgment result, and the head angle detection result with the first threshold and the second threshold specifically comprises:
calling whether a current driver is in a face library which is input in advance or not in the face library, and calling threshold data which is stored in advance in the library if the current driver is in the face library;
if not, threshold data is calculated by the associated algorithm.
7. The intelligent safe driving system according to claim 5, wherein the forced safe protection process comprises stopping of the vehicle, and reminding other vehicles around by uploading cloud in the internet of vehicles for emergency avoidance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210408461.7A CN114771545A (en) | 2022-04-19 | 2022-04-19 | Intelligent safe driving system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210408461.7A CN114771545A (en) | 2022-04-19 | 2022-04-19 | Intelligent safe driving system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114771545A true CN114771545A (en) | 2022-07-22 |
Family
ID=82431918
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210408461.7A Pending CN114771545A (en) | 2022-04-19 | 2022-04-19 | Intelligent safe driving system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114771545A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115439918A (en) * | 2022-10-27 | 2022-12-06 | 天津中德应用技术大学 | Method and system for monitoring driver state and readable storage medium |
| CN117622177A (en) * | 2024-01-23 | 2024-03-01 | 青岛创新奇智科技集团股份有限公司 | Vehicle data processing method and device based on industrial large model |
| CN118387114A (en) * | 2024-06-26 | 2024-07-26 | 华东交通大学 | Vehicle control system based on driver sudden disease discriminant analysis method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009087303A (en) * | 2007-10-03 | 2009-04-23 | Nissan Motor Co Ltd | Expression estimation device, expression estimation method, and vehicle controller |
| DE102009016936A1 (en) * | 2009-04-08 | 2009-11-19 | Daimler Ag | Driver assistance system for assisting vehicle's driver during tiredness, has tiredness recognition system to recognize driver's tiredness, where hazard warning signal of vehicle is activated for warning road users during tiredness |
| CN109367539A (en) * | 2018-11-01 | 2019-02-22 | 哈尔滨理工大学 | A kind of intelligence system detecting fatigue driving |
| WO2019218571A1 (en) * | 2018-05-15 | 2019-11-21 | 江南大学 | Fatigued driving early warning system based on opencv technology |
| CN110532887A (en) * | 2019-07-31 | 2019-12-03 | 郑州大学 | A kind of method for detecting fatigue driving and system based on facial characteristics fusion |
| CN112017404A (en) * | 2020-08-06 | 2020-12-01 | 上海交通大学 | Anti-fatigue driving control system based on human-computer interaction |
| CN112389448A (en) * | 2020-11-23 | 2021-02-23 | 重庆邮电大学 | Abnormal driving behavior identification method based on vehicle state and driver state |
| CN113920491A (en) * | 2021-09-07 | 2022-01-11 | 哈尔滨工业大学（威海） | Fatigue detection system, method, medium and detection device based on facial skeleton model |
-
2022
- 2022-04-19 CN CN202210408461.7A patent/CN114771545A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009087303A (en) * | 2007-10-03 | 2009-04-23 | Nissan Motor Co Ltd | Expression estimation device, expression estimation method, and vehicle controller |
| DE102009016936A1 (en) * | 2009-04-08 | 2009-11-19 | Daimler Ag | Driver assistance system for assisting vehicle's driver during tiredness, has tiredness recognition system to recognize driver's tiredness, where hazard warning signal of vehicle is activated for warning road users during tiredness |
| WO2019218571A1 (en) * | 2018-05-15 | 2019-11-21 | 江南大学 | Fatigued driving early warning system based on opencv technology |
| CN109367539A (en) * | 2018-11-01 | 2019-02-22 | 哈尔滨理工大学 | A kind of intelligence system detecting fatigue driving |
| CN110532887A (en) * | 2019-07-31 | 2019-12-03 | 郑州大学 | A kind of method for detecting fatigue driving and system based on facial characteristics fusion |
| CN112017404A (en) * | 2020-08-06 | 2020-12-01 | 上海交通大学 | Anti-fatigue driving control system based on human-computer interaction |
| CN112389448A (en) * | 2020-11-23 | 2021-02-23 | 重庆邮电大学 | Abnormal driving behavior identification method based on vehicle state and driver state |
| CN113920491A (en) * | 2021-09-07 | 2022-01-11 | 哈尔滨工业大学（威海） | Fatigue detection system, method, medium and detection device based on facial skeleton model |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115439918A (en) * | 2022-10-27 | 2022-12-06 | 天津中德应用技术大学 | Method and system for monitoring driver state and readable storage medium |
| CN117622177A (en) * | 2024-01-23 | 2024-03-01 | 青岛创新奇智科技集团股份有限公司 | Vehicle data processing method and device based on industrial large model |
| CN117622177B (en) * | 2024-01-23 | 2024-05-14 | 青岛创新奇智科技集团股份有限公司 | Vehicle data processing method and device based on industrial large model |
| CN118387114A (en) * | 2024-06-26 | 2024-07-26 | 华东交通大学 | Vehicle control system based on driver sudden disease discriminant analysis method |
| CN118387114B (en) * | 2024-06-26 | 2024-10-11 | 华东交通大学 | Vehicle control system based on driver sudden disease discriminant analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114771545A (en) | Intelligent safe driving system | |
| Singh | Learning based driver drowsiness detection model | |
| Xing et al. | Driver activity recognition for intelligent vehicles: A deep learning approach | |
| CN102263937B (en) | Driver's driving behavior monitoring device and monitoring method based on video detection | |
| Hossain et al. | IOT based real-time drowsy driving detection system for the prevention of road accidents | |
| CN101950355A (en) | Method for detecting fatigue state of driver based on digital video | |
| CN110582437A (en) | driving reminding method, driving state detection method and computing device | |
| Sathasivam et al. | Drowsiness detection system using eye aspect ratio technique | |
| DE112018007454T5 (en) | Occupant condition determination device, warning output control device, and occupant condition determination method | |
| US20220009509A1 (en) | Vehicle travel control device | |
| CN114049677A (en) | Vehicle ADAS control method and system based on emotion index of driver | |
| Sharara et al. | A real-time automotive safety system based on advanced ai facial detection algorithms | |
| Rani et al. | Development of an Automated Tool for Driver Drowsiness Detection | |
| CN113312958B (en) | Method and device for adjusting dispatch priority based on driver state | |
| CN112052829B (en) | Pilot behavior monitoring method based on deep learning | |
| CN113420656A (en) | Fatigue driving detection method and device, electronic equipment and storage medium | |
| CN117842085A (en) | Driving state detection and early warning method, driving state detection and early warning system, electronic equipment and storage medium | |
| Palaniappan et al. | Real time fatigue detection using shape predictor 68 face landmarks algorithm | |
| Abdi Khojasteh et al. | An intelligent safety system for human-centered semi-autonomous vehicles | |
| Soman et al. | Internet-of-Things-Assisted Artificial Intelligence-Enabled Drowsiness Detection Framework | |
| Poliak et al. | Analysis and research plan of commercial truck drivers’ potentially dangerous driving behaviors caused by the changes of Regulation (EC) No. 561/2006 | |
| Sontakke | Efficient driver fatigue detection and alerting system | |
| DE112022001065T5 (en) | INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING PROGRAM | |
| CN202177912U (en) | Driver driving behavior monitoring device based on video detection | |
| Gao et al. | Perceiving driving hazards in a data-fusion way using multi-modal net and semantic driving trajectory |
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 |