CN112406767B - Monitoring method and device for vehicle pit accident, server and storage medium - Google Patents
Monitoring method and device for vehicle pit accident, server and storage medium Download PDFInfo
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- CN112406767B CN112406767B CN202011255021.XA CN202011255021A CN112406767B CN 112406767 B CN112406767 B CN 112406767B CN 202011255021 A CN202011255021 A CN 202011255021A CN 112406767 B CN112406767 B CN 112406767B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
- B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
- B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
- B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
- B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
- B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
Abstract
The application discloses a monitoring method, a monitoring device, a server and a storage medium for a vehicle pit accident. The monitoring method comprises the following steps: acquiring a vehicle running signal; judging the parking posture of the vehicle according to the vehicle running signal to determine whether the parking posture is abnormal; acquiring a driver behavior signal and a vehicle fault signal under the condition that the parking attitude is abnormal; and judging whether the vehicle has a pit accident or not according to the parking posture, the driver behavior signal and the vehicle fault signal. According to the vehicle monitoring method, the vehicle monitoring device, the server and the storage medium, whether a pit accident occurs in the vehicle is judged according to the vehicle driving signal, the driver behavior signal and the vehicle fault signal, the vehicle pit accident can be monitored by using original equipment of the vehicle, data processing load of a vehicle end can be reduced, the accident can be actively identified after the vehicle pits, corresponding processing is carried out, vehicle after-sale service is improved, and user experience is improved.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a method, a device, a server, and a storage medium for monitoring a vehicle pit accident.
Background
Driving in bad weather or road conditions, the vehicle can take place the pit accident, and after the occurence of failure, rely on driver self often can't solve, and the initiative discovery and in time rescue after the accident can provide very big help for the driver. In the related technology, hardware for acquiring data aiming at pit accidents is additionally arranged on a vehicle body, data processing needs to be carried out on a vehicle end, and the data processing load of the vehicle end is increased.
Disclosure of Invention
In view of this, embodiments of the present application provide a monitoring method, a monitoring device, a server and a storage medium for a vehicle pit accident.
The application provides a monitoring method for a vehicle pit accident, which comprises the following steps:
acquiring a vehicle running signal;
judging the parking posture of the vehicle according to the vehicle running signal to determine whether the parking posture is abnormal or not;
under the condition that the parking posture is abnormal, acquiring a driver behavior signal and a vehicle fault signal;
and judging whether the vehicle has the pit accident or not according to the parking posture, the driver behavior signal and the vehicle fault signal.
In some embodiments, the acquiring the vehicle travel signal comprises:
the method comprises the steps of obtaining a vehicle running signal of each unit time in a first time period from a first preset time before the current time to the current time.
In some embodiments, said determining a parking posture of said vehicle from said vehicle travel signal to determine whether said parking posture is abnormal comprises;
and identifying the vehicle driving signal according to a pre-stored parking posture model to judge the parking posture of the vehicle so as to determine whether the parking posture is abnormal.
In some embodiments, the vehicle driving signal includes a real-time three-axis acceleration signal of the vehicle and speed signals of respective wheels of the vehicle, and the recognizing the vehicle driving signal according to a pre-stored parking posture model to determine a parking posture of the vehicle to determine whether the parking posture is abnormal includes:
judging whether the vehicle is in a parking state or not according to the speed signal;
determining an attitude angle of the vehicle per unit time within the first time period according to the three-axis acceleration signal when the vehicle is in the parking state;
and processing the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
In some embodiments, said obtaining a driver behavior signal and a vehicle fault signal in case of said abnormal parking posture comprises;
under the condition that the parking gesture is abnormal, acquiring a driver behavior signal of each unit time in a second time period from a second preset time before the current time to a third preset time after the current time;
and acquiring a vehicle fault signal of each unit time in the second time period.
In some embodiments, the determining whether the vehicle has the pit accident according to the parking posture, the driver behavior signal, and the vehicle fault signal includes:
and identifying the driver behavior signal according to a pre-stored driver abnormal behavior model to judge the abnormal level of the driver behavior.
In some embodiments, the driver behavior signal includes a vehicle locking signal, a double flashing light signal, a gear position signal, an accelerator pedal signal, a main driving door signal and a steering wheel angle signal, and the identifying the driver behavior signal according to a pre-stored driver abnormal behavior model to determine the driver behavior abnormal level includes:
judging whether a first abnormal behavior of unlocking within a preset time after getting off exists or not according to the main driving door signal and the vehicle locking signal;
judging whether a second abnormal behavior of starting the double flashing lamps exists or not according to the gear signal and the double flashing lamp signal;
judging whether a third abnormal behavior of pressing an accelerator pedal hard exists or not according to the accelerator pedal signal;
judging whether a fourth abnormal behavior of repeatedly operating the steering wheel exists or not according to the steering wheel turning angle signal;
judging whether a fifth abnormal behavior that the steering wheel is not returned to the right after the driver leaves the vehicle exists or not according to the main driving door signal and the steering wheel corner signal;
and judging the abnormal level of the driver behavior according to a preset rating rule, the first abnormal behavior, the second abnormal behavior, the third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior.
In some embodiments, the determining whether the vehicle has the pit accident according to the parking posture, the driver behavior signal, and the vehicle fault signal includes:
and identifying the parking posture, the abnormal level of the driver behavior and the vehicle fault signal according to a pre-stored vehicle pit model to judge whether the vehicle has the pit accident.
In some embodiments, the vehicle fault signals include a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal and an electrical system fault signal, and the identifying the parking posture, the driver behavior abnormality level and the vehicle fault signal according to a pre-stored vehicle pit model to determine whether the pit accident occurs to the vehicle includes:
processing a feature vector formed by the parking posture, the abnormal level of the driver behavior and the vehicle fault signal according to the pre-stored vehicle pit trapping model to obtain the probability of the vehicle in the pit trapping accident;
determining that the vehicle has the crater accident when the probability is greater than a predetermined threshold.
In some embodiments, the monitoring method further comprises:
in the case that it is determined that the vehicle has the pit accident, an alarm signal is transmitted to a service provider of the vehicle so that the service provider can perform rescue according to the alarm signal.
The application provides a monitoring device of vehicle, monitoring device includes:
the acquisition module is used for acquiring a vehicle running signal;
the judging module is used for judging the parking gesture of the vehicle according to the vehicle running signal so as to determine whether the parking gesture is abnormal or not;
the acquisition module is also used for acquiring a driver behavior signal and a vehicle fault signal under the condition that the parking posture is abnormal;
the judging module is further used for judging whether the vehicle has the pit accident or not according to the parking gesture, the driver behavior signal and the vehicle fault signal.
The application provides a server, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used for realizing the monitoring method of the vehicle pit accident of any one of the above embodiments when executing the computer program.
The present application provides one or more non-transitory computer-readable storage media storing a computer program that, when executed by one or more processors, implements the method of monitoring a vehicle pit accident of any of the above embodiments.
According to the monitoring method, the monitoring device, the server and the storage medium for the vehicle pit accident, whether the vehicle pit accident occurs or not is judged according to the vehicle running signal, the driver behavior signal and the vehicle fault signal, the vehicle pit accident can be monitored by using original equipment of the vehicle, the data processing load of a vehicle end can be reduced, the accident can be actively identified after the vehicle pit accident occurs, corresponding processing is carried out, vehicle after-sale service is improved, and user experience is improved.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a method for monitoring a vehicle pit accident according to some embodiments of the present disclosure.
FIG. 2 is a block schematic diagram of a monitoring device of a vehicle according to certain embodiments of the present application.
Fig. 3 is a schematic flow chart of a vehicle monitoring apparatus according to some embodiments of the present disclosure.
FIG. 4 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 5 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 6 is a schematic diagram of three-axis coordinates of certain embodiments of the present application.
FIG. 7 is a flow chart illustrating a method for monitoring a vehicle according to some embodiments of the present disclosure.
FIG. 8 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 9 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 10 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 11 is a model schematic of a method of monitoring a vehicle according to some embodiments of the present application.
FIG. 12 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
FIG. 13 is a schematic flow chart diagram of a method for monitoring a vehicle in accordance with certain embodiments of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
Referring to fig. 1, the present application provides a method for monitoring a vehicle pit accident, including:
s11: acquiring a vehicle running signal;
s12: judging the parking posture of the vehicle according to the vehicle running signal to determine whether the parking posture is abnormal;
s13: acquiring a driver behavior signal and a vehicle fault signal under the condition that the parking attitude is abnormal;
s14: and judging whether the vehicle has a pit accident or not according to the parking posture, the driver behavior signal and the vehicle fault signal.
The embodiment of the application provides a server 100. The server 100 includes a processor 104. The processor 104 is configured to obtain the vehicle driving signal, determine a parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, obtain the driver behavior signal and the vehicle failure signal in case of abnormal parking posture, and determine whether the vehicle has a pit accident according to the parking posture, the driver behavior signal and the vehicle failure signal. The processor 104 may be a processor 104 independently configured to implement a method for monitoring a vehicle pit accident, or may be the processor 104 of the server 100 itself, which is not limited herein.
Referring to fig. 2, an embodiment of the present application further provides a vehicle monitoring device 110, and the vehicle pit accident monitoring method according to the embodiment of the present application can be implemented by the vehicle monitoring device 110. The monitoring device 110 of the vehicle includes an acquisition module 112 and a determination module 114. S11 and S13 may be implemented by the obtaining module 112, and S12 and S14 may be implemented by the determining module 114. In other words, the obtaining module 112 is used for obtaining the vehicle running signal, and for obtaining the driver behavior signal and the vehicle fault signal in case of abnormal parking posture. The judging module 114 is configured to judge a parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, and is configured to judge whether a pit accident occurs in the vehicle according to the parking posture, the driver behavior signal, and the vehicle failure signal.
Particularly, when the vehicle is driven in severe weather or the road conditions of the vehicle are poor, the vehicle may have a pit accident, and the wheels fall into snow, mud pits, ditches or well covers, so that the vehicle cannot continue to drive. After a pit accident happens, the vehicle is usually taken out of the predicament by help of foreign objects, and the problem is often difficult to solve only by the driver.
In the related art, a camera system is used for recognizing road conditions, a photosensitive system is used for detecting road conditions, or a radar system is used for distance measurement, so that the vehicle is prevented from being trapped in a pit. According to the scheme, hardware for acquiring data aiming at pit accidents, such as a camera system and a radar system, needs to be additionally added on the vehicle body, and the production cost of the vehicle is increased. And the data that above-mentioned scheme gathered need be handled at the car end, has also increased the data processing load of car end, can drag the response speed of vehicle processor to a certain extent, and user experience is relatively poor.
Compared with the scheme of using a camera system, a photosensitive system and a radar system to realize pit-trapping early warning of a vehicle, in the monitoring method for a pit-trapping accident of a vehicle, the server 100 and the monitoring device 110 of the vehicle according to the embodiment of the present application, the parking posture of the vehicle is determined by obtaining a vehicle driving signal, so as to determine whether the parking posture of the vehicle is abnormal, and a driver behavior signal and a vehicle fault signal are obtained under the condition that the parking posture of the vehicle is abnormal. And then comprehensively judging whether the vehicle has a pit accident or not according to the vehicle running signal, the driver behavior signal and the vehicle fault signal. So, only need use the original equipment of vehicle, can realize the control to the vehicle pit accident, data size, less to the calculated amount of data, can alleviate the data processing load of car end, and can initiatively discern the emergence of accident behind the vehicle pit to corresponding processing has improved vehicle after-sales service, promotes user experience.
Referring to fig. 3, in some embodiments, S11 includes:
s111: the method comprises the steps of obtaining a vehicle running signal of each unit time in a first time period from a first preset time before the current time to the current time.
In some embodiments, S111 may be implemented by the obtaining module 112. In other words, the obtaining module 112 is configured to obtain the vehicle driving signal of each unit time in a first time period from a first predetermined time before the current time to the current time.
In some embodiments, the processor 104 is configured to obtain the vehicle travel signal per unit time for a first time period from a first predetermined time before the current time to the current time.
Specifically, during the running of the vehicle, a vehicle running signal of each unit time in a first time period is acquired, and the parking posture of the vehicle is judged according to the vehicle running signal, so that whether the parking posture of the vehicle is abnormal or not is determined. The first time period may be a time range from a predetermined time t11 before the current time t to the current time t, or may be a time range from a predetermined time t11 before the current time t to a predetermined time t12 after the current time t.
In some embodiments, the first time period may be a time range from a predetermined time t11 before the current time t to the current time t. And acquiring a vehicle running signal of each unit time in the time range of t11-t during the running of the vehicle, analyzing and processing the vehicle running signal of each unit time, and judging the parking posture of the vehicle so as to determine whether the parking posture of the vehicle is abnormal.
Therefore, the parking posture of the vehicle at the current time can be compared and analyzed with the parking posture of the vehicle before the current time, the parking posture of the vehicle at the current time can be accurately judged, and the accuracy of analyzing the pit accident result is ensured.
In other embodiments, the first time period may be a time range from a predetermined time t11 before the current time t to a predetermined time t12 after the current time t. And acquiring a vehicle running signal of each unit time in the time range of t11-t12 during the running of the vehicle, analyzing and processing the vehicle running signal of each unit time, and judging the parking posture of the vehicle so as to determine whether the parking posture of the vehicle is abnormal.
Therefore, the parking posture of the vehicle at the current time, the parking posture of the vehicle before the current time and the parking posture of the vehicle after the current time can be compared and analyzed, the parking posture of the vehicle at the current time can be accurately judged, and the accuracy of the result of analyzing the pit trapping accident is ensured.
The length of the time range of the first time period may be set according to the road condition, the service life of the vehicle, the vehicle maintenance record, the vehicle performance, and other factors, and is not limited specifically, and may be, for example, 3 seconds, 5 seconds, 10 seconds, 13 seconds, 17 seconds, and the like. Correspondingly, the predetermined time t11 before the current time t and the predetermined time t12 after the current time t may be set according to road conditions, vehicle service life, vehicle maintenance records, vehicle performance, and other factors, and are not limited specifically, for example, 1 second, 3 seconds, 5 seconds, 8 seconds, 10 seconds, and the like, and the specific values of t11 and t12 may be equal or unequal.
Referring to fig. 4, in some embodiments, S12 includes:
s121: and recognizing the vehicle driving signal according to a pre-stored parking posture model to judge the parking posture of the vehicle so as to determine whether the parking posture is abnormal.
In some embodiments, S121 may be implemented by the determining module 114. In other words, the determining module 114 is configured to recognize the vehicle driving signal according to a pre-stored parking gesture model to determine the parking gesture of the vehicle so as to determine whether the parking gesture is abnormal.
In some embodiments, the processor 104 is configured to recognize the vehicle driving signal according to a pre-stored parking gesture model to determine a parking gesture of the vehicle to determine whether the parking gesture is abnormal.
Specifically, the vehicle driving signal may be recognized according to a pre-stored parking posture model, and the parking posture of the vehicle may be determined, thereby determining whether the parking posture is abnormal. The specific processing method of the parking attitude model may be selected according to factors such as a data type and a data size, and is not particularly limited, and may be, for example, a Gradient Boosting Decision Tree (GBDT) algorithm, a Support Vector Machine (SVM) algorithm, a regression algorithm, and the like.
The identification process of the vehicle driving signal is developed and stored in the form of a model, so that the development efficiency of the monitoring apparatus 110 can be improved. And faults occur in the subsequent use process, and the checking and the modification are convenient. After model development is completed, based on the characteristic of high model reuse rate, the parking attitude model can be suitable for various vehicle types and/or systems, and development cost is reduced.
Referring to fig. 5, in some embodiments, the vehicle driving signal includes a real-time three-axis acceleration signal of the vehicle and speed signals of each wheel of the vehicle, and S121 includes:
s1211: judging whether the vehicle is in a parking state or not according to the speed signal;
s1212: when the vehicle is in a parking state, determining the attitude angle of the vehicle in each unit time within a first time period according to the triaxial acceleration signal;
s1213: and processing the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
In some embodiments, S1211-S1213 may be implemented by the determination module 114. In other words, the determining module 114 is configured to determine whether the vehicle is in a parking state according to the speed signal, determine an attitude angle of the vehicle per unit time in a first time period according to the three-axis acceleration signal when the vehicle is in the parking state, and process the attitude angle according to the parking attitude model to obtain a parking attitude of the vehicle.
In some embodiments, the processor 104 is configured to determine whether the vehicle is in a parking state according to the speed signal, determine a posture angle of the vehicle per unit time in a first time period according to the three-axis acceleration signal when the vehicle is in the parking state, and process the posture angle according to the parking posture model to obtain a parking posture of the vehicle.
Specifically, it is possible to determine whether the vehicle is in a stopped state based on the speed signals of the respective wheels of the vehicle. The speed signal can be a speed signal for collecting the driving wheel, and can also be a speed signal for collecting all normal tires.
In some embodiments, where the vehicle is a front-drive vehicle, the speed signals for the front left and front right wheels may be collected, as well as the speed signals for all wheels normally in use. In some embodiments, the vehicle is a rear drive vehicle, and the speed signals for the rear left wheel and the front right wheel may be collected, or the speed signals for all wheels normally used may be collected. In other embodiments, where the vehicle is a four-wheel drive vehicle, the speed signals of all normally used wheels, including the driven wheels and the drive wheels, may be collected.
Therefore, by identifying the speed signal of each vehicle, whether the vehicle is in a parking state or not can be accurately reflected, and the accuracy of the signal is ensured.
In addition, it is also possible to determine whether or not the vehicle is in a parked state based on a Global Positioning System (GPS). When the GPS indicates that the vehicle position has moved, the vehicle may be considered to be not in a stopped state. When the GPS indicates that the vehicle position is stopped, the vehicle may be considered to be in a stopped state.
When the vehicle is judged to be in the parking state, acquiring a triaxial acceleration signal of the vehicle in each unit time within a first time period, determining an attitude angle of the vehicle according to the triaxial acceleration signal, and processing the attitude angle by using a parking attitude model to obtain the parking state of the vehicle. Referring to fig. 6, specifically, the three-axis acceleration signals include a pitch angle signal of an x-axis, a yaw angle signal of a y-axis, and a roll angle signal of a z-axis. The pitch angle pitch may represent an included angle between an x axis of a vehicle coordinate system and a horizontal plane, the yaw angle yaw may represent an included angle between a y axis of the vehicle coordinate system and the horizontal plane, and the roll angle roll may represent an included angle between a z axis of the vehicle coordinate system and the horizontal plane.
In some embodiments, the yaw angle yaw and roll angle roll may be measured using a LIS3DH triaxial accelerometer. The yaw angle yaw and the roll angle roll per unit time of the vehicle in the first time period can be obtained from the pitch (arctan (-y, z) 180/3.14159) and the roll (x, z) 180/3.14159).
When the vehicle is judged to be in a parking state, acquiring the attitude angle of the vehicle in each unit time in a first time period, inputting each acquired attitude angle into a parking attitude model, processing the attitude angles by the parking attitude model, and outputting the probability of whether the vehicle is inclined forwards, backwards, leftwards or rightwards or whether the vehicle is in the parking state without abnormality.
For example, after the parking posture model processes the input pitch angle, the parking state of the vehicle is obtained as follows: the probability of the vehicle leaning forward is 0.2, the probability of the vehicle leaning backward is 0.9, and the probability of no abnormality is 0.1. It may be determined that the vehicle is leaning backwards at the current time according to the maximum value of the above results.
For another example, after the parking posture model processes the input roll angle, the parking state of the vehicle is obtained as follows: the probability of the vehicle leaning to the left is 0.7, the probability of the vehicle leaning to the right is 0.4, and the probability of no abnormality is 0.1. It can be determined that the vehicle has left-leaning at the current time according to the maximum value of the above results.
Therefore, the attitude angle of the vehicle in each unit time in the first time period can be compared and analyzed, and the parking attitude of the vehicle at the current time can be accurately judged.
Referring to fig. 7, in some embodiments, S13 includes:
s131: under the condition that the parking posture is abnormal, acquiring a driver behavior signal of each unit time in a second time period from a second preset time before the current time to a third preset time after the current time;
s132: a vehicle fault signal per unit time within the second time period is acquired.
In some embodiments, S131 and S132 may be implemented by the obtaining module 112. In other words, the obtaining module 112 is configured to obtain the driver behavior signal per unit time in a second time period from a second predetermined time before the current time to a third predetermined time after the current time, and is configured to obtain the vehicle failure signal per unit time in the second time period, in case of abnormal parking posture.
In some embodiments, the processor 104 is configured to obtain the driver behavior signal per unit time in a second time period from a second predetermined time before the current time to a third predetermined time after the current time, and to obtain the vehicle failure signal per unit time in the second time period, in case of the abnormal parking posture.
Specifically, under the condition that the parking posture is judged to be abnormal, the driver behavior signal and the vehicle fault signal of each unit time in the second time period are obtained, and whether the vehicle is in a pit accident or not is judged according to the parking posture, the driver behavior signal and the vehicle fault signal. The second time period may be a time range from a predetermined time t21 before the current time t to the current time t, or a time range from a second predetermined time t21 before the current time t to a predetermined time t22 after the current time t.
In some embodiments, the second time period may be a time range from a predetermined time t21 before the current time t to the current time t. And under the condition that the parking posture is judged to be abnormal, acquiring the driver behavior signal and the vehicle fault signal of each unit time within the time range of t21-t, and analyzing and processing the driver behavior signal and the vehicle fault signal of each unit time so as to judge whether the vehicle has a pit accident.
Therefore, the driver behavior at the current time can be compared and analyzed with the driver behavior before the current time, whether the driver behavior at the current time is abnormal or not can be accurately judged, the vehicle fault signal at the current time and the vehicle fault signal before the current time can be compared and analyzed, whether the vehicle at the current time breaks down or not can be accurately judged, and the accuracy of analyzing the pit-trapping accident result is ensured.
In other embodiments, the second time period may be a time range from a second predetermined time t21 before the current time t to a predetermined time t22 after the current time t. And under the condition that the parking posture is judged to be abnormal, acquiring the driver behavior signal and the vehicle fault signal of each unit time in the time range of t21-t22, and analyzing and processing the driver behavior signal and the vehicle fault signal of each unit time so as to judge whether the vehicle has a pit accident.
Therefore, the driver behaviors before the current time, the current time and the current time can be compared and analyzed, whether the driver behavior at the current time is abnormal or not can be accurately judged, the vehicle fault signals before the current time, at the current time and after the current time can be compared and analyzed, whether the vehicle has a fault or not at the current time can be accurately judged, and the accuracy of analyzing the pit trapping accident result can be ensured.
The length of the time range of the second time period may be set according to the road condition, the service life of the vehicle, the vehicle maintenance record, the vehicle performance, and other factors, and the time range is not limited specifically, and may be, for example, 10 seconds, 30 seconds, 50 seconds, 60 seconds, 100 seconds, and the like. The second predetermined time t21 before the current time t and the third predetermined time t22 after the current time t may be set according to road conditions, service life of the vehicle, vehicle maintenance records, vehicle performance, and other factors, and are not specifically limited, for example, 5 seconds, 10 seconds, 30 seconds, 50 seconds, 80 seconds, and the like may be used, and the specific values of t21 and t22 may be equal or unequal.
Referring to fig. 8, in some embodiments, S14 includes:
s141: and identifying the driver behavior signal according to a pre-stored driver abnormal behavior model to judge the abnormal level of the driver behavior.
In some embodiments, S141 may be implemented by the determining module 114. In other words, the determining module 114 is configured to identify the driver behavior signal according to a pre-stored abnormal driver behavior model to determine the abnormal driver behavior level.
In some embodiments, the processor 104 is configured to identify the driver behavior signal according to a pre-stored abnormal driver behavior model to determine an abnormal level of driver behavior.
Specifically, the driver behavior signal can be recognized according to a pre-stored driver abnormal behavior model, whether the driver behavior is abnormal or not is judged, and the abnormal level of the driver behavior is evaluated, so that whether the vehicle has a pit accident or not is determined. The specific processing method of the driver abnormal behavior model may be selected according to factors such as data type and data size, and is not limited specifically, for example, the driver abnormal behavior model may be a counting model, may be a GBDT algorithm, may be an SVM algorithm, and may be a regression algorithm.
The identification process of the driver behavior signal is developed and stored in the form of a model, so that the development efficiency of the monitoring apparatus 110 can be improved. And faults occur in the subsequent use process, and the checking and the modification are convenient. After model development is completed, based on the characteristic of high model reuse rate, the abnormal behavior model of the driver can be suitable for various vehicle types and/or systems, and development cost is reduced.
Referring to fig. 9, in some embodiments, the driver behavior signals include a vehicle lock signal, a twin flashing light signal, a gear signal, an accelerator pedal signal, a main driving door signal, and a steering wheel angle signal, and S141 includes:
s1411: judging whether a first abnormal behavior that the vehicle is not locked within a preset time after getting off exists or not according to the main driving door signal and the vehicle locking signal;
s1412: judging whether a second abnormal behavior of starting the double flashing lamps exists according to the gear signal and the double flashing lamp signal;
s1413: judging whether a third abnormal behavior of pressing an accelerator pedal hard exists or not according to the accelerator pedal signal;
s1414: judging whether a fourth abnormal behavior of repeatedly operating the steering wheel exists or not according to the steering wheel turning angle signal;
s1415: judging whether a fifth abnormal behavior that the steering wheel is not returned to the right after the driver leaves the vehicle exists or not according to the main driving door signal and the steering wheel corner signal;
s1416: and judging the abnormal level of the driver behavior according to a preset rating rule, a first abnormal behavior, a second abnormal behavior, a third abnormal behavior, a fourth abnormal behavior and a fifth abnormal behavior.
In some embodiments, S1411-S1416 may be implemented by the determination module 114. Or, the determining module 114 is configured to determine whether there is a first abnormal behavior of unlocking the vehicle within a predetermined time period after getting off the vehicle according to the main driving door signal and the vehicle locking signal, determine whether there is a second abnormal behavior of turning on the dual flashing light according to the gear signal and the dual flashing light signal, determine whether there is a third abnormal behavior of hard stepping on the accelerator pedal according to the accelerator pedal signal, determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel according to the steering wheel angle signal, determine whether there is a fifth abnormal behavior of not returning the steering wheel after getting off the vehicle according to the main driving door signal and the steering wheel angle signal, and determine the driver behavior abnormal level according to the preset rating rule, the first abnormal behavior, the second abnormal behavior, the third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior.
In some embodiments, the processor 104 is configured to determine whether there is a first abnormal behavior of unlocking within a predetermined time period after getting off the vehicle, based on the main driving door signal and the vehicle locking signal, and to determine whether there is a second abnormal behavior of turning on the blinker, based on the shift signal and the blinker signal, and to determine whether there is a third abnormal behavior of slamming on the accelerator pedal, based on the accelerator pedal signal, and to determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel, based on the steering wheel angle signal, and to determine whether there is a fifth abnormal behavior of not returning the steering wheel after getting off the vehicle, based on the main driving door signal and the steering wheel angle signal, and the driver behavior abnormity level is judged according to the preset rating rule, the first abnormal behavior, the second abnormal behavior, the third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior.
Specifically, whether the first abnormal behavior X1 that the driver does not lock the automobile within the preset time after getting off the automobile exists or not is judged according to the main driving door signal and the automobile locking signal. When the vehicle is locked, the vehicle locking signal is 0, that is, the vehicle is not locked, within a predetermined time after the main driving door is opened or closed, and at this time, it may be determined that the first abnormal behavior X1 exists in the driver within the second time period.
In the case where the first abnormal behavior X1 exists, the driver behavior abnormality level is set to 1, and in the case where the first abnormal behavior X1 does not exist, the driver behavior abnormality level is set to 0.
And in the second time period, under the condition that the driver has the first abnormal behavior X1, judging whether the driver has the second abnormal behavior X2 of turning on the double flashing light in the second time period according to the gear signal and the double flashing light signal. The current gear of the vehicle is a parking gear, the double flashing light signal is 1, namely the double flashing light is in a light-emitting state, and at the moment, it can be judged that the second abnormal behavior X2 exists in the driver in the second time period.
In the case where there are the first abnormal behavior X1 and the second abnormal behavior X2, the driver's behavior abnormality level is 2. In the case where the first abnormal behavior X1 exists but the second abnormal behavior X2 does not exist, the driver's behavior abnormality level is still 1.
In the second time zone, when the driver is judged to have the first abnormal behavior X1 in the second time zone, whether the driver has the third abnormal behavior X3 of pressing the accelerator pedal suddenly, the fourth abnormal behavior X4 of repeatedly operating the steering wheel or the fifth abnormal behavior X5 of not returning the steering wheel after getting off is judged according to the accelerator pedal signal, the main driving door signal and the steering wheel rotation angle signal. Wherein, the number a of times that the depth of the stepping on the accelerator pedal exceeds the preset depth threshold value in the second time period is calculated, and when the value a is greater than or equal to the first preset number threshold value, the third abnormal behavior X3 of the driver is judged. And b, calculating the number of times that the difference value between the steering wheel angle at the current time and the steering wheel angle at the preset time before the current time is greater than a preset difference threshold value, and judging that the driver has a fourth abnormal behavior X4 when the value of b is greater than or equal to a second preset number threshold value. And in a second time period after the main driving door is opened, if the degree of the steering wheel angle exceeds a preset angle threshold value, judging that the driver has a fifth abnormal behavior X5.
In the case where the first abnormal behavior X1 is present, when the driver has two or more of the third abnormal behavior X3, the fourth abnormal behavior X4, and/or the fifth abnormal behavior X5, the driver behavior abnormality level is 2. In the case where the first abnormal behavior X1 exists, but the third abnormal behavior X3, the fourth abnormal behavior X4, and the fifth abnormal behavior X5 do not exist, or the kind of the third abnormal behavior X3, the fourth abnormal behavior X4, or the fifth abnormal behavior X5 existing is less than two, the driver's behavior abnormality level is still 1.
Further, in the case where the driver has the first abnormal behavior X1 and the second abnormal behavior X2 within the second period of time, there are two or more abnormal behaviors out of the third abnormal behavior X3, the fourth abnormal behavior X4, and/or the fifth abnormal behavior X5, and the driver behavior abnormality level is 3.
For example, in the second time period, the driver has abnormal behaviors of X3, X4, and X5, and the driver behavior abnormality level is 0. If X1 exists, the driver behavior anomaly level is 1. There are X1 and X2, then the driver behavior anomaly level is 2. There are X1, X4, and X5, the driver behavior anomaly level is 2. There are X1, X2, and X5, the driver behavior anomaly level is 2. There are X1, X3, X4, and X5, the driver behavior anomaly level is 2. There are X1, X2, X3, X4, and X5, the driver behavior anomaly level is 3. And so on.
Therefore, the behavior of the driver in the second time period can be compared and analyzed, the abnormal level of the behavior of the driver can be accurately judged, and the accuracy of the result of the pit accident analysis is ensured.
The predetermined depth threshold, the first predetermined number of times threshold, the predetermined difference threshold, the second predetermined number of times threshold, the predetermined angle threshold, and other thresholds may be set according to parameters such as vehicle type and behavior habit of driver, and are not limited specifically, for example, the predetermined depth threshold may be 10, 15, 18, and the like, the first predetermined number of times threshold and the second predetermined number of times threshold may be 1, 2, 5, and the like, the predetermined difference threshold may be 50, 80, 100, and the like, and the predetermined angle threshold may be 250 degrees, 300 degrees, 350 degrees, 400 degrees, and the like.
Referring to fig. 10, in some embodiments, S14 includes:
s142: and identifying the parking posture, the abnormal level of the driver behavior and the vehicle fault signal according to a pre-stored vehicle pit model to judge whether the vehicle has a pit accident.
In some embodiments, S142 may be implemented by the determination module 114. In other words, the determining module 114 is configured to identify the parking posture, the abnormal level of the driver behavior, and the vehicle fault signal according to a pre-stored vehicle pit model to determine whether a pit accident occurs on the vehicle.
In some embodiments, the processor 104 is configured to identify the parking posture, the abnormal level of the driver behavior, and the vehicle fault signal according to a pre-stored vehicle pit model to determine whether the vehicle has a pit accident.
Specifically, referring to fig. 11, the parking posture, the abnormal level of the driver behavior, and the vehicle fault signal may be identified according to a pre-stored vehicle pit model, so as to determine whether a pit accident occurs in the vehicle. The specific processing method of the vehicle pit model may be selected according to factors such as data type and data size, and is not limited specifically, for example, the processing method may be a counting model, may be a GBDT algorithm, may be an SVM algorithm, and may be a regression algorithm.
The recognition process of the parking posture, the driver's behavior abnormality level, and the vehicle failure signal is developed and stored in the form of a model, so that the development efficiency of the monitoring apparatus 110 can be improved. And faults occur in the subsequent use process, and the checking and the modification are convenient. After model development is completed, the vehicle pit model can be suitable for various vehicle types and/or systems based on the characteristic of high model reuse rate, and development cost is reduced.
Referring to fig. 12, in some embodiments, the vehicle fault signals include a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal, and an electrical system fault signal, and S142 includes:
s1421: processing a characteristic vector consisting of a parking posture, a driver behavior abnormal grade and a vehicle fault signal according to a pre-stored vehicle pit model to obtain the probability of the vehicle in a pit accident;
s1422: and determining that the vehicle has a pit accident when the probability is greater than a preset threshold value.
In some embodiments, S1421 and S1422 may be implemented by the determination module 114. In other words, the determining module 114 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver behavior, and the vehicle fault signal according to a pre-stored vehicle pit model to obtain the probability of the vehicle occurring a pit accident, and is configured to determine that the vehicle occurring a pit accident when the probability is greater than a predetermined threshold.
In some embodiments, the processor 104 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver behavior, and the vehicle fault signal according to a pre-stored vehicle pit model to obtain a probability of the vehicle occurring a pit accident, and to determine that the vehicle occurring a pit accident when the probability is greater than a predetermined threshold.
Specifically, the vehicle fault signals include a wheel slip signal, a chassis fault signal, a tire pressure monitoring system fault signal, and an electrical system fault signal. The wheel slip signal can be detected by calculating the speed difference between the front wheel and the rear wheel, and when the difference value between the front wheel and the rear wheel is larger than a preset speed difference value threshold value, the wheel slip phenomenon can be considered to occur. The chassis fault signal and the tire pressure monitoring system fault signal can be detected by vehicle systems such as an anti-lock brake system, an electronic stability program, an automatic parking system and/or a tire pressure monitoring system of the vehicle, and when any one or more of the chassis fault signal and the tire pressure monitoring system fault signal is detected to be greater than 0, the chassis and/or the wheels of the vehicle can be considered to be in fault. The electric system fault signal can be detected through vehicle systems such as a motor system, a battery management system, a car lamp control system and a radar system, and when any one or more fault signals in the electric system are detected to be larger than 0, the electric system of the vehicle can be considered to be in fault.
And (3) synthesizing the parking attitude, the abnormal level of the driver behavior and the vehicle fault signal to form a characteristic vector, and processing the characteristic vector to obtain the probability of the pit accident of the vehicle. And when the probability is greater than a preset threshold value, judging that the vehicle has a pit accident.
Therefore, the parking attitude, the abnormal level of the driver behavior and the vehicle fault signal are compared and analyzed, and whether the vehicle is in a pit or not can be accurately judged.
The predetermined speed difference threshold, the predetermined threshold for determining the probability of vehicle getting stuck, and the like may be set according to parameters such as a vehicle type, a behavior habit of a driver, a vehicle maintenance record, a service life of the vehicle, and the like, and are not limited specifically, for example, the predetermined speed difference threshold may be 1 meter per second, 2 meters per second, 3 meters per second, 5 meters per second, and the like, and the predetermined threshold for determining the probability of vehicle getting stuck may be 0.5, 0.7, 0.8, and the like.
Referring to fig. 13, in some embodiments, the monitoring method further includes:
s15: and in case that the vehicle is determined to have the pit accident, sending an alarm signal to a service provider of the vehicle so that the service provider can carry out rescue according to the alarm signal.
In some embodiments, S15 may be implemented by the determination module 114. In other words, the determining module 114 is used for sending an alarm signal to a service provider of the vehicle so that the service provider can perform rescue according to the alarm signal in case that the vehicle is determined to have a pit accident.
In some embodiments, the processor 104 is configured to send an alert signal to a service provider of the vehicle to enable the service provider to perform a rescue based on the alert signal in the event that the vehicle is determined to have a pothole accident.
Specifically, under the condition that the vehicle is determined to have the pit accident, the alarm signal is sent to a service provider of the vehicle, the service provider can carry out rescue according to the alarm signal or contact with a driver, and after the vehicle is determined to have the pit accident, the rescue is carried out according to the condition.
Therefore, after the vehicle is subjected to pit accident, corresponding rescue help is provided, after-sale service of the vehicle can be improved, and user experience is improved.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media storing a computer program that, when executed by one or more processors, implements the method of monitoring a vehicle of any of the embodiments described above.
The embodiment of the application also provides a vehicle. The vehicle includes a memory and one or more processors, one or more programs being stored in the memory and configured to be executed by the one or more processors. The program includes instructions for executing the method for monitoring a vehicle pit accident of any one of the above embodiments.
The processor may be used to provide computational and control capabilities to support the operation of the entire vehicle. The memory of the vehicle provides an environment for the computer readable instructions in the memory to operate.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in one or more non-volatile computer-readable storage media, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), or the like.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A method of monitoring for a vehicle pit accident, the method comprising:
acquiring a vehicle running signal of each unit time in a first time period from first preset time before current time to current time;
identifying real-time three-axis acceleration signals in the vehicle driving signals and speed signals of each wheel based on a pre-stored parking attitude model to judge the parking attitude of the vehicle so as to determine whether the parking attitude is abnormal;
under the condition that the parking gesture is abnormal, acquiring a driver behavior signal of each unit time in a second time period from a second preset time before the current time to a third preset time after the current time;
acquiring a vehicle fault signal of each unit time in the second time period;
identifying the driver behavior signal according to a pre-stored driver abnormal behavior model to judge the abnormal level of the driver behavior;
and identifying the parking posture, the abnormal level of the driver behavior and the vehicle fault signal based on a pre-stored vehicle pit model to judge whether the vehicle has the pit accident.
2. The monitoring method according to claim 1, wherein the identifying the real-time three-axis acceleration signal and the speed signal of each wheel in the vehicle driving signal based on a pre-stored parking posture model to determine the parking posture of the vehicle to determine whether the parking posture is abnormal comprises:
judging whether the vehicle is in a parking state or not according to the speed signal;
determining an attitude angle of the vehicle per unit time within the first time period according to the three-axis acceleration signal when the vehicle is in the parking state;
and processing the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
3. The monitoring method according to claim 1, wherein the driver behavior signals include a vehicle locking signal, a double flashing light signal, a gear signal, an accelerator pedal signal, a main driving door signal and a steering wheel angle signal, and the identifying the driver behavior signals according to a pre-stored driver abnormal behavior model to determine the driver behavior abnormal level comprises:
judging whether a first abnormal behavior of unlocking within a preset time after getting off exists or not according to the main driving door signal and the vehicle locking signal;
judging whether a second abnormal behavior of starting the double flashing lamps exists or not according to the gear signal and the double flashing lamp signal;
judging whether a third abnormal behavior of pressing an accelerator pedal suddenly exists or not according to the accelerator pedal signal;
judging whether a fourth abnormal behavior of repeatedly operating the steering wheel exists or not according to the steering wheel turning angle signal;
judging whether a fifth abnormal behavior that the steering wheel is not returned to the right after the driver leaves the vehicle exists or not according to the main driving door signal and the steering wheel corner signal;
and judging the abnormal level of the driver behavior according to a preset rating rule, the first abnormal behavior, the second abnormal behavior, the third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior.
4. The monitoring method according to claim 1, wherein the vehicle fault signals include a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal and an electrical system fault signal, and the identifying the parking attitude, the driver behavior abnormality level and the vehicle fault signal according to a pre-stored vehicle pit model to determine whether the pit accident occurs to the vehicle comprises:
processing a feature vector formed by the parking posture, the abnormal behavior grade of the driver and the vehicle fault signal according to the pre-stored vehicle pit trapping model to obtain the probability of the pit trapping accident of the vehicle;
determining that the vehicle has the pit accident when the probability is greater than a predetermined threshold.
5. The monitoring method of claim 1, further comprising:
in the case that it is determined that the vehicle has the pit accident, an alarm signal is transmitted to a service provider of the vehicle so that the service provider can perform rescue according to the alarm signal.
6. A monitoring device of a vehicle, characterized by comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a vehicle running signal of each unit time in a first time period from first preset time before current time to current time;
the judging module is used for identifying and judging the parking gesture of the vehicle based on a pre-stored parking gesture model, wherein the real-time three-axis acceleration signal and the speed signal of each wheel in the vehicle driving signal are used for determining whether the parking gesture is abnormal or not;
the obtaining module is further used for obtaining a driver behavior signal of each unit time in a second time period from a second preset time before the current time to a third preset time after the current time and a vehicle fault signal of each unit time in the second time period under the condition that the parking gesture is abnormal;
the judging module is also used for identifying the driver behavior signal according to a pre-stored driver abnormal behavior model so as to judge the abnormal level of the driver behavior;
the judging module is further used for identifying the parking gesture, the abnormal level of the driver behavior and the vehicle fault signal based on a pre-stored vehicle pit model so as to judge whether the vehicle has the pit accident.
7. A server, characterized by comprising a memory storing a computer program and a processor for implementing the method of monitoring a vehicle crater accident according to any one of claims 1-5 when executing the computer program.
8. One or more non-transitory computer-readable storage media storing a computer program, wherein the computer program, when executed by one or more processors, implements the method of monitoring a vehicle pit accident of any one of claims 1-5.
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| CN202011255021.XA CN112406767B (en) | 2020-11-11 | 2020-11-11 | Monitoring method and device for vehicle pit accident, server and storage medium |
| PCT/CN2021/112249 WO2022100174A1 (en) | 2020-11-11 | 2021-08-12 | Detection method and apparatus for detecting accident of vehicle being stuck in ditch, server, and storage medium |
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