CN117068182B - Intelligent cabin intelligent safety precaution method and system based on driving safety - Google Patents
Intelligent cabin intelligent safety precaution method and system based on driving safety Download PDFInfo
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- 238000013473 artificial intelligence Methods 0.000 claims description 14
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- 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
- B60W40/09—Driving style or behaviour
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- 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
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- 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
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- 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
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
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Abstract
The invention provides an intelligent cabin intelligent safety precaution method and system based on driving safety, which comprises the steps of firstly determining driving environment data and determining behavior data of a driver based on intelligent navigation and prediction functions, acquiring corresponding data from two systems of the intelligent cabin, then carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, realizing fusion and comprehensive analysis of multi-system data, providing an accurate data basis for safe driving judgment, judging whether the comprehensive identification data meets preset safe driving conditions, matching corresponding control instructions based on safe driving judgment results, carrying out intelligent operation on the intelligent cabin based on the control instructions, realizing intelligent safety precaution on the driving safety, and guaranteeing the driving safety.
Description
Technical Field
The invention relates to the technical field of intelligent cabins, in particular to an intelligent cabin intelligent safety precaution method and system based on driving safety.
Background
With the development of vehicle technology, the electrification, intellectualization and networking of vehicles are redefining the relationship of people and vehicles. The intelligent cabin in the vehicle is the most closely interacted part of the intelligent vehicle and the driver, and aims to create a brand new in-vehicle integrated digital platform for the driver of the vehicle by integrating various internet technologies and artificial intelligence technologies, so as to provide intelligent driving experience for the driver.
"Intelligent cabin" refers to an automobile cabin system that achieves personalization and intellectualization of drivers and passengers by artificial intelligence technology. The advent of this concept has brought many advantages and benefits to the driver and passengers.
Conventional car safety only allows for passive safety control in emergency situations, and potential safety risks in non-emergency situations are unavoidable.
Disclosure of Invention
The invention provides an intelligent cabin intelligent safety precaution method and system based on driving safety, which are used for solving the problems in the background technology.
An intelligent cabin intelligent safety precaution method based on driving safety comprises the following steps:
S1: determining intelligent navigation and prediction functions of the travelling crane through an artificial intelligent technology, and determining travelling crane environment data based on the intelligent navigation and prediction functions;
s2: acquiring acquisition data of a driver based on a camera and a sensor, and determining behavior data of the driver through the acquisition data;
S3: carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, and judging whether the comprehensive identification data meets preset safe driving conditions or not;
S4: based on the safe driving judgment result, matching the corresponding control instruction, and performing intelligent operation on the intelligent cabin based on the control instruction.
Preferably, in S1, determining the intelligent navigation and prediction function for the driving through the artificial intelligence technology, and determining the driving environment data based on the intelligent navigation and prediction function, includes:
acquiring a driving destination, analyzing road conditions and traffic jam by using an artificial intelligence technology to obtain intelligent navigation optimal for driving, and predicting the intelligent navigation by combining the habit of a driver to perform time and driving analysis so as to realize a prediction function;
And matching the intelligent navigation function with the prediction function, and obtaining driving environment data according to a matching result.
Preferably, in S2, acquiring the collected data of the driver based on the camera and the sensor, and determining the behavior data of the driver through the collected data includes:
acquiring acquisition data from a camera and a sensor, and classifying the acquisition data according to data types to obtain type data;
and combining the type data into corresponding data sets according to time, and packaging all the data sets to obtain the behavior data of the driver.
In this embodiment, since the behavior data needs multiple acquisitions of one type of data to comprehensively determine the behavior exhibited by the behavior data, the acquired data needs to be classified according to data types to obtain type data, and the type data is combined into a corresponding data set according to time.
Preferably, in S3, data fusion is performed on driving environment data and behavior data to obtain comprehensive identification data, including:
uniformly preprocessing driving environment data and behavior data to obtain standard driving environment data and standard behavior data;
establishing a driving environment model based on standard driving environment data, acquiring first key data which does not meet preset driving environment data in the standard driving environment data, and marking the first key data at a position corresponding to the driving environment model to obtain a first marking result;
Simulating the driving gesture of the driver based on the behavior data to obtain a gesture simulation model, acquiring second data which does not meet preset behavior data in the standard behavior data, and marking the second data at a position corresponding to the gesture simulation model to obtain a second marking result;
Based on time, matching the driving environment model with the gesture simulation model, and taking the matched data at the same time as a data whole according to a matching result to obtain a plurality of data whole;
Adding data marks to the plurality of data integers based on the first marking result and the second marking result to obtain a plurality of data mark integers;
And according to the preset comprehensive data content, carrying out data extraction on the whole of the plurality of data identifiers to obtain comprehensive identification data.
Preferably, in S3, determining whether the comprehensive identification data meets a preset safe driving condition includes:
acquiring a common safe driving condition and a special safe driving condition from preset safe driving conditions;
acquiring special identification data with data identification and common identification data without data identification from the comprehensive identification data;
performing first safety judgment on the common identification data based on the common safety driving conditions, and acquiring local common identification data which does not meet the corresponding common safety sub-driving conditions according to a first judgment result;
performing second safety judgment on the special identification data based on the special safety driving conditions, and acquiring local special identification data which does not meet the special safety sub-driving conditions according to a second judgment result;
and obtaining a safe driving judgment result based on the local common identification data and the local special identification data.
Preferably, the method for obtaining the safe driving judgment result based on the local common identification data and the local special identification data includes:
integrating the local common identification data and the local special identification data according to a time sequence to obtain target identification data, and determining an unsafe driving sequence based on the target identification data;
And taking the unsafe driving sequence as a safe driving judgment result.
Preferably, in S4, based on the safe driving determination result, matching the corresponding control instruction includes:
acquiring an unsafe driving sequence from a safe driving result, and judging whether the unsafe driving sequence is consistent or not;
if yes, determining a driving adjustment scheme corresponding to the unsafe driving sequence;
otherwise, judging whether the unsafe driving sequence is a periodic sequence, if so, obtaining a corresponding driving adjustment scheme based on the unsafe driving sequence in one period;
When the unsafe driving sequence is determined to be the non-periodic sequence, determining an unsafe driving fluctuation coefficient curve of the unsafe driving sequence, and determining driving association among all unsafe driving in the unsafe driving sequence;
Acquiring all driving adjustment schemes corresponding to the unsafe driving sequences, selecting a to-be-selected driving adjustment scheme meeting driving association from all driving adjustment schemes, and selecting a driving adjustment scheme eliminating the unsafe driving fluctuation coefficient curve to the maximum extent from the to-be-selected driving adjustment scheme as a final driving adjustment scheme of the unsafe driving sequences;
and calling a control instruction corresponding to the driving adjustment scheme from the instruction library.
Preferably, in S4, the intelligent cabin is intelligently operated based on the control instruction, including:
acquiring an instruction operation type of a control instruction, and determining a cabin system in an intelligent cabin corresponding to the control instruction based on the instruction operation type;
Acquiring a signal receiving interface corresponding to the cabin system, and transmitting a control instruction to the cabin system based on the signal receiving interface;
when the cabin system is determined to be one system, performing corresponding intelligent operation on the single cabin system based on the control instruction;
When the cabin system is determined to be a plurality of systems, judging whether control instructions among the systems can be operated in parallel;
if yes, performing corresponding intelligent operation on the cabin systems based on the control instruction;
otherwise, determining the execution sequence of the cabin systems, sending an operation completion signal after the previous cabin system completes the corresponding intelligent operation, and executing the corresponding intelligent operation after the next cabin system receives the operation completion signal.
Preferably, the method further comprises:
And (3) carrying out timing monitoring on each cabin system in the intelligent cabin, acquiring monitoring data, comparing the monitoring data with standard state data, and carrying out corresponding early warning and reminding according to a comparison result.
An intelligent cabin intelligent safety precaution system based on driving safety, comprising:
the first data acquisition module is used for determining intelligent navigation and prediction functions of the driving through an artificial intelligence technology and determining driving environment data based on the intelligent navigation and prediction functions;
the second data acquisition module is used for acquiring acquisition data of a driver based on the camera and the sensor, and determining behavior data of the driver through the acquisition data;
The driving judgment module is used for carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data and judging whether the comprehensive identification data meets preset safe driving conditions or not;
and the instruction operation module is used for matching corresponding control instructions based on the safe driving judgment result and performing intelligent operation on the intelligent cabin based on the control instructions.
Compared with the prior art, the invention has the following beneficial effects:
Firstly, determining driving environment data and behavior data of a driver based on intelligent navigation and prediction functions, acquiring corresponding data from two systems of an intelligent cabin, then carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, realizing fusion and comprehensive analysis of multi-system data, providing accurate data basis for safe driving judgment, judging whether the comprehensive identification data meets preset safe driving conditions or not, matching corresponding control instructions based on safe driving judgment results, carrying out intelligent operation on the intelligent cabin based on the control instructions, realizing intelligent safety precaution on driving safety, and guaranteeing driving safety.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent cabin intelligent safety precaution method based on driving safety in an embodiment of the invention;
FIG. 2 is a flow chart of a determination of data in an embodiment of the invention;
Fig. 3 is a block diagram of an intelligent cabin intelligent security system based on driving safety in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, which is shown in fig. 1 and comprises the following steps:
S1: determining intelligent navigation and prediction functions of the travelling crane through an artificial intelligent technology, and determining travelling crane environment data based on the intelligent navigation and prediction functions;
s2: acquiring acquisition data of a driver based on a camera and a sensor, and determining behavior data of the driver through the acquisition data;
S3: carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, and judging whether the comprehensive identification data meets preset safe driving conditions or not;
S4: based on the safe driving judgment result, matching the corresponding control instruction, and performing intelligent operation on the intelligent cabin based on the control instruction.
In this embodiment, the driving environment data includes data of the road condition of the vehicle, the driving speed of the driver, the direction, and the like.
In this embodiment, the collected data includes facial expressions, gestures, eye tracking, and the like.
In this embodiment, the intelligent operation includes, for example, a voice reminder, seat belt tightening, seat adjustment, and the like.
The beneficial effects of above-mentioned design scheme are: firstly, determining driving environment data and behavior data of a driver based on intelligent navigation and prediction functions, acquiring corresponding data from two systems of an intelligent cabin, then carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, realizing fusion and comprehensive analysis of multi-system data, providing accurate data basis for safe driving judgment, judging whether the comprehensive identification data meets preset safe driving conditions or not, matching corresponding control instructions based on safe driving judgment results, carrying out intelligent operation on the intelligent cabin based on the control instructions, realizing intelligent safety precaution on driving safety, and guaranteeing driving safety.
Example 2:
Based on embodiment 1, the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, in S1, the intelligent navigation and prediction function of driving is determined by artificial intelligence technology, and driving environment data is determined based on the intelligent navigation and prediction function, comprising the following steps:
acquiring a driving destination, analyzing road conditions and traffic jam by using an artificial intelligence technology to obtain intelligent navigation optimal for driving, and predicting the intelligent navigation by combining the habit of a driver to perform time and driving analysis so as to realize a prediction function;
And matching the intelligent navigation function with the prediction function, and obtaining driving environment data according to a matching result.
In this embodiment, one intelligent navigation corresponds to one prediction function data.
The beneficial effects of above-mentioned design scheme are: the intelligent navigation and prediction functions of the driving are determined through the artificial intelligence technology, and the driving environment data is determined based on the intelligent navigation and prediction functions, so that a data base is provided for subsequent safe driving judgment.
Example 3:
Based on embodiment 1, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, as shown in fig. 2, in S2, acquiring collected data of a driver based on a camera and a sensor, and determining behavior data of the driver through the collected data, wherein the method comprises the following steps:
acquiring acquisition data from a camera and a sensor, and classifying the acquisition data according to data types to obtain type data;
and combining the type data into corresponding data sets according to time, and packaging all the data sets to obtain the behavior data of the driver.
In this embodiment, since the behavior data needs multiple acquisitions of one type of data to comprehensively determine the behavior exhibited by the behavior data, the acquired data needs to be classified according to data types to obtain type data, and the type data is combined into a corresponding data set according to time.
The beneficial effects of above-mentioned design scheme are: and acquiring the acquired data of the driver based on the camera and the sensor, determining the behavior data of the driver through the acquired data, and providing a data base for subsequent safe driving judgment.
Example 4:
Based on embodiment 1, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, in S3, data fusion is carried out on driving environment data and behavior data to obtain comprehensive identification data, and the method comprises the following steps:
uniformly preprocessing driving environment data and behavior data to obtain standard driving environment data and standard behavior data;
establishing a driving environment model based on standard driving environment data, acquiring first key data which does not meet preset driving environment data in the standard driving environment data, and marking the first key data at a position corresponding to the driving environment model to obtain a first marking result;
Simulating the driving gesture of the driver based on the behavior data to obtain a gesture simulation model, acquiring second data which does not meet preset behavior data in the standard behavior data, and marking the second data at a position corresponding to the gesture simulation model to obtain a second marking result;
Based on time, matching the driving environment model with the gesture simulation model, and taking the matched data at the same time as a data whole according to a matching result to obtain a plurality of data whole;
Adding data marks to the plurality of data integers based on the first marking result and the second marking result to obtain a plurality of data mark integers;
And according to the preset comprehensive data content, carrying out data extraction on the whole of the plurality of data identifiers to obtain comprehensive identification data.
In this embodiment, the first key data and the second key data are data features in which the post-driving-environment driving posture is rare.
In this embodiment, one data center corresponds to one driving environment and one driving posture.
The beneficial effects of above-mentioned design scheme are: according to the method, a driving environment model is built according to standard driving environment data, driving gestures of a driver are simulated based on behavior data, a gesture simulation model is obtained, the driving environment model and the gesture simulation model are matched based on time, matching data at the same time are used as a data whole according to matching results, a plurality of data whole are obtained, data identification is added to the plurality of data whole based on a first marking result and a second marking result, and a plurality of data identification whole is obtained; and according to the preset comprehensive data content, carrying out data extraction on the plurality of data identifiers to obtain comprehensive identification data, realizing the association of environment data and behavior data in a mode of matching a simulation model, and marking key data to ensure that the obtained comprehensive identification data can truly reflect driving conditions and provide a data basis for intelligent cabin intelligent safety precaution.
Example 5:
Based on embodiment 4, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, in S3, judging whether comprehensive identification data meets preset safe driving conditions or not, comprising:
acquiring a common safe driving condition and a special safe driving condition from preset safe driving conditions;
acquiring special identification data with data identification and common identification data without data identification from the comprehensive identification data;
performing first safety judgment on the common identification data based on the common safety driving conditions, and acquiring local common identification data which does not meet the corresponding common safety sub-driving conditions according to a first judgment result;
performing second safety judgment on the special identification data based on the special safety driving conditions, and acquiring local special identification data which does not meet the special safety sub-driving conditions according to a second judgment result;
and obtaining a safe driving judgment result based on the local common identification data and the local special identification data.
In this embodiment, the normal safe driving condition is used to judge the normal driving condition, and the special safe driving condition is used to judge the rare driving condition.
The beneficial effects of above-mentioned design scheme are: the method comprises the steps of performing two types of safety judgment by acquiring common safety driving conditions and special safety driving conditions from preset safety driving conditions, performing first safety judgment on common identification data based on the common safety driving conditions, and acquiring local common identification data which do not meet the corresponding common safety sub-driving conditions according to a first judgment result; and carrying out second safety judgment on the special identification data based on the special safety driving conditions, and acquiring local special identification data which does not meet the corresponding special safety sub-driving conditions according to the second judgment result, so as to ensure the accuracy of the acquired safety driving judgment result.
Example 6:
based on embodiment 5, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, which obtains a safe driving judgment result based on local common identification data and local special identification data, and comprises the following steps:
integrating the local common identification data and the local special identification data according to a time sequence to obtain target identification data, and determining an unsafe driving sequence based on the target identification data;
And taking the unsafe driving sequence as a safe driving judgment result.
In this embodiment, the unsafe driving sequences are used to identify a series of unsafe driving operations in which the driver varies over time.
The beneficial effects of above-mentioned design scheme are: the local common identification data and the local special identification data are integrated according to the time sequence to obtain target identification data, an unsafe driving sequence is determined based on the target identification data, the unsafe driving sequence is used as a safe driving judgment result, and the intuitiveness and the accuracy of the obtained safe driving judgment result are ensured.
Example 7:
Based on embodiment 1, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, in S4, based on a safe driving judgment result, a corresponding control instruction is matched, and the intelligent cabin intelligent safety precaution method comprises the following steps:
acquiring an unsafe driving sequence from a safe driving result, and judging whether the unsafe driving sequence is consistent or not;
if yes, determining a driving adjustment scheme corresponding to the unsafe driving sequence;
otherwise, judging whether the unsafe driving sequence is a periodic sequence, if so, obtaining a corresponding driving adjustment scheme based on the unsafe driving sequence in one period;
When the unsafe driving sequence is determined to be the non-periodic sequence, determining an unsafe driving fluctuation coefficient curve of the unsafe driving sequence, and determining driving association among all unsafe driving in the unsafe driving sequence;
Acquiring all driving adjustment schemes corresponding to the unsafe driving sequences, selecting a to-be-selected driving adjustment scheme meeting driving association from all driving adjustment schemes, and selecting a driving adjustment scheme eliminating the unsafe driving fluctuation coefficient curve to the maximum extent from the to-be-selected driving adjustment scheme as a final driving adjustment scheme of the unsafe driving sequences;
and calling a control instruction corresponding to the driving adjustment scheme from the instruction library.
In this embodiment, the unsafe driving sequences agree to indicate that the driver is always in the same dangerous driving state, and the unsafe driving sequences disagree to indicate that the driver is in a dangerous driving change state.
In this embodiment, the driving adjustment scheme is designed in advance based on the history of driving experience.
The beneficial effects of above-mentioned design scheme are: according to the method, the driving adjustment scheme is obtained by analyzing different modes according to different sequence characteristics of the unsafe driving sequences in the safe driving result, the suitability of the obtained driving adjustment scheme and the driving condition is ensured, and the accuracy of the finally determined control instruction is ensured.
Example 8:
Based on embodiment 1, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, in S4, intelligent operation is carried out on the intelligent cabin based on control instructions, and the intelligent cabin intelligent precaution method comprises the following steps:
acquiring an instruction operation type of a control instruction, and determining a cabin system in an intelligent cabin corresponding to the control instruction based on the instruction operation type;
Acquiring a signal receiving interface corresponding to the cabin system, and transmitting a control instruction to the cabin system based on the signal receiving interface;
when the cabin system is determined to be one system, performing corresponding intelligent operation on the single cabin system based on the control instruction;
When the cabin system is determined to be a plurality of systems, judging whether control instructions among the systems can be operated in parallel;
if yes, performing corresponding intelligent operation on the cabin systems based on the control instruction;
otherwise, determining the execution sequence of the cabin systems, sending an operation completion signal after the previous cabin system completes the corresponding intelligent operation, and executing the corresponding intelligent operation after the next cabin system receives the operation completion signal.
The beneficial effects of above-mentioned design scheme are: the optimal intelligent operation is determined according to the instruction operation type of the control instruction and the number of corresponding cabin systems, so that smooth execution of the intelligent operation is ensured, and safety precaution is realized.
Example 9:
based on embodiment 1, the embodiment of the invention provides an intelligent cabin intelligent safety precaution method based on driving safety, which further comprises the following steps:
And (3) carrying out timing monitoring on each cabin system in the intelligent cabin, acquiring monitoring data, comparing the monitoring data with standard state data, and carrying out corresponding early warning and reminding according to a comparison result.
The beneficial effects of above-mentioned design scheme are: by carrying out timing monitoring on each cabin system in the intelligent cabin, abnormality and fault of each cabin system are found in time, early warning reminding is carried out, a foundation is provided for smooth operation of the intelligent cabin, and safety precaution based on the intelligent cabin is realized.
Example 10:
an intelligent cabin intelligent safety precaution system based on driving safety, as shown in fig. 3, comprises:
the first data acquisition module is used for determining intelligent navigation and prediction functions of the driving through an artificial intelligence technology and determining driving environment data based on the intelligent navigation and prediction functions;
the second data acquisition module is used for acquiring acquisition data of a driver based on the camera and the sensor, and determining behavior data of the driver through the acquisition data;
The driving judgment module is used for carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data and judging whether the comprehensive identification data meets preset safe driving conditions or not;
and the instruction operation module is used for matching corresponding control instructions based on the safe driving judgment result and performing intelligent operation on the intelligent cabin based on the control instructions.
In this embodiment, the driving environment data includes data of the road condition of the vehicle, the driving speed of the driver, the direction, and the like.
In this embodiment, the collected data includes facial expressions, gestures, eye tracking, and the like.
In this embodiment, the intelligent operation includes, for example, a voice reminder, seat belt tightening, seat adjustment, and the like.
The beneficial effects of above-mentioned design scheme are: firstly, determining driving environment data and behavior data of a driver based on intelligent navigation and prediction functions, acquiring corresponding data from two systems of an intelligent cabin, then carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, realizing fusion and comprehensive analysis of multi-system data, providing accurate data basis for safe driving judgment, judging whether the comprehensive identification data meets preset safe driving conditions or not, matching corresponding control instructions based on safe driving judgment results, carrying out intelligent operation on the intelligent cabin based on the control instructions, realizing intelligent safety precaution on driving safety, and guaranteeing driving safety.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (4)
1. An intelligent cabin intelligent safety precaution method based on driving safety is characterized by comprising the following steps:
S1: determining intelligent navigation and prediction functions of the travelling crane through an artificial intelligent technology, and determining travelling crane environment data based on the intelligent navigation and prediction functions;
s2: acquiring acquisition data of a driver based on a camera and a sensor, and determining behavior data of the driver through the acquisition data;
S3: carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, and judging whether the comprehensive identification data meets preset safe driving conditions or not;
s4: based on the safe driving judgment result, matching the corresponding control instruction, and performing intelligent operation on the intelligent cabin based on the control instruction;
s1, determining intelligent navigation and prediction functions of a traveling crane through an artificial intelligence technology, and determining traveling crane environment data based on the intelligent navigation and prediction functions, wherein the method comprises the following steps:
acquiring a driving destination, analyzing road conditions and traffic jam by using an artificial intelligence technology to obtain intelligent navigation optimal for driving, and predicting the intelligent navigation by combining the habit of a driver to perform time and driving analysis so as to realize a prediction function;
matching the intelligent navigation and prediction functions, and obtaining driving environment data according to a matching result;
In S2, acquiring the collected data of the driver based on the camera and the sensor, and determining the behavior data of the driver through the collected data includes:
acquiring acquisition data from a camera and a sensor, and classifying the acquisition data according to data types to obtain type data;
Combining the type data into corresponding data sets according to time, and packaging all the data sets to obtain behavior data of a driver;
s3, carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data, wherein the method comprises the following steps:
uniformly preprocessing driving environment data and behavior data to obtain standard driving environment data and standard behavior data;
establishing a driving environment model based on standard driving environment data, acquiring first key data which does not meet preset driving environment data in the standard driving environment data, and marking the first key data at a position corresponding to the driving environment model to obtain a first marking result;
Simulating the driving gesture of the driver based on the behavior data to obtain a gesture simulation model, acquiring second data which does not meet preset behavior data in the standard behavior data, and marking the second data at a position corresponding to the gesture simulation model to obtain a second marking result;
Based on time, matching the driving environment model with the gesture simulation model, and taking the matched data at the same time as a data whole according to a matching result to obtain a plurality of data whole;
Adding data marks to the plurality of data integers based on the first marking result and the second marking result to obtain a plurality of data mark integers;
According to the preset comprehensive data content, carrying out data extraction on the plurality of data identifiers as a whole to obtain comprehensive identification data;
s3, judging whether the comprehensive identification data meets the preset safe driving condition or not, wherein the method comprises the following steps:
acquiring a common safe driving condition and a special safe driving condition from preset safe driving conditions;
acquiring special identification data with data identification and common identification data without data identification from the comprehensive identification data;
performing first safety judgment on the common identification data based on the common safety driving conditions, and acquiring local common identification data which does not meet the corresponding common safety sub-driving conditions according to a first judgment result;
performing second safety judgment on the special identification data based on the special safety driving conditions, and acquiring local special identification data which does not meet the special safety sub-driving conditions according to a second judgment result;
Based on the local common identification data and the local special identification data, a safe driving judgment result is obtained;
s4, based on the safe driving judgment result, matching the corresponding control instruction comprises the following steps:
acquiring an unsafe driving sequence from a safe driving result, and judging whether the unsafe driving sequence is consistent or not;
if yes, determining a driving adjustment scheme corresponding to the unsafe driving sequence;
otherwise, judging whether the unsafe driving sequence is a periodic sequence, if so, obtaining a corresponding driving adjustment scheme based on the unsafe driving sequence in one period;
When the unsafe driving sequence is determined to be the non-periodic sequence, determining an unsafe driving fluctuation coefficient curve of the unsafe driving sequence, and determining driving association among all unsafe driving in the unsafe driving sequence;
Acquiring all driving adjustment schemes corresponding to the unsafe driving sequences, selecting a to-be-selected driving adjustment scheme meeting driving association from all driving adjustment schemes, and selecting a driving adjustment scheme eliminating the unsafe driving fluctuation coefficient curve to the maximum extent from the to-be-selected driving adjustment scheme as a final driving adjustment scheme of the unsafe driving sequences;
calling a control instruction corresponding to the driving adjustment scheme from the instruction library;
s4, intelligent operation is performed on the intelligent cabin based on the control instruction, and the intelligent cabin comprises the following steps:
acquiring an instruction operation type of a control instruction, and determining a cabin system in an intelligent cabin corresponding to the control instruction based on the instruction operation type;
Acquiring a signal receiving interface corresponding to the cabin system, and transmitting a control instruction to the cabin system based on the signal receiving interface;
when the cabin system is determined to be one system, performing corresponding intelligent operation on the single cabin system based on the control instruction;
When the cabin system is determined to be a plurality of systems, judging whether control instructions among the systems can be operated in parallel;
if yes, performing corresponding intelligent operation on the cabin systems based on the control instruction;
otherwise, determining the execution sequence of the cabin systems, sending an operation completion signal after the previous cabin system completes the corresponding intelligent operation, and executing the corresponding intelligent operation after the next cabin system receives the operation completion signal.
2. The intelligent cabin intelligent safety precaution method based on driving safety according to claim 1, wherein the obtaining of the safe driving judgment result based on the local normal identification data and the local special identification data comprises:
integrating the local common identification data and the local special identification data according to a time sequence to obtain target identification data, and determining an unsafe driving sequence based on the target identification data;
And taking the unsafe driving sequence as a safe driving judgment result.
3. The intelligent cabin safety precaution method based on driving safety according to claim 1, further comprising:
And (3) carrying out timing monitoring on each cabin system in the intelligent cabin, acquiring monitoring data, comparing the monitoring data with standard state data, and carrying out corresponding early warning and reminding according to a comparison result.
4. Intelligent cabin intelligent safety precaution system based on driving safety, characterized by comprising:
the first data acquisition module is used for determining intelligent navigation and prediction functions of the driving through an artificial intelligence technology and determining driving environment data based on the intelligent navigation and prediction functions;
the second data acquisition module is used for acquiring acquisition data of a driver based on the camera and the sensor, and determining behavior data of the driver through the acquisition data;
The driving judgment module is used for carrying out data fusion on the driving environment data and the behavior data to obtain comprehensive identification data and judging whether the comprehensive identification data meets preset safe driving conditions or not;
The instruction operation module is used for matching corresponding control instructions based on the safe driving judgment result and performing intelligent operation on the intelligent cabin based on the control instructions;
determining intelligent navigation and prediction functions of the driving through artificial intelligence technology, and determining driving environment data based on the intelligent navigation and prediction functions, comprising:
acquiring a driving destination, analyzing road conditions and traffic jam by using an artificial intelligence technology to obtain intelligent navigation optimal for driving, and predicting the intelligent navigation by combining the habit of a driver to perform time and driving analysis so as to realize a prediction function;
matching the intelligent navigation and prediction functions, and obtaining driving environment data according to a matching result;
acquiring acquisition data of a driver based on a camera and a sensor, determining behavior data of the driver through the acquisition data, including:
acquiring acquisition data from a camera and a sensor, and classifying the acquisition data according to data types to obtain type data;
Combining the type data into corresponding data sets according to time, and packaging all the data sets to obtain behavior data of a driver;
data fusion is carried out on driving environment data and behavior data to obtain comprehensive identification data, and the method comprises the following steps:
uniformly preprocessing driving environment data and behavior data to obtain standard driving environment data and standard behavior data;
establishing a driving environment model based on standard driving environment data, acquiring first key data which does not meet preset driving environment data in the standard driving environment data, and marking the first key data at a position corresponding to the driving environment model to obtain a first marking result;
Simulating the driving gesture of the driver based on the behavior data to obtain a gesture simulation model, acquiring second data which does not meet preset behavior data in the standard behavior data, and marking the second data at a position corresponding to the gesture simulation model to obtain a second marking result;
Based on time, matching the driving environment model with the gesture simulation model, and taking the matched data at the same time as a data whole according to a matching result to obtain a plurality of data whole;
Adding data marks to the plurality of data integers based on the first marking result and the second marking result to obtain a plurality of data mark integers;
According to the preset comprehensive data content, carrying out data extraction on the plurality of data identifiers as a whole to obtain comprehensive identification data;
judging whether the comprehensive identification data meets the preset safe driving condition or not, comprising:
acquiring a common safe driving condition and a special safe driving condition from preset safe driving conditions;
acquiring special identification data with data identification and common identification data without data identification from the comprehensive identification data;
performing first safety judgment on the common identification data based on the common safety driving conditions, and acquiring local common identification data which does not meet the corresponding common safety sub-driving conditions according to a first judgment result;
performing second safety judgment on the special identification data based on the special safety driving conditions, and acquiring local special identification data which does not meet the special safety sub-driving conditions according to a second judgment result;
Based on the local common identification data and the local special identification data, a safe driving judgment result is obtained;
Based on the safe driving judgment result, matching the corresponding control instruction comprises:
acquiring an unsafe driving sequence from a safe driving result, and judging whether the unsafe driving sequence is consistent or not;
if yes, determining a driving adjustment scheme corresponding to the unsafe driving sequence;
otherwise, judging whether the unsafe driving sequence is a periodic sequence, if so, obtaining a corresponding driving adjustment scheme based on the unsafe driving sequence in one period;
When the unsafe driving sequence is determined to be the non-periodic sequence, determining an unsafe driving fluctuation coefficient curve of the unsafe driving sequence, and determining driving association among all unsafe driving in the unsafe driving sequence;
Acquiring all driving adjustment schemes corresponding to the unsafe driving sequences, selecting a to-be-selected driving adjustment scheme meeting driving association from all driving adjustment schemes, and selecting a driving adjustment scheme eliminating the unsafe driving fluctuation coefficient curve to the maximum extent from the to-be-selected driving adjustment scheme as a final driving adjustment scheme of the unsafe driving sequences;
calling a control instruction corresponding to the driving adjustment scheme from the instruction library;
intelligent operation is carried out to intelligent cabin based on control command, includes:
acquiring an instruction operation type of a control instruction, and determining a cabin system in an intelligent cabin corresponding to the control instruction based on the instruction operation type;
Acquiring a signal receiving interface corresponding to the cabin system, and transmitting a control instruction to the cabin system based on the signal receiving interface;
when the cabin system is determined to be one system, performing corresponding intelligent operation on the single cabin system based on the control instruction;
When the cabin system is determined to be a plurality of systems, judging whether control instructions among the systems can be operated in parallel;
if yes, performing corresponding intelligent operation on the cabin systems based on the control instruction;
otherwise, determining the execution sequence of the cabin systems, sending an operation completion signal after the previous cabin system completes the corresponding intelligent operation, and executing the corresponding intelligent operation after the next cabin system receives the operation completion signal.
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