WO2022100174A1 - Detection method and apparatus for detecting accident of vehicle being stuck in ditch, server, and storage medium - Google Patents
Detection method and apparatus for detecting accident of vehicle being stuck in ditch, server, and storage medium Download PDFInfo
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- WO2022100174A1 WO2022100174A1 PCT/CN2021/112249 CN2021112249W WO2022100174A1 WO 2022100174 A1 WO2022100174 A1 WO 2022100174A1 CN 2021112249 W CN2021112249 W CN 2021112249W WO 2022100174 A1 WO2022100174 A1 WO 2022100174A1
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- 230000006399 behavior Effects 0.000 claims description 106
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 99
- 238000000034 method Methods 0.000 claims description 60
- 238000012544 monitoring process Methods 0.000 claims description 47
- 238000012806 monitoring device Methods 0.000 claims description 16
- 230000001133 acceleration Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
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- 238000004458 analytical method Methods 0.000 description 5
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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
Definitions
- the present application relates to the technical field of vehicles, and in particular, to a monitoring method, monitoring device, server and storage medium for a vehicle sinkhole accident.
- the vehicle When driving in bad weather or road conditions, the vehicle may have a sinkhole accident. After the accident, it is often impossible to rely on the driver to solve it. Active detection and timely rescue after the accident can provide great help to the driver.
- additional hardware for collecting data for sinkhole accidents is often used in the vehicle body, and data processing needs to be performed at the vehicle end, which increases the data processing load at the vehicle end.
- embodiments of the present application provide a monitoring method, a monitoring device, a server, and a storage medium for a vehicle sinkhole accident.
- the application provides a monitoring method for a vehicle sinkhole accident, the monitoring method comprising:
- Whether the sinkhole accident occurs to the vehicle is determined according to the parking posture, the driver behavior signal and the vehicle failure signal.
- the obtaining the vehicle driving signal includes:
- the vehicle driving signal of each unit time in the first time period from the first predetermined time before the current time to the current time is acquired.
- the judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal includes;
- the vehicle driving signal is identified according to the pre-stored parking attitude model to judge the parking attitude of the vehicle to determine whether the parking attitude is abnormal.
- the vehicle driving signal includes a real-time three-axis acceleration signal of the vehicle and a speed signal of each wheel of the vehicle, and the vehicle driving signal is identified according to a pre-stored parking attitude model to Judging the parking posture of the vehicle to determine whether the parking posture is abnormal includes:
- the attitude angle is processed according to the parking attitude model to obtain the parking attitude of the vehicle.
- acquiring the driver behavior signal and the vehicle fault signal includes;
- the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal includes:
- the driver's behavior signal is identified according to a pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
- the driver behavior signal includes a vehicle lock 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.
- the behavior model identifies the driver behavior signal to determine the abnormal level of driver behavior, including:
- the main driving door signal and the steering wheel angle signal determine whether there is a fifth abnormal behavior of the steering wheel not returning to the right position after getting off the car;
- the abnormal level of the driver's behavior is judged 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 .
- the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal includes:
- the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal are identified according to a pre-stored vehicle sinkhole model to determine whether the vehicle has the sinkhole accident.
- the vehicle fault signal includes a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal, and an electrical system fault signal, and the parking attitude, the driving, Identifying the abnormal level of employee behavior and the vehicle fault signal to determine whether the vehicle has the sinkhole accident includes:
- the feature vector formed by the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal is processed to obtain the probability of the vehicle having the sinkhole accident;
- the monitoring method further includes:
- an alarm signal is sent to a service provider of the vehicle so that the service provider can implement rescue according to the alarm signal.
- the application provides a monitoring device for a vehicle, and the monitoring device includes:
- the acquisition module is used to acquire the vehicle driving signal
- a judging module which is used for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal
- the obtaining module is further configured to obtain the driver behavior signal and the vehicle fault signal when the parking posture is abnormal;
- the judging module is further configured to judge whether the vehicle has the sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
- the present application provides a server, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to implement the method for monitoring a vehicle sinkhole accident according to any of the foregoing embodiments when the computer program is executed.
- the present application provides one or more non-volatile computer-readable storage media storing a computer program, when the computer program is executed by one or more processors, the monitoring of the vehicle sinkhole accident in any of the above embodiments is implemented method.
- the monitoring method, monitoring device, server and storage medium for a vehicle sinkhole accident it is possible to use the original equipment of the vehicle to determine whether a vehicle sinkhole accident has occurred according to the vehicle driving signal, the driver's behavior signal and the vehicle fault signal.
- the monitoring of vehicle sinkhole accidents can reduce the data processing load on the vehicle end, and can actively identify the occurrence of accidents after the vehicle sinks in, and carry out corresponding processing, improving vehicle after-sales service and enhancing user experience.
- FIG. 1 is a schematic flowchart of a monitoring method for a vehicle sinkhole accident according to some embodiments of the present application.
- FIG. 2 is a schematic block diagram of a monitoring device for a vehicle according to some embodiments of the present application.
- FIG. 3 is a schematic flowchart of a monitoring device for a vehicle according to some embodiments of the present application.
- FIG. 4 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 5 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 6 is a schematic diagram of three-axis coordinates of some embodiments of the present application.
- FIG. 7 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 8 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 9 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 10 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 11 is a model schematic diagram of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 12 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- FIG. 13 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
- the present application provides a method for monitoring a vehicle sinkhole accident, including:
- S14 Determine whether the vehicle has a sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
- the embodiment of the present application provides a server 100 .
- Server 100 includes processor 104 .
- the processor 104 is used for acquiring the vehicle driving signal, and for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, and for acquiring the driver behavior signal and the vehicle fault signal when the parking posture is abnormal, And it is used to judge whether the vehicle has a sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
- the processor 104 may be the processor 104 independently set for implementing the monitoring method for the vehicle sinkhole accident, or may be the processor 104 of the server 100 itself, which is not limited herein.
- an embodiment of the present application further provides a monitoring device 110 for a vehicle.
- the monitoring method for a vehicle sinkhole accident in the embodiment of the present application may be implemented by the monitoring device 110 for a vehicle.
- 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 acquisition module 112
- S12 and S14 may be implemented by the determination module 114 .
- the acquisition module 112 is used to acquire the vehicle driving signal, and to acquire the driver behavior signal and the vehicle fault signal when the parking posture is abnormal.
- the judging module 114 is used for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, and for judging whether the vehicle has a sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal.
- the vehicle when driving in bad weather, or when the driving road conditions are poor, the vehicle may have a sinkhole accident, and the wheels will sink into the snow, mud pits, ditch or manhole cover, etc., so that the vehicle cannot continue to drive. After a sinkhole accident, it is usually necessary to use the help of foreign objects to get the vehicle out of the predicament, which is often difficult to solve only by the driver.
- a camera system is used to identify road conditions
- a photosensitive system is used to detect road conditions
- a radar system is used for ranging, so as to prevent vehicle sinkhole accidents.
- the above solutions all need to add additional hardware for data collection for sinkhole accidents, such as camera systems, radar systems, etc., which increase vehicle production costs.
- the data collected by the above solution needs to be processed on the vehicle side, which also increases the data processing load on the vehicle side, which will slow down the response speed of the vehicle processor to a certain extent, and the user experience will be poor.
- the vehicle is judged by acquiring the vehicle driving signal.
- the parking attitude is determined to determine whether the parking attitude of the vehicle is abnormal, and in the case of abnormal parking attitude of the vehicle, the driver behavior signal and the vehicle fault signal are obtained.
- the vehicle driving signal, driver behavior signal and vehicle fault signal comprehensively determine whether the vehicle has a sinkhole accident.
- the data size and the amount of data calculation are small, which can reduce the data processing load on the vehicle end, and can actively identify the accident after the vehicle sinks. occurrence, and deal with it accordingly, improve the after-sales service of the vehicle and enhance the user experience.
- S11 includes:
- S111 Acquire a 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.
- S111 may be implemented by the obtaining module 112 .
- the obtaining module 112 is configured to obtain the vehicle running signal of each unit time in the first time period from the first predetermined time before the current time to the current time.
- the processor 104 is configured to acquire the vehicle running signal of each unit time in the first time period from the first predetermined time before the current time to the current time.
- 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 a time range from a predetermined time t11 before the current time t to a predetermined time t12 after the current time t.
- the first time period may be a time range from a predetermined time t11 before the current time t to the current time t.
- obtain the vehicle driving signal of each unit time in the time range of t11-t analyze and process the vehicle driving signal of each unit time, and judge the parking posture of the vehicle, so as to determine whether the parking posture of the vehicle is not. abnormal.
- the parking posture of the vehicle at the current time and the parking posture of the vehicle before 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 analyzing the result of the pit accident can be ensured.
- 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.
- obtain the vehicle driving signal of each unit time in the time range of t11-t12 analyze and process the vehicle driving signal of each unit time, and judge the parking posture of the vehicle, so as to determine whether the parking posture of the vehicle is not. abnormal.
- the vehicle parking attitude at the current time, the vehicle parking attitude before the current time, and the vehicle parking attitude after the current time can be compared and analyzed, the vehicle parking attitude at the current time can be accurately judged, and the accuracy of the analysis result of the sinkhole accident can be ensured.
- the length of the time range of the first time period can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc., and there is no specific limitation, for example, it can be 3 seconds, 5 seconds, 10 seconds, 13 seconds , 17 seconds, etc.
- the predetermined time t11 before the current time t and the predetermined time t12 after the current time t can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc. 3 seconds, 5 seconds, 8 seconds, 10 seconds, etc., the specific values of t11 and t12 may be equal or unequal.
- S12 includes:
- S121 Identify the vehicle driving signal according to the pre-stored parking attitude model to determine the parking attitude of the vehicle to determine whether the parking attitude is abnormal.
- S121 may be implemented by the judgment module 114 .
- the judging module 114 is configured to identify the driving signal of the vehicle according to the pre-stored parking posture model to judge the parking posture of the vehicle to determine whether the parking posture is abnormal.
- the processor 104 is configured to identify the vehicle driving signal according to the pre-stored parking posture model to judge the parking posture of the vehicle and determine whether the parking posture is abnormal.
- the vehicle driving signal can be recognized according to the pre-stored parking attitude model, and the parking attitude of the vehicle can be determined, thereby determining whether the parking attitude is abnormal.
- the specific processing method of the parking attitude model can be selected according to the data category, data volume and other factors, which is not limited. For example, it can be a gradient boosting tree (Gradient Boosting Decision Tree, GBDT) algorithm or a support vector machine (Support Vector Machine, SVM) algorithm, it can also be a regression algorithm, etc.
- GDT gradient boosting tree
- SVM Support Vector Machine
- the identification process of the vehicle running signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use.
- the parking attitude model can be applied to a variety of vehicle models and/or systems, reducing development costs.
- the vehicle driving signal includes the real-time three-axis acceleration signal of the vehicle and the speed signal of each wheel of the vehicle, and S121 includes:
- S1211 Determine whether the vehicle is in a parked state according to the speed signal
- S1213 Process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
- S1211-S1213 may be implemented by the determination module 114 .
- the determination module 114 is configured to determine whether the vehicle is in a parked state according to the speed signal, and to determine the attitude angle of the vehicle per unit time in the first time period according to the three-axis acceleration signal when the vehicle is in a parked state, and It is used to process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
- the processor 104 is configured to determine whether the vehicle is in a parked state according to the speed signal, and to determine the speed of the vehicle per unit time in the first time period according to the three-axis acceleration signal when the vehicle is in a parked state. attitude angle, and is used to process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
- the speed signal can be the speed signal collected from the driving wheel, or the speed signal collected from all normal tires.
- the speed signals of the left front wheel and the right front wheel may be collected, and the speed signals of all the wheels in normal use may also be collected.
- the speed signals of the left rear wheel and the right front wheel may be collected, and the speed signals of all the wheels in normal use may also be collected.
- the speed signals of all the wheels in normal use including the driven wheels and the driving wheels, can be collected.
- GPS Global Positioning System
- the three-axis acceleration signal of the vehicle per unit time in the first period of time is obtained, the attitude angle of the vehicle can be determined according to the three-axis acceleration signal, and the attitude angle is processed by using the parking attitude model to obtain The parking status of the vehicle.
- the three-axis acceleration signal includes a pitch angle signal of the x-axis, a yaw angle signal of the y-axis, and a roll angle signal of the z-axis.
- the pitch angle pitch can represent the angle between the x-axis of the vehicle coordinate system and the horizontal plane
- the yaw angle yaw can represent the angle between the y-axis of the vehicle coordinate system and the horizontal plane
- the roll angle roll can represent the vehicle coordinate system. The angle between the z-axis and the horizontal plane .
- the attitude angle of the vehicle per unit time in the first time period is obtained, and each obtained attitude angle is input into the parking attitude model, and the attitude angle is processed by the parking attitude model to output whether the vehicle has occurred. There is no abnormal probability of leaning forward, leaning backward, leaning left, leaning right, or the vehicle is parked.
- the parking state of the vehicle is obtained: the probability of the vehicle tilting forward is 0.2, the probability of the vehicle tilting backward is 0.9, and the probability of no abnormality is 0.1. Then, according to the maximum value of the above results, it can be determined that the vehicle is tilted backwards at the current time.
- the parking state of the vehicle is obtained: the probability of the vehicle leaning to the left is 0.7, the probability of the vehicle leaning right is 0.4, and the probability of no abnormality is 0.1. Then, according to the maximum value of the above results, it can be determined that the vehicle leans to the left at the current time.
- the attitude angle of the vehicle per 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 determined.
- S13 includes:
- S131 In the case of abnormal parking posture, obtain the driver behavior signal of each unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time;
- S132 Acquire a vehicle fault signal per unit time in the second time period.
- S131 and S132 may be implemented by the obtaining module 112 .
- the obtaining module 112 is configured to obtain the driver behavior signal per unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time in the case of abnormal parking posture, and use in acquiring the vehicle fault signal per unit time in the second time period.
- the processor 104 is configured to obtain the driver behavior per unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time when the parking posture is abnormal signal, and a vehicle fault signal for each unit time in the second time period.
- 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.
- the second time period may be a time range from a predetermined time t21 before the current time t to the current time t.
- 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.
- the length of the time range of the second time period can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc., and is not specifically limited, for example, it can be 10 seconds, 30 seconds, 50 seconds, 60 seconds , 100 seconds, etc.
- the second predetermined time t21 before the current time t and the third predetermined time t22 after the current time t can be set according to factors such as road conditions, vehicle age, vehicle maintenance records, vehicle performance, etc. Seconds, 30 seconds, 50 seconds, 80 seconds, etc., the specific values of t21 and t22 may be equal or unequal.
- S14 includes:
- S141 Identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
- S141 may be implemented by the judgment module 114 .
- the judging module 114 is configured to identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to judge the abnormal level of the driver's behavior.
- the processor 104 is configured to identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
- the driver's behavior signal can be identified according to the pre-stored abnormal driver behavior model, to determine whether the driver's behavior is abnormal, and to evaluate the abnormal level of the driver's behavior, so as to determine whether the vehicle has a sinkhole accident.
- the specific processing method of the driver's abnormal behavior model can be selected according to the data type, data volume and other factors, and there is no specific limitation. For example, it can be a counting model, a GBDT algorithm, an SVM algorithm, or a regression algorithm.
- the identification process of the driver's behavior signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use. After the model development is completed, based on the characteristics of high model reuse rate, the abnormal driver behavior model can be applied to a variety of vehicle models and/or systems, reducing development costs.
- the driver behavior signal includes a vehicle lock 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
- S141 includes:
- S1411 Determine whether there is a first abnormal behavior of unlocking the car within a predetermined period of time after getting off the car according to the main driving door signal and the vehicle locking signal;
- S1412 Determine whether there is a second abnormal behavior of turning on the double flashing light according to the gear signal and the double flashing light signal;
- S1414 Determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel according to the steering wheel angle signal
- S1415 According to the main driving door signal and the steering wheel angle signal, determine whether there is a fifth abnormal behavior of the steering wheel not returning to the right position after getting off the car;
- S1416 Determine the abnormal level of the driver's behavior 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.
- S1411-S1416 may be implemented by the determination module 114 .
- the judging module 114 is used for judging whether there is a first abnormal behavior of not locking the car within a predetermined period of time after getting off the car according to the main driving door signal and the vehicle locking signal, and for judging whether it is not according to the gear signal and the double flashing light signal.
- the third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior determine the abnormal level of the driver's behavior.
- the processor 104 is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle according to the main driving door signal and the vehicle locking signal, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to use the gear signal and the double flashing
- the light signal judges whether there is a second abnormal behavior of turning on the double flashing lights, and the third abnormal behavior for judging whether there is a hard pressing of the accelerator pedal according to the accelerator pedal signal, and the third abnormal behavior for judging whether the steering wheel is repeatedly operated according to the steering wheel angle signal.
- abnormal behaviors and a fifth abnormal behavior for judging whether there is a steering wheel not returning after getting off the car according to the main driving door signal and the steering wheel angle signal, and a fifth abnormal behavior for judging whether the steering wheel does not return after getting off the car, and for according to the preset rating rules, the first abnormal behavior, the second abnormal behavior
- the abnormal behavior, the third abnormal behavior, the fourth abnormal behavior, and the fifth abnormal behavior determine the abnormal level of the driver's behavior.
- the vehicle lock signal is 0, that is, the vehicle is not locked. At this time, it can be determined that the driver has the first abnormal behavior X1 in the second time period.
- the abnormal level of driver behavior is set to 1, and when the first abnormal behavior X1 does not exist, the abnormal level of driver behavior is set to 0.
- the driver has the first abnormal behavior X1
- the gear signal and the double flashing light signal it is judged whether the driver has the second abnormal behavior X2 of turning on the double flashing light in the second time period .
- the current gear of the vehicle is the parking gear
- the double flashing light signal is 1, that is, the double flashing light is in a light-on state.
- the abnormal level of the driver's behavior is 2. In the case where the first abnormal behavior X1 exists but the second abnormal behavior X2 does not exist, the abnormal level of the driver's behavior is still 1.
- the driver when it is determined that the driver has the first abnormal behavior X1 in the second time period, according to the accelerator pedal signal, the main driving door signal and the steering wheel angle signal, it is determined that the driver is in the second time period. Whether there is a third abnormal behavior X3 of pressing the accelerator pedal hard, a fourth abnormal behavior X4 of repeatedly operating the steering wheel, or a fifth abnormal behavior X5 of the steering wheel not returning to the right position after getting off the car. Among them, the number of times a that the depth of the accelerator pedal is stepped over the predetermined depth threshold in the second time period is calculated, and when the value of a is greater than or equal to the first predetermined number of times threshold, it is determined that the driver has a third abnormal behavior X3.
- the abnormal behavior level of the driver is 2. In the presence of the first abnormal behavior X1, but not the third abnormal behavior X3, the fourth abnormal behavior X4 and the fifth abnormal behavior X5, or the existence of the third abnormal behavior X3, the fourth abnormal behavior X4 or the fifth abnormal behavior X5 In the case of less than two types, the abnormal driver behavior level is still 1.
- the driver behavior abnormal level is 3.
- the abnormal level of the driver's behavior is 0. If X1 exists, the abnormal level of driver behavior is 1. If X1 and X2 exist, the abnormal level of driver behavior is 2. If X1, X4 and X5 exist, the abnormal level of driver behavior is 2. If X1, X2 and X5 exist, the abnormal level of driver behavior is 2. If there are X1, X3, X4 and X5, the abnormal level of driver behavior is 2. If there are X1, X2, X3, X4 and X5, the abnormal level of driver behavior is 3. And so on.
- the behavior of the driver in the second time period can be compared and analyzed, the abnormal level of the driver's behavior can be accurately judged, and the accuracy of analyzing the result of the sinkhole accident can be ensured.
- the above-mentioned thresholds such as the predetermined depth threshold, the first predetermined number of times threshold, the predetermined difference threshold, the second predetermined number of times threshold, and the predetermined angle threshold can all be set according to parameters such as the vehicle model and the driver's behavior habits, and are not specifically limited.
- the predetermined depth threshold may be 10, 15, 18, etc.
- the first predetermined number of times threshold and the second predetermined number of times threshold may be 1, 2, 5, etc.
- the predetermined difference threshold may be 50, 80, 100, etc.
- the predetermined angle threshold may be 250 degrees, 300 degrees, 350 degrees, 400 degrees, or the like.
- S14 includes:
- S142 Identify the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal according to the pre-stored vehicle sinkhole model to determine whether the vehicle has a sinkhole accident.
- S142 may be implemented by the determination module 114 .
- the judging module 114 is configured to identify the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal according to the pre-stored vehicle sinkhole model, so as to judge whether the vehicle has a sinkhole accident.
- the processor 104 is configured to identify the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model, so as to determine whether the vehicle has a sinkhole accident.
- the parking posture, the abnormal level of driver behavior and the vehicle fault signal can be identified according to the pre-stored vehicle sinkhole model to determine whether the vehicle has a sinkhole accident.
- the specific processing method of the vehicle pit model can be selected according to the data type, data volume and other factors, and there is no specific limitation. For example, it can be a counting model, a GBDT algorithm, an SVM algorithm, or a regression algorithm.
- the identification process of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use.
- the vehicle sinkhole model can be applied to a variety of vehicle models and/or systems, reducing development costs.
- the vehicle fault signal includes a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal and an electrical system fault signal
- S142 includes:
- S1421 Process the feature vector formed by the parking posture, the abnormal level of driver behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability of a vehicle sinkhole accident;
- S1421 and S1422 may be implemented by the judgment module 114 .
- the judging module 114 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability that the vehicle has a sinkhole accident, and is used to obtain the probability that the vehicle has a sinkhole accident when the probability is greater than It is determined that the vehicle has a sinkhole accident when the predetermined threshold value is used.
- the processor 104 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability of the vehicle having a sinkhole accident, and use It is determined that the vehicle has a pit accident when the probability is greater than a predetermined threshold.
- the vehicle fault signal includes 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. When the difference between the two is greater than a predetermined speed difference threshold, it can be considered that the wheel slip phenomenon occurs.
- Chassis fault signal and tire pressure monitoring system fault signal can be detected by vehicle systems such as anti-lock braking system, electronic stability program, automatic parking system and/or tire pressure monitoring system. When any one or more of the monitoring system fault signals is greater than 0, it may be considered that the vehicle chassis and/or wheels are faulty.
- the electrical system fault signal can be detected by the motor system, battery management system, light control system, radar system and other vehicle systems. When any one or more fault signals in the electrical system are detected to be greater than 0, it can be considered that the vehicle electrical system is faulty .
- the predetermined speed difference threshold, the predetermined threshold for judging the probability of vehicle sinking, etc. can be set according to parameters such as model, driver behavior, vehicle maintenance record, vehicle service life, etc., and there is no specific limitation.
- the predetermined speed difference The value threshold may be 1 m/s, 2 m/s, 3 m/s, 5 m/s, etc.
- the predetermined threshold for judging the probability of vehicle sinking may be 0.5, 0.7, 0.8, etc.
- the monitoring method further includes:
- S15 may be implemented by the determination module 114 .
- the judging module 114 is configured to send an alarm signal to a service provider of the vehicle when it is determined that the vehicle has a sinkhole accident, so that the service provider can implement rescue according to the alarm signal.
- the processor 104 is configured to send an alarm signal to a service provider of the vehicle when it is determined that the vehicle has a sinkhole accident, so that the service provider can implement rescue according to the alarm signal.
- an alarm signal is sent to the service provider of the vehicle, and the service provider can implement rescue according to the alarm signal, or contact the driver to confirm that the vehicle has a sinkhole accident and implement rescue according to the situation.
- the embodiments of the present application also provide a computer-readable storage medium.
- One or more non-volatile computer-readable storage media storing a computer program, when the computer program is executed by one or more processors, implements the vehicle monitoring method of any one of the above embodiments.
- the embodiments of the present application also provide a vehicle.
- the vehicle includes a memory and one or more processors, and one or more programs are stored in the memory and configured to be executed by the one or more processors.
- the program includes instructions for performing the monitoring method for a vehicle sinkhole accident of any one of the above embodiments.
- the processor can be used to provide the computing and control capabilities that underpin the operation of the entire vehicle.
- the memory of the vehicle provides an environment for the execution of computer readable instructions in the memory.
- the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or the like.
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Abstract
A detection method and apparatus for detecting an accident of a vehicle being stuck in a ditch, a server, and a storage medium. The detection method comprises: acquiring a vehicle driving signal; determining, according to the vehicle driving signal, a stationary pose of a vehicle so as to determine whether the stationary pose is abnormal; if the stationary pose is abnormal, acquiring a driver behavior signal and a vehicle fault signal; and determining whether an accident of the vehicle being stuck in a ditch has occurred according to the stationary pose, the driver behavior signal and the vehicle fault signal. In the detection method and apparatus for a vehicle, a server, and a storage medium, whether an accident of a vehicle being stuck in a ditch has occurred is determined according to a vehicle driving signal, a driver behavior signal and a vehicle fault signal. Since original equipment of a vehicle is used to perform detection on an accident of the vehicle being stuck in a ditch, data processing load of a vehicle terminal can be reduced. Moreover, when a vehicle is stuck in a ditch, active identification of the accident and corresponding processing can be performed, thereby improving after-sales service of vehicles, and improving user experience.
Description
相关申请的交叉引用CROSS-REFERENCE TO RELATED APPLICATIONS
本申请要求于2020年11月11日提交的申请号为202011255021.X的中国申请的优先权,其在此处于所有目的通过引用将其全部内容并入本文。This application claims priority to Chinese Application No. 202011255021.X, filed on November 11, 2020, which is hereby incorporated by reference in its entirety for all purposes.
本申请涉及车辆技术领域,特别涉及一种车辆陷坑事故的监控方法、监控装置、服务器和存储介质。The present application relates to the technical field of vehicles, and in particular, to a monitoring method, monitoring device, server and storage medium for a vehicle sinkhole accident.
在恶劣天气或路况中行车,车辆可能会发生陷坑事故,事故发生后,依靠驾驶员自身往往无法解决,在事故后的主动发现并及时进行救援能够为驾驶员提供极大的帮助。相关技术中,多采用在车身额外添加针对陷坑事故进行数据采集的硬件,且需要在车端进行数据处理,增加了车端的数据处理负荷。When driving in bad weather or road conditions, the vehicle may have a sinkhole accident. After the accident, it is often impossible to rely on the driver to solve it. Active detection and timely rescue after the accident can provide great help to the driver. In the related art, additional hardware for collecting data for sinkhole accidents is often used in the vehicle body, and data processing needs to be performed at the vehicle end, which increases the data processing load at the vehicle end.
发明内容SUMMARY OF THE INVENTION
有鉴于此,本申请的实施例提供了一种车辆陷坑事故的监控方法、监控装置、服务器和存储介质。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 sinkhole accident.
本申请提供了一种车辆陷坑事故的监控方法,所述监控方法包括:The application provides a monitoring method for a vehicle sinkhole accident, the monitoring method comprising:
获取车辆行驶信号;Obtain vehicle driving signals;
根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常;Judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal;
在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号;In the case that the parking posture is abnormal, obtain the driver behavior signal and the vehicle fault signal;
根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故。Whether the sinkhole accident occurs to the vehicle is determined according to the parking posture, the driver behavior signal and the vehicle failure signal.
在某些实施方式中,所述获取车辆行驶信号包括:In some embodiments, the obtaining the vehicle driving signal includes:
获取当前时间前第一预定时间至当前时间的第一时间段内每个单位时间的车辆行驶信号。The vehicle driving signal of each unit time in the first time period from the first predetermined time before the current time to the current time is acquired.
在某些实施方式中,所述根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常包括;In some implementations, the judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal includes;
根据预存储的停车姿态模型对所述车辆行驶信号进行识别以判断所述车辆的停车姿态从而确定所述停车姿态是否异常。The vehicle driving signal is identified according to the pre-stored parking attitude model to judge the parking attitude of the vehicle to determine whether the parking attitude is abnormal.
在某些实施方式中,所述车辆行驶信号包括所述车辆的实时三轴加速度信号和所述车辆各个车轮的速度信号,所述根据预存储的停车姿态模型对所述车辆行驶信号进行识别以判断所述车辆 的停车姿态从而确定所述停车姿态是否异常包括:In some embodiments, the vehicle driving signal includes a real-time three-axis acceleration signal of the vehicle and a speed signal of each wheel of the vehicle, and the vehicle driving signal is identified according to a pre-stored parking attitude model to Judging the parking posture of the vehicle to determine whether the parking posture is abnormal includes:
根据所述速度信号判断所述车辆是否处于停车状态;Determine whether the vehicle is in a parking state according to the speed signal;
在所述车辆处于所述停车状态时,根据所述三轴加速度信号确定所述车辆在所述第一时间段内每个单位时间的姿态角;When the vehicle is in the parking state, determining the attitude angle of the vehicle per unit time in the first time period according to the three-axis acceleration signal;
根据所述停车姿态模型对所述姿态角进行处理以得到所述车辆的停车姿态。The attitude angle is processed according to the parking attitude model to obtain the parking attitude of the vehicle.
在某些实施方式中,所述在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号包括;In some embodiments, in the case that the parking posture is abnormal, acquiring the driver behavior signal and the vehicle fault signal includes;
在所述停车姿态异常的情况下,获取当前时间前第二预定时间至当前时间后第三预定时间的第二时间段内每个单位时间的驾驶员行为信号;In the case that the parking posture is abnormal, obtain the driver behavior signal of each unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time;
获取所述第二时间段内每个单位时间的车辆故障信号。Acquire a vehicle fault signal per unit time in the second time period.
在某些实施方式中,所述根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故包括:In some embodiments, the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal includes:
根据预存储的驾驶员异常行为模型对所述驾驶员行为信号进行识别以判断驾驶员行为异常等级。The driver's behavior signal is identified according to a pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
在某些实施方式中,所述驾驶员行为信号包括车辆上锁信号、双闪灯信号、挡位信号、加速踏板信号、主驾车门信号和方向盘转角信号,所述根据预存储的驾驶员异常行为模型对所述驾驶员行为信号进行识别以判断驾驶员行为异常等级包括:In some embodiments, the driver behavior signal includes a vehicle lock 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. The behavior model identifies the driver behavior signal to determine the abnormal level of driver behavior, including:
根据所述主驾车门信号和所述车辆上锁信号判断是否存在下车后预定时长内未锁车的第一异常行为;Determine whether there is a first abnormal behavior of unlocking the vehicle within a predetermined period of time after getting off the vehicle according to the main driving door signal and the vehicle locking signal;
根据所述挡位信号和所述双闪灯信号判断是否存在开启双闪灯的第二异常行为;Determine whether there is a second abnormal behavior of turning on the double flashing lights according to the gear signal and the double flashing light signal;
根据所述加速踏板信号判断是否存在猛踩加速踏板的第三异常行为;judging whether there is a third abnormal behavior of slamming the accelerator pedal according to the accelerator pedal signal;
根据所述方向盘转角信号判断是否存在反复操作方向盘的第四异常行为;Determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel according to the steering wheel angle signal;
根据所述主驾车门信号和所述方向盘转角信号判断是否存在下车后方向盘未回正的第五异常行为;According to the main driving door signal and the steering wheel angle signal, determine whether there is a fifth abnormal behavior of the steering wheel not returning to the right position after getting off the car;
根据预设定的评级规则、所述第一异常行为、所述第二异常行为、所述第三异常行为、所述第四异常行为和所述第五异常行为判断所述驾驶员行为异常等级。The abnormal level of the driver's behavior is judged 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 .
在某些实施方式中,所述根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故包括:In some embodiments, the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal includes:
根据预存储的车辆陷坑模型对所述停车姿态、所述驾驶员行为异常等级以及所述车辆故障信号进行识别以判断所述车辆是否发生所述陷坑事故。The parking posture, the abnormal level of the driver's behavior and the vehicle fault signal are identified according to a pre-stored vehicle sinkhole model to determine whether the vehicle has the sinkhole accident.
在某些实施方式中,所述车辆故障信号包括打滑信号、底盘故障信号、胎压监控系统故障信号和电气系统故障信号,所述根据预存储的车辆陷坑模型对所述停车姿态、所述驾驶员行为异常 等级以及所述车辆故障信号进行识别以判断所述车辆是否发生所述陷坑事故包括:In some embodiments, the vehicle fault signal includes a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal, and an electrical system fault signal, and the parking attitude, the driving, Identifying the abnormal level of employee behavior and the vehicle fault signal to determine whether the vehicle has the sinkhole accident includes:
根据所述根据预存储的车辆陷坑模型对由所述停车姿态、所述驾驶员行为异常等级以及所述车辆故障信号构成的特征向量进行处理以得到所述车辆发生所述陷坑事故的概率;According to the pre-stored vehicle sinkhole model, the feature vector formed by the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal is processed to obtain the probability of the vehicle having the sinkhole accident;
在所述概率大于预定阈值时确定所述车辆发生所述陷坑事故。It is determined that the sinkhole accident occurs to the vehicle when the probability is greater than a predetermined threshold.
在某些实施方式中,所述监控方法还包括:In certain embodiments, the monitoring method further includes:
在确定所述车辆发生所述陷坑事故的情况下,发送报警信号至所述车辆的服务商以使得所述服务商可根据所述报警信号实施救援。When it is determined that the vehicle has the sinkhole accident, an alarm signal is sent to a service provider of the vehicle so that the service provider can implement rescue according to the alarm signal.
本申请提供了一种车辆的监控装置,所述监控装置包括:The application provides a monitoring device for a vehicle, and the monitoring device includes:
获取模块,所述获取模块用于获取车辆行驶信号;an acquisition module, the acquisition module is used to acquire the vehicle driving signal;
判断模块,所述判断模块用于根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常;a judging module, which is used for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal;
所述获取模块还用于在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号;The obtaining module is further configured to obtain the driver behavior signal and the vehicle fault signal when the parking posture is abnormal;
所述判断模块还用于根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故。The judging module is further configured to judge whether the vehicle has the sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
本申请提供了一种服务器,包括存储器和处理器,所述存储器存储有计算机程序,所述处理器用于执行所述计算机程序时实现上述任一实施方式的车辆陷坑事故的监控方法。The present application provides a server, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to implement the method for monitoring a vehicle sinkhole accident according to any of the foregoing embodiments when the computer program is executed.
本申请提供了一个或多个存储有计算机程序的非易失性计算机可读存储介质,当所述计算机程序被一个或多个处理器执行时,实现上述任一实施方式的车辆陷坑事故的监控方法。The present application provides one or more non-volatile computer-readable storage media storing a computer program, when the computer program is executed by one or more processors, the monitoring of the vehicle sinkhole accident in any of the above embodiments is implemented method.
本申请实施方式的车辆陷坑事故的监控方法、监控装置、服务器和存储介质中,根据车辆行驶信号、驾驶员行为信号和车辆故障信号判断车辆是否发生陷坑事故,使用车辆原有的设备即可实现对车辆陷坑事故的监控,能够减轻车端的数据处理负荷,且能够在车辆陷坑后主动识别到事故的发生,并进行相应的处理,改进了车辆售后服务,提升用户体验。In the monitoring method, monitoring device, server and storage medium for a vehicle sinkhole accident according to the embodiments of the present application, it is possible to use the original equipment of the vehicle to determine whether a vehicle sinkhole accident has occurred according to the vehicle driving signal, the driver's behavior signal and the vehicle fault signal. The monitoring of vehicle sinkhole accidents can reduce the data processing load on the vehicle end, and can actively identify the occurrence of accidents after the vehicle sinks in, and carry out corresponding processing, improving vehicle after-sales service and enhancing user experience.
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解,其中:The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:
图1是本申请某些实施方式的车辆陷坑事故的监控方法的流程示意图。FIG. 1 is a schematic flowchart of a monitoring method for a vehicle sinkhole accident according to some embodiments of the present application.
图2是本申请某些实施方式的车辆的监控装置的模块示意图。FIG. 2 is a schematic block diagram of a monitoring device for a vehicle according to some embodiments of the present application.
图3是本申请某些实施方式的车辆的监控装置的流程示意图。FIG. 3 is a schematic flowchart of a monitoring device for a vehicle according to some embodiments of the present application.
图4是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 4 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图5是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 5 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图6是本申请某些实施方式的三轴坐标示意图。FIG. 6 is a schematic diagram of three-axis coordinates of some embodiments of the present application.
图7是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 7 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图8是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 8 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图9是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 9 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图10是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 10 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图11是本申请某些实施方式的车辆的监控方法的模型示意图。FIG. 11 is a model schematic diagram of a vehicle monitoring method according to some embodiments of the present application.
图12是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 12 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
图13是本申请某些实施方式的车辆的监控方法的流程示意图。FIG. 13 is a schematic flowchart of a vehicle monitoring method according to some embodiments of the present application.
下面详细描述本申请的实施例,所述实施例的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的,旨在用于解释本申请,而不能理解为对本申请的限制。The following describes in detail the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein the same or similar 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 accompanying drawings are exemplary, and are intended to be used to explain the present application, but should not be construed as a limitation to the present application.
请参阅图1,本申请提供一种车辆陷坑事故的监控方法,包括:Referring to FIG. 1, the present application provides a method for monitoring a vehicle sinkhole accident, including:
S11:获取车辆行驶信号;S11: Obtain the vehicle driving signal;
S12:根据车辆行驶信号判断车辆的停车姿态以确定停车姿态是否异常;S12: Judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal;
S13:在停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号;S13: In the case of abnormal parking posture, obtain the driver's behavior signal and the vehicle fault signal;
S14:根据停车姿态、驾驶员行为信号和车辆故障信号判断车辆是否发生陷坑事故。S14: Determine whether the vehicle has a sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
本申请实施方式提供了一种服务器100。服务器100包括处理器104。处理器104用于获取车辆行驶信号,及用于根据车辆行驶信号判断车辆的停车姿态以确定停车姿态是否异常,及用于在停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号,以及用于根据停车姿态、驾驶员行为信号和车辆故障信号判断车辆是否发生陷坑事故。其中,处理器104可以是为实施车辆陷坑事故的监控方法而独立设置的处理器104,也可以是服务器100自身的处理器104,在此不做限制。The embodiment of the present application provides a server 100 . Server 100 includes processor 104 . The processor 104 is used for acquiring the vehicle driving signal, and for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, and for acquiring the driver behavior signal and the vehicle fault signal when the parking posture is abnormal, And it is used to judge whether the vehicle has a sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal. Wherein, the processor 104 may be the processor 104 independently set for implementing the monitoring method for the vehicle sinkhole accident, or may be the processor 104 of the server 100 itself, which is not limited herein.
请参阅图2,本申请实施方式还提供了一种车辆的监控装置110,本申请实施方式的车辆陷坑事故的监控方法可以由车辆的监控装置110实现。车辆的监控装置110包括获取模块112和判断模块114。S11和S13可以由获取模块112实现,S12和S14可以由判断模块114实现。或者说,获取模块112用于获取车辆行驶信号,以及用于在停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号。判断模块114用于根据车辆行驶信号判断车辆的停车姿态以确定停车姿态是否异常,以及用于根据停车姿态、驾驶员行为信号和车辆故障信号判断车辆是否发生陷坑事故。Referring to FIG. 2 , an embodiment of the present application further provides a monitoring device 110 for a vehicle. The monitoring method for a vehicle sinkhole accident in the embodiment of the present application may be implemented by the monitoring device 110 for a vehicle. 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 acquisition module 112 , and S12 and S14 may be implemented by the determination module 114 . In other words, the acquisition module 112 is used to acquire the vehicle driving signal, and to acquire the driver behavior signal and the vehicle fault signal when the parking posture is abnormal. The judging module 114 is used for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal, and for judging whether the vehicle has a sinkhole accident according to the parking posture, the driver behavior signal and the vehicle fault signal.
具体地,在恶劣天气中行车,或行车路况较差时,车辆可能会发生陷坑事故,车轮陷入雪地、泥坑、水沟或井盖等,使车辆无法继续行驶。在陷坑事故发生后,通常需要借助外物的帮助使车辆脱离困境,仅依靠驾驶员自身往往难以解决。Specifically, when driving in bad weather, or when the driving road conditions are poor, the vehicle may have a sinkhole accident, and the wheels will sink into the snow, mud pits, ditch or manhole cover, etc., so that the vehicle cannot continue to drive. After a sinkhole accident, it is usually necessary to use the help of foreign objects to get the vehicle out of the predicament, which is often difficult to solve only by the driver.
相关技术中,或使用摄像头系统识别路况,或使用光敏系统检测路况,或使用雷达系统进行测距,以防止车辆发生陷坑事故。而上述方案均需在车身额外添加针对陷坑事故进行数据采集的硬件,例如摄像头系统、雷达系统等,增加了车辆生产成本。且上述方案采集的数据需要在车端 进行处理,也增加了车端的数据处理负荷,在一定程度上会拖慢车辆处理器的响应速度,用户体验较差。In the related art, a camera system is used to identify road conditions, a photosensitive system is used to detect road conditions, or a radar system is used for ranging, so as to prevent vehicle sinkhole accidents. The above solutions all need to add additional hardware for data collection for sinkhole accidents, such as camera systems, radar systems, etc., which increase vehicle production costs. In addition, the data collected by the above solution needs to be processed on the vehicle side, which also increases the data processing load on the vehicle side, which will slow down the response speed of the vehicle processor to a certain extent, and the user experience will be poor.
相较于使用摄像头系统、光敏系统和雷达系统实现车辆的陷坑预警的方案,本申请实施方式的车辆陷坑事故的监控方法、服务器100和车辆的监控装置110中,通过获取车辆行驶信号判断车辆的停车姿态,从而确定车辆的停车姿态是否异常,并在车辆停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号。接着根据车辆行驶信号、驾驶员行为信号和车辆故障信号,综合判定车辆是否发生陷坑事故。如此,只需使用车辆原有的设备,即可实现对车辆陷坑事故的监控,数据大小、对数据的计算量较小,能够减轻车端的数据处理负荷,且能够在车辆陷坑后主动识别到事故的发生,并进行相应的处理,改进了车辆售后服务,提升用户体验。Compared with the solution of using the camera system, the photosensitive system and the radar system to realize the early warning of the vehicle sinkhole, in the monitoring method, the server 100 and the vehicle monitoring device 110 of the vehicle sinkhole accident according to the embodiment of the present application, the vehicle is judged by acquiring the vehicle driving signal. The parking attitude is determined to determine whether the parking attitude of the vehicle is abnormal, and in the case of abnormal parking attitude of the vehicle, the driver behavior signal and the vehicle fault signal are obtained. Then, according to the vehicle driving signal, driver behavior signal and vehicle fault signal, comprehensively determine whether the vehicle has a sinkhole accident. In this way, only the original equipment of the vehicle can be used to monitor the vehicle sinkhole accident. The data size and the amount of data calculation are small, which can reduce the data processing load on the vehicle end, and can actively identify the accident after the vehicle sinks. occurrence, and deal with it accordingly, improve the after-sales service of the vehicle and enhance the user experience.
请参阅图3,在某些实施方式中,S11包括:Referring to Figure 3, in some embodiments, S11 includes:
S111:获取当前时间前第一预定时间至当前时间的第一时间段内每个单位时间的车辆行驶信号。S111: Acquire a 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.
在某些实施方式中,S111可以由获取模块112实现。或者说,获取模块112用于获取当前时间前第一预定时间至当前时间的第一时间段内每个单位时间的车辆行驶信号。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 running signal of each unit time in the first time period from the first predetermined time before the current time to the current time.
在某些实施方式中,处理器104用于获取当前时间前第一预定时间至当前时间的第一时间段内每个单位时间的车辆行驶信号。In some embodiments, the processor 104 is configured to acquire the vehicle running signal of each unit time in the first time period from the first predetermined time before the current time to the current time.
具体地,在车辆行驶过程中,获取第一时间段内每个单位时间的车辆行驶信号,根据车辆行驶信号判断车辆的停车姿态,从而确定车辆的停车姿态是否异常。其中,第一时间段可以是当前时间t以前的预定时间t11至当前时间t的时间范围,也可以是当前时间t以前预定时间t11至当前时间t以后预定时间t12的时间范围。Specifically, during the running of the vehicle, the vehicle running signal of each unit time in the first time period is obtained, and the parking posture of the vehicle is judged according to the vehicle running signal, so as to determine whether the parking posture of the vehicle is abnormal. 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 a time range from a predetermined time t11 before the current time t to a predetermined time t12 after the current time t.
在一些实施例中,第一时间段可以是当前时间t以前的预定时间t11至当前时间t的时间范围。在车辆行驶过程中,获取t11-t的时间范围内每个单位时间的车辆行驶信号,对每个单位时间的车辆行驶信号进行分析和处理,判断车辆的停车姿态,从而确定车辆的停车姿态是否异常。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. During the driving process of the vehicle, obtain the vehicle driving signal of each unit time in the time range of t11-t, analyze and process the vehicle driving signal of each unit time, and judge the parking posture of the vehicle, so as to determine whether the parking posture of the vehicle is not. abnormal.
如此,能够将当前时间的车辆停车姿态与当前时间以前的车辆停车姿态进行比较分析,准确判断当前时间车辆的停车姿态,确保分析陷坑事故结果的准确性。In this way, the parking posture of the vehicle at the current time and the parking posture of the vehicle before 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 analyzing the result of the pit accident can be ensured.
在另一些实施例中,第一时间段可以是当前时间t以前预定时间t11至当前时间t以后预定时间t12的时间范围。在车辆行驶过程中,获取t11-t12的时间范围内每个单位时间的车辆行驶信号,对每个单位时间的车辆行驶信号进行分析和处理,判断车辆的停车姿态,从而确定车辆的停车姿态是否异常。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. During the driving process of the vehicle, obtain the vehicle driving signal of each unit time in the time range of t11-t12, analyze and process the vehicle driving signal of each unit time, and judge the parking posture of the vehicle, so as to determine whether the parking posture of the vehicle is not. abnormal.
如此,能够将当前时间的车辆停车姿态、当前时间以前的车辆停车姿态和当前时间以后的车辆停车姿态进行比较分析,准确判断当前时间车辆的停车姿态,确保分析陷坑事故结果的准确性。In this way, the vehicle parking attitude at the current time, the vehicle parking attitude before the current time, and the vehicle parking attitude after the current time can be compared and analyzed, the vehicle parking attitude at the current time can be accurately judged, and the accuracy of the analysis result of the sinkhole accident can be ensured.
需要说明地,第一时间段的时间范围长短可以根据路况、车辆使用年限、车辆维修记录、车 辆性能等因素设定,具体不做限定,例如可以是3秒、5秒、10秒、13秒、17秒等。相应地,当前时间t以前的预定时间t11和当前时间t以后的预定时间t12可以根据路况、车辆使用年限、车辆维修记录、车辆性能等因素设定,具体不做限定,例如可以是1秒、3秒、5秒、8秒、10秒等,t11和t12的具体数值可以相等,也可以不相等。It should be noted that the length of the time range of the first time period can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc., and there is no specific limitation, for example, it can be 3 seconds, 5 seconds, 10 seconds, 13 seconds , 17 seconds, etc. Correspondingly, the predetermined time t11 before the current time t and the predetermined time t12 after the current time t can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc. 3 seconds, 5 seconds, 8 seconds, 10 seconds, etc., the specific values of t11 and t12 may be equal or unequal.
请参阅图4,在某些实施方式中,S12包括:Referring to FIG. 4, in some embodiments, S12 includes:
S121:根据预存储的停车姿态模型对车辆行驶信号进行识别以判断车辆的停车姿态从而确定停车姿态是否异常。S121 : Identify the vehicle driving signal according to the pre-stored parking attitude model to determine the parking attitude of the vehicle to determine whether the parking attitude is abnormal.
在某些实施方式中,S121可以由判断模块114实现。或者说,判断模块114用于根据预存储的停车姿态模型对车辆行驶信号进行识别以判断车辆的停车姿态从而确定停车姿态是否异常。In some embodiments, S121 may be implemented by the judgment module 114 . In other words, the judging module 114 is configured to identify the driving signal of the vehicle according to the pre-stored parking posture model to judge the parking posture of the vehicle to determine whether the parking posture is abnormal.
在某些实施方式中,处理器104用于根据预存储的停车姿态模型对车辆行驶信号进行识别以判断车辆的停车姿态从而确定停车姿态是否异常。In some embodiments, the processor 104 is configured to identify the vehicle driving signal according to the pre-stored parking posture model to judge the parking posture of the vehicle and determine whether the parking posture is abnormal.
具体地,可根据预存储的停车姿态模型对车辆行驶信号进行识别,判断出车辆的停车姿态,从而确定停车姿态是否异常。停车姿态模型的具体处理方法可以根据数据类别、数据量大小等因素选定,具体不做限定,例如可以是梯度提升树(Gradient Boosting Decision Tree,GBDT)算法,也可以是支持向量机(Support Vector Machine,SVM)算法,还可以是回归算法等。Specifically, the vehicle driving signal can be recognized according to the pre-stored parking attitude model, and the parking attitude of the vehicle can be determined, thereby determining whether the parking attitude is abnormal. The specific processing method of the parking attitude model can be selected according to the data category, data volume and other factors, which is not limited. For example, it can be a gradient boosting tree (Gradient Boosting Decision Tree, GBDT) algorithm or a support vector machine (Support Vector Machine, SVM) algorithm, it can also be a regression algorithm, etc.
将对车辆行驶信号的识别过程以模型的形式进行开发和存储,如此,能够提高监控装置110的开发效率。在后续使用过程中出现故障,也便于查看和修改。完成模型开发后,基于模型复用率高的特点,停车姿态模型能够适用于多种车型和/或系统,减少开发成本。The identification process of the vehicle running signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use. After the model development is completed, based on the high model reuse rate, the parking attitude model can be applied to a variety of vehicle models and/or systems, reducing development costs.
请参阅图5,在某些实施方式中,车辆行驶信号包括车辆的实时三轴加速度信号和车辆各个车轮的速度信号,S121包括:Referring to FIG. 5 , in some embodiments, the vehicle driving signal includes the real-time three-axis acceleration signal of the vehicle and the speed signal of each wheel of the vehicle, and S121 includes:
S1211:根据速度信号判断车辆是否处于停车状态;S1211: Determine whether the vehicle is in a parked state according to the speed signal;
S1212:在车辆处于停车状态时,根据三轴加速度信号确定车辆在第一时间段内每个单位时间的姿态角;S1212: When the vehicle is in a parked state, determine the attitude angle of the vehicle per unit time in the first time period according to the three-axis acceleration signal;
S1213:根据停车姿态模型对姿态角进行处理以得到车辆的停车姿态。S1213: Process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
在某些实施方式中,S1211-S1213可以由判断模块114实现。或者说,判断模块114用于根据速度信号判断车辆是否处于停车状态,及用于在车辆处于停车状态时,根据三轴加速度信号确定车辆在第一时间段内每个单位时间的姿态角,以及用于根据停车姿态模型对姿态角进行处理以得到车辆的停车姿态。In some embodiments, S1211-S1213 may be implemented by the determination module 114 . In other words, the determination module 114 is configured to determine whether the vehicle is in a parked state according to the speed signal, and to determine the attitude angle of the vehicle per unit time in the first time period according to the three-axis acceleration signal when the vehicle is in a parked state, and It is used to process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
在某些实施方式中,处理器104用于根据速度信号判断车辆是否处于停车状态,及用于在车辆处于停车状态时,根据三轴加速度信号确定车辆在第一时间段内每个单位时间的姿态角,以及用于根据停车姿态模型对姿态角进行处理以得到车辆的停车姿态。In some embodiments, the processor 104 is configured to determine whether the vehicle is in a parked state according to the speed signal, and to determine the speed of the vehicle per unit time in the first time period according to the three-axis acceleration signal when the vehicle is in a parked state. attitude angle, and is used to process the attitude angle according to the parking attitude model to obtain the parking attitude of the vehicle.
具体地,可以根据车辆各个车轮的速度信号判断车辆是否处于停车状态。其中,速度信号可 以是采集驱动轮的速度信号,也可以是采集所有正常轮胎的速度信号。Specifically, whether the vehicle is in a parked state can be determined according to the speed signals of each wheel of the vehicle. Among them, the speed signal can be the speed signal collected from the driving wheel, or the speed signal collected from all normal tires.
在一些实施例中,车辆为前驱车,则可采集左前车轮的和右前车轮的速度信号,也可以采集所有正常使用的车轮的速度信号。在一些实施例中,车辆为后驱车,则可采集左后车轮的和右前车轮的速度信号,也可以采集所有正常使用的车轮的速度信号。在另一些实施例中,车辆为四驱车,则可采集所有正常使用的车轮,包括从动轮和驱动轮的速度信号。In some embodiments, if the vehicle is a front-wheel drive vehicle, the speed signals of the left front wheel and the right front wheel may be collected, and the speed signals of all the wheels in normal use may also be collected. In some embodiments, if the vehicle is a rear-wheel-drive vehicle, the speed signals of the left rear wheel and the right front wheel may be collected, and the speed signals of all the wheels in normal use may also be collected. In other embodiments, if the vehicle is a four-wheel drive vehicle, the speed signals of all the wheels in normal use, including the driven wheels and the driving wheels, can be collected.
如此,通过识别车辆各个车辆的速度信号,能够较准确地反映车辆是否处于停车状态,保证信号的准确性。In this way, by identifying the speed signals of each vehicle in the vehicle, it can more accurately reflect whether the vehicle is in a parked state, thereby ensuring the accuracy of the signals.
此外,还可以根据全球定位系统(Global Positioning System,GPS)判断车辆是否处于停车状态。在GPS显示车辆位置发生移动时,可以认为车辆未处于停车状态。在GPS显示车辆位置停止不变时,可以认为车辆处于停车状态。In addition, it can also be judged whether the vehicle is in a parked state according to the Global Positioning System (GPS). When the GPS shows that the vehicle position moves, it can be considered that the vehicle is not in a parked state. When the GPS shows that the position of the vehicle remains unchanged, it can be considered that the vehicle is in a parked state.
在判断车辆处于停车状态时,获取车辆在第一时间段内每个单位时间的三轴加速度信号,根据三轴加速度信号可以确定车辆的姿态角,并使用停车姿态模型对姿态角进行处理,得到车辆的停车状态。请参阅图6,具体而言,三轴加速度信号包括x轴的俯仰角信号、y轴的偏航角信号和z轴的翻滚角信号。其中,俯仰角pitch可以表示车辆坐标系x轴与水平面的夹角,偏航角yaw可以表示车辆坐标系y轴与水平面的夹角,翻滚角roll可以表示车辆坐标系z轴与水平面的夹角。When it is judged that the vehicle is in a parked state, the three-axis acceleration signal of the vehicle per unit time in the first period of time is obtained, the attitude angle of the vehicle can be determined according to the three-axis acceleration signal, and the attitude angle is processed by using the parking attitude model to obtain The parking status of the vehicle. Referring to FIG. 6 , specifically, the three-axis acceleration signal includes a pitch angle signal of the x-axis, a yaw angle signal of the y-axis, and a roll angle signal of the z-axis. Among them, the pitch angle pitch can represent the angle between the x-axis of the vehicle coordinate system and the horizontal plane, the yaw angle yaw can represent the angle between the y-axis of the vehicle coordinate system and the horizontal plane, and the roll angle roll can represent the vehicle coordinate system. The angle between the z-axis and the horizontal plane .
在一些实施例中,可以采用LIS3DH三轴加速度计测出偏航角yaw和翻滚角roll。根据俯仰角的计算公式pitch=arctan(-y,z)*180/3.14159和翻滚角的计算公式roll=arctan(x,z)*180/3.14159,可以得到车辆在第一时间段内每个单位时间的偏航角yaw和翻滚角roll。In some embodiments, the yaw angle yaw and the roll angle roll can be measured using the LIS3DH three-axis accelerometer. According to the calculation formula of pitch angle pitch=arctan(-y,z)*180/3.14159 and the calculation formula of roll angle roll=arctan(x,z)*180/3.14159, each unit of the vehicle in the first time period can be obtained Time yaw angle yaw and roll angle roll.
在判断车辆处于停车状态时,获取车辆在第一时间段内每个单位时间的姿态角,将获取到的各个姿态角输入停车姿态模型,由停车姿态模型对姿态角进行处理,输出车辆是否发生前倾、后倾、左倾、右倾或车辆停车状态无异常的概率。When judging that the vehicle is in a parked state, the attitude angle of the vehicle per unit time in the first time period is obtained, and each obtained attitude angle is input into the parking attitude model, and the attitude angle is processed by the parking attitude model to output whether the vehicle has occurred. There is no abnormal probability of leaning forward, leaning backward, leaning left, leaning right, or the vehicle is parked.
例如,停车姿态模型对输入的俯仰角进行处理后,得到车辆的停车状态为:车辆发生前倾的概率为0.2,车辆发生后倾的概率为0.9,无异常的概率为0.1。则可根据上述结果中的最大值,判断车辆在当前时间发生后倾。For example, after the parking attitude model processes the input pitch angle, the parking state of the vehicle is obtained: the probability of the vehicle tilting forward is 0.2, the probability of the vehicle tilting backward is 0.9, and the probability of no abnormality is 0.1. Then, according to the maximum value of the above results, it can be determined that the vehicle is tilted backwards at the current time.
又如,停车姿态模型对输入的翻滚角进行处理后,得到车辆的停车状态为:车辆发生左倾的概率为0.7,车辆发生右倾的概率为0.4,无异常的概率为0.1。则可根据上述结果中的最大值,判断车辆在当前时间发生左倾。For another example, after the parking attitude model processes the input roll angle, the parking state of the vehicle is obtained: the probability of the vehicle leaning to the left is 0.7, the probability of the vehicle leaning right is 0.4, and the probability of no abnormality is 0.1. Then, according to the maximum value of the above results, it can be determined that the vehicle leans to the left at the current time.
如此,能够将车辆在第一时间段内每个单位时间的姿态角进行比较分析,准确判断当前时间车辆的停车姿态。In this way, the attitude angle of the vehicle per 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 determined.
请参阅图7,在某些实施方式中,S13包括:Referring to Figure 7, in some embodiments, S13 includes:
S131:在停车姿态异常的情况下,获取当前时间前第二预定时间至当前时间后第三预定时间的第二时间段内每个单位时间的驾驶员行为信号;S131: In the case of abnormal parking posture, obtain the driver behavior signal of each unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time;
S132:获取第二时间段内每个单位时间的车辆故障信号。S132: Acquire a vehicle fault signal per unit time in the second time period.
在某些实施方式中,S131和S132可以由获取模块112实现。或者说,获取模块112用于在停车姿态异常的情况下,获取当前时间前第二预定时间至当前时间后第三预定时间的第二时间段内每个单位时间的驾驶员行为信号,以及用于获取第二时间段内每个单位时间的车辆故障信号。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 the second time period from the second predetermined time before the current time to the third predetermined time after the current time in the case of abnormal parking posture, and use in acquiring the vehicle fault signal per unit time in the second time period.
在某些实施方式中,处理器104用于在停车姿态异常的情况下,获取当前时间前第二预定时间至当前时间后第三预定时间的第二时间段内每个单位时间的驾驶员行为信号,以及用于获取第二时间段内每个单位时间的车辆故障信号。In some embodiments, the processor 104 is configured to obtain the driver behavior per unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time when the parking posture is abnormal signal, and a vehicle fault signal for each unit time in the second time period.
具体地,在判断出停车姿态异常的情况下,获取第二时间段内每个单位时间的驾驶员行为信号和车辆故障信号,根据停车姿态、驾驶员行为信号和车辆故障信号,判断车辆是否发生陷坑事故。其中,第二时间段可以是当前时间t以前的预定时间t21至当前时间t的时间范围,也可以是当前时间t以前第二预定时间t21至当前时间t以后预定时间t22的时间范围。Specifically, in the case of judging that the parking posture is abnormal, the driver behavior signal and the vehicle fault signal of each unit time in the second time period are obtained, and according to the parking posture, the driver behavior signal and the vehicle fault signal, it is judged whether the vehicle has occurred. sinkhole accident. 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.
在一些实施例中,第二时间段可以是当前时间t以前的预定时间t21至当前时间t的时间范围。在判断出停车姿态异常的情况下,获取t21-t的时间范围内每个单位时间的驾驶员行为信号和车辆故障信号,对每个单位时间的驾驶员行为信号和车辆故障信号进行分析和处理,从而判断车辆是否发生陷坑事故。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. In the case of judging that the parking posture is abnormal, obtain the driver behavior signal and vehicle fault signal per unit time within the time range of t21-t, and analyze and process the driver behavior signal and vehicle fault signal per unit time , so as to determine whether the vehicle has a sinkhole accident.
如此,能够将当前时间的驾驶员行为与当前时间以前的驾驶员行为进行比较分析,准确判断当前时间驾驶员行为是否异常,以及将当前时间的车辆故障信号与当前时间以前的车辆故障信号进行比较分析,准确判断当前时间车辆是否发生故障,确保分析陷坑事故结果的准确性。In this way, it is possible to compare and analyze the driver's behavior at the current time and the driver's behavior before the current time, accurately determine whether the driver's behavior at the current time is abnormal, and compare the vehicle fault signal at the current time with the vehicle fault signal before the current time. Analysis, accurately determine whether the vehicle is faulty at the current time, and ensure the accuracy of the results of the analysis of the sinkhole accident.
在另一些实施例中,第二时间段可以是当前时间t以前第二预定时间t21至当前时间t以后预定时间t22的时间范围。在判断出停车姿态异常的情况下,获取t21-t22的时间范围内每个单位时间的驾驶员行为信号和车辆故障信号,对每个单位时间的驾驶员行为信号和车辆故障信号进行分析和处理,从而判断车辆是否发生陷坑事故。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. In the case of judging that the parking posture is abnormal, obtain the driver behavior signal and vehicle fault signal per unit time within the time range of t21-t22, and analyze and process the driver behavior signal and vehicle fault signal per unit time , so as to determine whether the vehicle has a sinkhole accident.
如此,能够将当前时间以前、当前时间和当前时间以后的驾驶员行为进行比较分析,准确判断当前时间驾驶员行为是否异常,以及将当前时间以前、当前时间和当前时间以后的车辆故障信号进行比较分析,准确判断当前时间车辆是否发生故障,确保分析陷坑事故结果的准确性。In this way, it is possible to compare and analyze the driver's behavior before the current time, the current time and after the current time, accurately determine whether the driver's behavior at the current time is abnormal, and compare the vehicle fault signals before the current time, the current time and after the current time. Analysis, accurately determine whether the vehicle is faulty at the current time, and ensure the accuracy of the results of the analysis of the sinkhole accident.
需要说明地,第二时间段的时间范围长短可以根据路况、车辆使用年限、车辆维修记录、车辆性能等因素设定,具体不做限定,例如可以是10秒、30秒、50秒、60秒、100秒等。当前时间t以前第二预定时间t21和当前时间t以后第三预定时间t22可以根据路况、车辆使用年限、车辆维修记录、车辆性能等因素设定,具体不做限定,例如可以是5秒、10秒、30秒、50秒、80秒等,t21和t22的具体数值可以相等,也可以不相等。It should be noted that the length of the time range of the second time period can be set according to factors such as road conditions, vehicle service life, vehicle maintenance records, vehicle performance, etc., and is not specifically limited, for example, it can be 10 seconds, 30 seconds, 50 seconds, 60 seconds , 100 seconds, etc. The second predetermined time t21 before the current time t and the third predetermined time t22 after the current time t can be set according to factors such as road conditions, vehicle age, vehicle maintenance records, vehicle performance, etc. Seconds, 30 seconds, 50 seconds, 80 seconds, etc., the specific values of t21 and t22 may be equal or unequal.
请参阅图8,在某些实施方式中,S14包括:Referring to FIG. 8, in some embodiments, S14 includes:
S141:根据预存储的驾驶员异常行为模型对驾驶员行为信号进行识别以判断驾驶员行为异常 等级。S141: Identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
在某些实施方式中,S141可以由判断模块114实现。或者说,判断模块114用于根据预存储的驾驶员异常行为模型对驾驶员行为信号进行识别以判断驾驶员行为异常等级。In some embodiments, S141 may be implemented by the judgment module 114 . In other words, the judging module 114 is configured to identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to judge the abnormal level of the driver's behavior.
在某些实施方式中,处理器104用于根据预存储的驾驶员异常行为模型对驾驶员行为信号进行识别以判断驾驶员行为异常等级。In some embodiments, the processor 104 is configured to identify the driver's behavior signal according to the pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
具体地,可根据预存储的驾驶员异常行为模型对驾驶员行为信号进行识别,判断出驾驶员行为是否异常,并评定驾驶员行为的异常等级,从而确定车辆是否发生陷坑事故。驾驶员异常行为模型的具体处理方法可以根据数据类别、数据量大小等因素选定,具体不做限定,例如可以是计数模型,可以是GBDT算法,可以是SVM算法,还可以是回归算法等。Specifically, the driver's behavior signal can be identified according to the pre-stored abnormal driver behavior model, to determine whether the driver's behavior is abnormal, and to evaluate the abnormal level of the driver's behavior, so as to determine whether the vehicle has a sinkhole accident. The specific processing method of the driver's abnormal behavior model can be selected according to the data type, data volume and other factors, and there is no specific limitation. For example, it can be a counting model, a GBDT algorithm, an SVM algorithm, or a regression algorithm.
将对驾驶员行为信号的识别过程以模型的形式进行开发和存储,如此,能够提高监控装置110的开发效率。在后续使用过程中出现故障,也便于查看和修改。完成模型开发后,基于模型复用率高的特点,驾驶员异常行为模型能够适用于多种车型和/或系统,减少开发成本。The identification process of the driver's behavior signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use. After the model development is completed, based on the characteristics of high model reuse rate, the abnormal driver behavior model can be applied to a variety of vehicle models and/or systems, reducing development costs.
请参阅图9,在某些实施方式中,驾驶员行为信号包括车辆上锁信号、双闪灯信号、挡位信号、加速踏板信号、主驾车门信号和方向盘转角信号,S141包括:Referring to FIG. 9 , in some embodiments, the driver behavior signal includes a vehicle lock 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 S141 includes:
S1411:根据主驾车门信号和车辆上锁信号判断是否存在下车后预定时长内未锁车的第一异常行为;S1411: Determine whether there is a first abnormal behavior of unlocking the car within a predetermined period of time after getting off the car according to the main driving door signal and the vehicle locking signal;
S1412:根据挡位信号和双闪灯信号判断是否存在开启双闪灯的第二异常行为;S1412: Determine whether there is a second abnormal behavior of turning on the double flashing light according to the gear signal and the double flashing light signal;
S1413:根据加速踏板信号判断是否存在猛踩加速踏板的第三异常行为;S1413: Determine whether there is a third abnormal behavior of slamming the accelerator pedal according to the accelerator pedal signal;
S1414:根据方向盘转角信号判断是否存在反复操作方向盘的第四异常行为;S1414: Determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel according to the steering wheel angle signal;
S1415:根据主驾车门信号和方向盘转角信号判断是否存在下车后方向盘未回正的第五异常行为;S1415: According to the main driving door signal and the steering wheel angle signal, determine whether there is a fifth abnormal behavior of the steering wheel not returning to the right position after getting off the car;
S1416:根据预设定的评级规则、第一异常行为、第二异常行为、第三异常行为、第四异常行为和第五异常行为判断驾驶员行为异常等级。S1416: Determine the abnormal level of the driver's behavior 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.
在某些实施方式中,S1411-S1416可以由判断模块114实现。或者说,判断模块114用于根据主驾车门信号和车辆上锁信号判断是否存在下车后预定时长内未锁车的第一异常行为,及用于根据挡位信号和双闪灯信号判断是否存在开启双闪灯的第二异常行为,及用于根据加速踏板信号判断是否存在猛踩加速踏板的第三异常行为,及用于根据方向盘转角信号判断是否存在反复操作方向盘的第四异常行为,及用于根据主驾车门信号和方向盘转角信号判断是否存在下车后方向盘未回正的第五异常行为,以及用于根据预设定的评级规则、第一异常行为、第二异常行为、第三异常行为、第四异常行为和第五异常行为判断驾驶员行为异常等级。In some embodiments, S1411-S1416 may be implemented by the determination module 114 . In other words, the judging module 114 is used for judging whether there is a first abnormal behavior of not locking the car within a predetermined period of time after getting off the car according to the main driving door signal and the vehicle locking signal, and for judging whether it is not according to the gear signal and the double flashing light signal. There is a second abnormal behavior of turning on the double flashing lights, and a third abnormal behavior for judging whether there is a slam on the accelerator pedal according to the accelerator pedal signal, and a fourth abnormal behavior for judging whether there is a repeated steering wheel operation according to the steering wheel angle signal, And it is used to judge whether there is a fifth abnormal behavior in which the steering wheel is not returned after getting off the car according to the main driving door signal and the steering wheel angle signal, and is used to judge whether there is a fifth abnormal behavior according to the preset rating rules, the first abnormal behavior, the second abnormal behavior, the first abnormal behavior, the first abnormal behavior The third abnormal behavior, the fourth abnormal behavior and the fifth abnormal behavior determine the abnormal level of the driver's behavior.
在某些实施方式中,处理器104用于根据主驾车门信号和车辆上锁信号判断是否存在下车后预定时长内未锁车的第一异常行为,及用于根据挡位信号和双闪灯信号判断是否存在开启双闪灯 的第二异常行为,及用于根据加速踏板信号判断是否存在猛踩加速踏板的第三异常行为,及用于根据方向盘转角信号判断是否存在反复操作方向盘的第四异常行为,及用于根据主驾车门信号和方向盘转角信号判断是否存在下车后方向盘未回正的第五异常行为,以及用于根据预设定的评级规则、第一异常行为、第二异常行为、第三异常行为、第四异常行为和第五异常行为判断驾驶员行为异常等级。In some embodiments, the processor 104 is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle according to the main driving door signal and the vehicle locking signal, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to determine whether there is a first abnormal behavior in which the vehicle is not locked within a predetermined period of time after getting off the vehicle, and is configured to use the gear signal and the double flashing The light signal judges whether there is a second abnormal behavior of turning on the double flashing lights, and the third abnormal behavior for judging whether there is a hard pressing of the accelerator pedal according to the accelerator pedal signal, and the third abnormal behavior for judging whether the steering wheel is repeatedly operated according to the steering wheel angle signal. Four abnormal behaviors, and a fifth abnormal behavior for judging whether there is a steering wheel not returning after getting off the car according to the main driving door signal and the steering wheel angle signal, and a fifth abnormal behavior for judging whether the steering wheel does not return after getting off the car, and for according to the preset rating rules, the first abnormal behavior, the second abnormal behavior The abnormal behavior, the third abnormal behavior, the fourth abnormal behavior, and the fifth abnormal behavior determine the abnormal level of the driver's behavior.
具体地,根据主驾车门信号和车辆上锁信号,判断驾驶员在第二时间段内是否存在下车后预定时长内未锁车的第一异常行为X1。其中,主驾车门打开或关闭后预定时间内,车辆上锁信号为0,即车辆未上锁,此时可以判断驾驶员在第二时间段内存在第一异常行为X1。Specifically, according to the main driving door signal and the vehicle locking signal, it is determined whether the driver has a first abnormal behavior X1 in which the vehicle is not locked within a predetermined period of time after getting off the vehicle within the second time period. Wherein, within a predetermined time after the main driving door is opened or closed, the vehicle lock signal is 0, that is, the vehicle is not locked. At this time, it can be determined that the driver has the first abnormal behavior X1 in the second time period.
在存在第一异常行为X1的情况下,驾驶员行为异常等级置1,在不存在第一异常行为X1的情况下,驾驶员行为异常等级为0。When the first abnormal behavior X1 exists, the abnormal level of driver behavior is set to 1, and when the first abnormal behavior X1 does not exist, the abnormal level of driver behavior is set to 0.
在第二时间段内,驾驶员存在第一异常行为X1的情况下,根据挡位信号和双闪灯信号,判断驾驶员在第二时间段内是否存在开启双闪灯的第二异常行为X2。其中,车辆当前挡位为停车挡,双闪灯信号为1,即双闪灯为发光状态,此时可以判断驾驶员在第二时间段存在第二异常行为X2。In the second time period, when the driver has the first abnormal behavior X1, according to the gear signal and the double flashing light signal, it is judged whether the driver has the second abnormal behavior X2 of turning on the double flashing light in the second time period . Wherein, the current gear of the vehicle is the parking gear, and the double flashing light signal is 1, that is, the double flashing light is in a light-on state. At this time, it can be determined that the driver has a second abnormal behavior X2 in the second time period.
在存在第一异常行为X1和第二异常行为X2的情况下,驾驶员行为异常等级为2。在存在第一异常行为X1,但不存在第二异常行为X2的情况下,驾驶员行为异常等级仍为1。When the first abnormal behavior X1 and the second abnormal behavior X2 exist, the abnormal level of the driver's behavior is 2. In the case where the first abnormal behavior X1 exists but the second abnormal behavior X2 does not exist, the abnormal level of the driver's behavior is still 1.
在第二时间段内,在判断驾驶员在第二时间段内存在第一异常行为X1的情况下,根据加速踏板信号、主驾车门信号和方向盘转角信号,判断驾驶员在第二时间段内是否存在猛踩加速踏板的第三异常行为X3、反复操作方向盘的第四异常行为X4或下车后方向盘未回正的第五异常行为X5。其中,计算第二时间段内加速踏板被踩踏的深度超过预定深度阈值的次数a,在a值大于或等于第一预定次数阈值时,则判断驾驶员存在第三异常行为X3。计算当前时间的方向盘转角与当前时间以前的预定时间的方向盘转角的差值大于预定差值阈值的次数b,在b值大于或等于第二预定次数阈值时,则判断驾驶员存在第四异常行为X4。在主驾车门打开后的第二时间段内,方向盘转角的度数超过预定角度阈值,则判断驾驶员存在第五异常行为X5。In the second time period, when it is determined that the driver has the first abnormal behavior X1 in the second time period, according to the accelerator pedal signal, the main driving door signal and the steering wheel angle signal, it is determined that the driver is in the second time period. Whether there is a third abnormal behavior X3 of pressing the accelerator pedal hard, a fourth abnormal behavior X4 of repeatedly operating the steering wheel, or a fifth abnormal behavior X5 of the steering wheel not returning to the right position after getting off the car. Among them, the number of times a that the depth of the accelerator pedal is stepped over the predetermined depth threshold in the second time period is calculated, and when the value of a is greater than or equal to the first predetermined number of times threshold, it is determined that the driver has a third abnormal behavior X3. Calculate the number of times b that the difference between the steering wheel angle at the current time and the steering wheel angle at a predetermined time before the current time is greater than the predetermined difference threshold. When the value of b is greater than or equal to the second predetermined number of times threshold, it is determined that the driver has a fourth abnormal behavior X4. In the second time period after the main driving door is opened, if the degree of the steering wheel angle exceeds the predetermined angle threshold, it is determined that the driver has a fifth abnormal behavior X5.
在存在第一异常行为X1的情况下,驾驶员存在第三异常行为X3、第四异常行为X4和/或第五异常行为X5中的两种或两种以上异常行为时,驾驶员行为异常等级为2。在存在第一异常行为X1,但不存在第三异常行为X3、第四异常行为X4和第五异常行为X5,或存在的第三异常行为X3、第四异常行为X4或第五异常行为X5的种类小于两种的情况下,驾驶员行为异常等级仍为1。In the presence of the first abnormal behavior X1, when the driver has two or more abnormal behaviors among the third abnormal behavior X3, the fourth abnormal behavior X4 and/or the fifth abnormal behavior X5, the abnormal behavior level of the driver is 2. In the presence of the first abnormal behavior X1, but not the third abnormal behavior X3, the fourth abnormal behavior X4 and the fifth abnormal behavior X5, or the existence of the third abnormal behavior X3, the fourth abnormal behavior X4 or the fifth abnormal behavior X5 In the case of less than two types, the abnormal driver behavior level is still 1.
进一步地,在第二时间段内,驾驶员存在第一异常行为X1和第二异常行为X2的情况下,存在第三异常行为X3、第四异常行为X4和/或第五异常行为X5中的两种或两种以上异常行为,驾驶员行为异常等级为3。Further, in the second time period, when the driver has the first abnormal behavior X1 and the second abnormal behavior X2, there is a third abnormal behavior X3, the fourth abnormal behavior X4 and/or the fifth abnormal behavior X5. Two or more abnormal behaviors, the driver behavior abnormal level is 3.
例如,在第二时间段内,驾驶员存在X3、X4和X5的异常行为,则驾驶员行为异常等级为0。存在X1,则驾驶员行为异常等级为1。存在X1和X2,则驾驶员行为异常等级为2。存在X1、X4 和X5,则驾驶员行为异常等级为2。存在X1、X2和X5,则驾驶员行为异常等级为2。存在X1、X3、X4和X5,则驾驶员行为异常等级为2。存在X1、X2、X3、X4和X5,则驾驶员行为异常等级为3。以此类推。For example, in the second time period, if the driver has abnormal behaviors of X3, X4 and X5, the abnormal level of the driver's behavior is 0. If X1 exists, the abnormal level of driver behavior is 1. If X1 and X2 exist, the abnormal level of driver behavior is 2. If X1, X4 and X5 exist, the abnormal level of driver behavior is 2. If X1, X2 and X5 exist, the abnormal level of driver behavior is 2. If there are X1, X3, X4 and X5, the abnormal level of driver behavior is 2. If there are X1, X2, X3, X4 and X5, the abnormal level of driver behavior is 3. And so on.
如此,能够将驾驶员在第二时间段内的行为进行比较分析,准确判断驾驶员行为的异常等级,确保分析陷坑事故结果的准确性。In this way, the behavior of the driver in the second time period can be compared and analyzed, the abnormal level of the driver's behavior can be accurately judged, and the accuracy of analyzing the result of the sinkhole accident can be ensured.
需要说明地,上述预定深度阈值、第一预定次数阈值、预定差值阈值、第二预定次数阈值、预定角度阈值等阈值,均可根据车型和驾驶员行为习惯等参数设定,具体不做限定,例如预定深度阈值可以是10、15、18等,第一预定次数阈值和第二预定次数阈值可以是1次、2次、5次等,预定差值阈值可以是50、80、100等,预定角度阈值可以是250度、300度、350度、400度等。It should be noted that the above-mentioned thresholds such as the predetermined depth threshold, the first predetermined number of times threshold, the predetermined difference threshold, the second predetermined number of times threshold, and the predetermined angle threshold can all be set according to parameters such as the vehicle model and the driver's behavior habits, and are not specifically limited. For example, the predetermined depth threshold may be 10, 15, 18, etc., the first predetermined number of times threshold and the second predetermined number of times threshold may be 1, 2, 5, etc., and the predetermined difference threshold may be 50, 80, 100, etc., The predetermined angle threshold may be 250 degrees, 300 degrees, 350 degrees, 400 degrees, or the like.
请参阅图10,在某些实施方式中,S14包括:Referring to FIG. 10, in some embodiments, S14 includes:
S142:根据预存储的车辆陷坑模型对停车姿态、驾驶员行为异常等级以及车辆故障信号进行识别以判断车辆是否发生陷坑事故。S142: Identify the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal according to the pre-stored vehicle sinkhole model to determine whether the vehicle has a sinkhole accident.
在某些实施方式中,S142可以由判断模块114实现。或者说,判断模块114用于根据预存储的车辆陷坑模型对停车姿态、驾驶员行为异常等级以及车辆故障信号进行识别以判断车辆是否发生陷坑事故。In some embodiments, S142 may be implemented by the determination module 114 . In other words, the judging module 114 is configured to identify the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal according to the pre-stored vehicle sinkhole model, so as to judge whether the vehicle has a sinkhole accident.
在某些实施方式中,处理器104用于根据预存储的车辆陷坑模型对停车姿态、驾驶员行为异常等级以及车辆故障信号进行识别以判断车辆是否发生陷坑事故。In some embodiments, the processor 104 is configured to identify the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model, so as to determine whether the vehicle has a sinkhole accident.
具体地,请参阅图11,可根据预存储的车辆陷坑模型对停车姿态、驾驶员行为异常等级以及车辆故障信号进行识别,判断车辆是否发生陷坑事故。车辆陷坑模型的具体处理方法可以根据数据类别、数据量大小等因素选定,具体不做限定,例如可以是计数模型,可以是GBDT算法,可以是SVM算法,还可以是回归算法等。Specifically, referring to FIG. 11 , the parking posture, the abnormal level of driver behavior and the vehicle fault signal can be identified according to the pre-stored vehicle sinkhole model to determine whether the vehicle has a sinkhole accident. The specific processing method of the vehicle pit model can be selected according to the data type, data volume and other factors, and there is no specific limitation. For example, it can be a counting model, a GBDT algorithm, an SVM algorithm, or a regression algorithm.
将对停车姿态、驾驶员行为异常等级以及车辆故障信号的识别过程以模型的形式进行开发和存储,如此,能够提高监控装置110的开发效率。在后续使用过程中出现故障,也便于查看和修改。完成模型开发后,基于模型复用率高的特点,车辆陷坑模型能够适用于多种车型和/或系统,减少开发成本。The identification process of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal is developed and stored in the form of a model, so that the development efficiency of the monitoring device 110 can be improved. It is also easy to view and modify in case of failure during subsequent use. After the model development is completed, based on the high model reuse rate, the vehicle sinkhole model can be applied to a variety of vehicle models and/or systems, reducing development costs.
请参阅图12,在某些实施方式中,车辆故障信号包括打滑信号、底盘故障信号、胎压监控系统故障信号和电气系统故障信号,S142包括:Referring to FIG. 12 , in some embodiments, the vehicle fault signal includes a slip signal, a chassis fault signal, a tire pressure monitoring system fault signal and an electrical system fault signal, and S142 includes:
S1421:根据预存储的车辆陷坑模型对由停车姿态、驾驶员行为异常等级以及车辆故障信号构成的特征向量进行处理以得到车辆发生陷坑事故的概率;S1421: Process the feature vector formed by the parking posture, the abnormal level of driver behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability of a vehicle sinkhole accident;
S1422:在概率大于预定阈值时确定车辆发生陷坑事故。S1422: When the probability is greater than a predetermined threshold, it is determined that the vehicle has a sinkhole accident.
在某些实施方式中,S1421和S1422可以由判断模块114实现。或者说,判断模块114用于根据预存储的车辆陷坑模型对由停车姿态、驾驶员行为异常等级以及车辆故障信号构成的特征向量 进行处理以得到车辆发生陷坑事故的概率,以及用于在概率大于预定阈值时确定车辆发生陷坑事故。In some embodiments, S1421 and S1422 may be implemented by the judgment module 114 . In other words, the judging module 114 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability that the vehicle has a sinkhole accident, and is used to obtain the probability that the vehicle has a sinkhole accident when the probability is greater than It is determined that the vehicle has a sinkhole accident when the predetermined threshold value is used.
在某些实施方式中,处理器104用于根据预存储的车辆陷坑模型对由停车姿态、驾驶员行为异常等级以及车辆故障信号构成的特征向量进行处理以得到车辆发生陷坑事故的概率,以及用于在概率大于预定阈值时确定车辆发生陷坑事故。In some embodiments, the processor 104 is configured to process the feature vector composed of the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal according to the pre-stored vehicle sinkhole model to obtain the probability of the vehicle having a sinkhole accident, and use It is determined that the vehicle has a pit accident when the probability is greater than a predetermined threshold.
具体地,车辆故障信号包括车轮打滑信号、底盘故障信号、胎压监控系统故障信号和电气系统故障信号。其中,车轮打滑信号可以通过计算前轮与后轮的速度差进行检测,在二者差值大于预定速度差值阈值时,可认为发生车轮打滑现象。底盘故障信号和胎压监控系统故障信号可以通过车辆的防抱死刹车系统、电子稳定程序、自动驻车系统和/或胎压监控系统等车辆系统进行检测,在检测到底盘故障信号和胎压监控系统故障信号其中任一或多个信号大于0时,可认为车辆底盘和/或车轮发生故障。电气系统故障信号可以通过电机系统、电池管理系统、车灯控制系统、雷达系统等车辆系统进行检测,在检测到电气系统中任一或多个故障信号大于0时,可认为车辆电气系统发生故障。Specifically, the vehicle fault signal includes 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. When the difference between the two is greater than a predetermined speed difference threshold, it can be considered that the wheel slip phenomenon occurs. Chassis fault signal and tire pressure monitoring system fault signal can be detected by vehicle systems such as anti-lock braking system, electronic stability program, automatic parking system and/or tire pressure monitoring system. When any one or more of the monitoring system fault signals is greater than 0, it may be considered that the vehicle chassis and/or wheels are faulty. The electrical system fault signal can be detected by the motor system, battery management system, light control system, radar system and other vehicle systems. When any one or more fault signals in the electrical system are detected to be greater than 0, it can be considered that the vehicle electrical system is faulty .
综合停车姿态、驾驶员行为异常等级以及车辆故障信号构成特征向量,对特征向量进行处理,可以得到车辆发生陷坑事故的概率。在概率大于预定阈值时,判断车辆发生陷坑事故。Comprehensive parking posture, abnormal level of driver behavior and vehicle fault signal constitute a feature vector, and the probability of vehicle pit accident can be obtained by processing the feature vector. When the probability is greater than the predetermined threshold, it is determined that the vehicle has a sinkhole accident.
如此,将停车姿态、驾驶员行为异常等级以及车辆故障信号进行比较分析,能够准确判断车辆是否陷坑事故。In this way, by comparing and analyzing the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal, it is possible to accurately determine whether the vehicle has a sinkhole accident.
需要说明地,预定速度差值阈值、判断车辆陷坑概率的预定阈值等,均可根据车型、驾驶员行为习惯、车辆维修记录、车辆使用年限等参数设定,具体不做限定,例如预定速度差值阈值可以是1米每秒、2米每秒、3米每秒、5米每秒等,判断车辆陷坑概率的预定阈值可以是0.5、0.7、0.8等。It should be noted that the predetermined speed difference threshold, the predetermined threshold for judging the probability of vehicle sinking, etc., can be set according to parameters such as model, driver behavior, vehicle maintenance record, vehicle service life, etc., and there is no specific limitation. For example, the predetermined speed difference The value threshold may be 1 m/s, 2 m/s, 3 m/s, 5 m/s, etc., and the predetermined threshold for judging the probability of vehicle sinking may be 0.5, 0.7, 0.8, etc.
请参阅图13,在某些实施方式中,监控方法还包括:Referring to FIG. 13, in some embodiments, the monitoring method further includes:
S15:在确定车辆发生陷坑事故的情况下,发送报警信号至车辆的服务商以使得服务商可根据报警信号实施救援。S15: When it is determined that the vehicle has a sinkhole accident, send an alarm signal to the service provider of the vehicle so that the service provider can implement rescue according to the alarm signal.
在某些实施方式中,S15可以由判断模块114实现。或者说,判断模块114用于在确定车辆发生陷坑事故的情况下,发送报警信号至车辆的服务商以使得服务商可根据报警信号实施救援。In some embodiments, S15 may be implemented by the determination module 114 . In other words, the judging module 114 is configured to send an alarm signal to a service provider of the vehicle when it is determined that the vehicle has a sinkhole accident, so that the service provider can implement rescue according to the alarm signal.
在某些实施方式中,处理器104用于在确定车辆发生陷坑事故的情况下,发送报警信号至车辆的服务商以使得服务商可根据报警信号实施救援。In some embodiments, the processor 104 is configured to send an alarm signal to a service provider of the vehicle when it is determined that the vehicle has a sinkhole accident, so that the service provider can implement rescue according to the alarm signal.
具体地,在确定车辆发生陷坑事故的情况下,发送报警信号至车辆的服务商,服务商可根据报警信号实施救援,或进行与驾驶员联系,确认车辆发生陷坑事故后,根据情况实施救援。Specifically, when it is determined that the vehicle has a sinkhole accident, an alarm signal is sent to the service provider of the vehicle, and the service provider can implement rescue according to the alarm signal, or contact the driver to confirm that the vehicle has a sinkhole accident and implement rescue according to the situation.
如此,在车辆发生陷坑事故后,提供相应的救援帮助,能够改进车辆售后服务,提升用户体验。In this way, after a vehicle sinkhole accident, providing corresponding rescue assistance can improve the after-sales service of the vehicle and enhance the user experience.
本申请实施方式还提供了一种计算机可读存储介质。一个或多个存储有计算机程序的非易失性计算机可读存储介质,当计算机程序被一个或多个处理器执行时,实现上述任一实施方式的车辆的监控方法。The embodiments of the present application also provide a computer-readable storage medium. One or more non-volatile computer-readable storage media storing a computer program, when the computer program is executed by one or more processors, implements the vehicle monitoring method of any one of the above embodiments.
本申请实施方式还提供了一种车辆。车辆包括存储器及一个或多个处理器,一个或多个程序被存储在存储器中,并且被配置成由一个或多个处理器执行。程序包括用于执行上述任意一项实施方式的车辆陷坑事故的监控方法的指令。The embodiments of the present application also provide a vehicle. The vehicle includes a memory and one or more processors, and one or more programs are stored in the memory and configured to be executed by the one or more processors. The program includes instructions for performing the monitoring method for a vehicle sinkhole accident of any one of the above embodiments.
处理器可用于提供计算和控制能力,支撑整个车辆的运行。车辆的存储器为存储器其中的计算机可读指令运行提供环境。The processor can be used to provide the computing and control capabilities that underpin the operation of the entire vehicle. The memory of the vehicle provides an environment for the execution of computer readable instructions in the memory.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,程序可存储于一个或多个非易失性计算机可读存储介质中,该程序在执行时,可包括如上述各方法的实施例的流程。其中,存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)等。Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the programs can be stored in one or more non-volatile computer-readable storage media , when the program is executed, it may include the processes of the foregoing method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or the like.
以上实施例仅表达了本申请的几种实施方式,其描述较为具体和详细,但并不能因此而理解为对本申请专利范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本申请构思的前提下,还可以做出若干变形和改进,这些都属于本申请的保护范围。因此,本申请专利的保护范围应以所附权利要求为准。The above examples only represent several embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but should not be construed as a limitation on the scope of the patent of the present application. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.
Claims (13)
- 一种车辆陷坑事故的监控方法,其特征在于,所述监控方法包括:A monitoring method for a vehicle sinkhole accident, characterized in that the monitoring method comprises:获取车辆行驶信号;Obtain vehicle driving signals;根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常;Judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal;在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号;In the case that the parking posture is abnormal, obtain the driver behavior signal and the vehicle fault signal;根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故。Whether the sinkhole accident occurs to the vehicle is determined according to the parking posture, the driver behavior signal and the vehicle failure signal.
- 根据权利要求1所述的监控方法,其特征在于,所述获取车辆行驶信号包括:The monitoring method according to claim 1, wherein the acquiring the vehicle driving signal comprises:获取当前时间前第一预定时间至当前时间的第一时间段内每个单位时间的车辆行驶信号。The vehicle driving signal of each unit time in the first time period from the first predetermined time before the current time to the current time is acquired.
- 根据权利要求2所述的监控方法,其特征在于,所述根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常包括;The monitoring method according to claim 2, wherein the judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal comprises;根据预存储的停车姿态模型对所述车辆行驶信号进行识别以判断所述车辆的停车姿态从而确定所述停车姿态是否异常。The vehicle driving signal is identified according to the pre-stored parking attitude model to judge the parking attitude of the vehicle to determine whether the parking attitude is abnormal.
- 根据权利要求3所述的监控方法,其特征在于,所述车辆行驶信号包括所述车辆的实时三轴加速度信号和所述车辆各个车轮的速度信号,所述根据预存储的停车姿态模型对所述车辆行驶信号进行识别以判断所述车辆的停车姿态从而确定所述停车姿态是否异常包括:The monitoring method according to claim 3, wherein the vehicle driving signal includes a real-time three-axis acceleration signal of the vehicle and a speed signal of each wheel of the vehicle, and the vehicle is based on a pre-stored parking attitude model. Identifying the vehicle driving signal to determine the parking posture of the vehicle to determine whether the parking posture is abnormal includes:根据所述速度信号判断所述车辆是否处于停车状态;Determine whether the vehicle is in a parking state according to the speed signal;在所述车辆处于所述停车状态时,根据所述三轴加速度信号确定所述车辆在所述第一时间段内每个单位时间的姿态角;When the vehicle is in the parking state, determining the attitude angle of the vehicle per unit time in the first time period according to the three-axis acceleration signal;根据所述停车姿态模型对所述姿态角进行处理以得到所述车辆的停车姿态。The attitude angle is processed according to the parking attitude model to obtain the parking attitude of the vehicle.
- 根据权利要求1所述的监控方法,其特征在于,所述在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号包括;The monitoring method according to claim 1, wherein, in the case that the parking posture is abnormal, acquiring the driver behavior signal and the vehicle fault signal comprises;在所述停车姿态异常的情况下,获取当前时间前第二预定时间至当前时间后第三预定时间的第二时间段内每个单位时间的驾驶员行为信号;In the case that the parking posture is abnormal, obtain the driver behavior signal of each unit time in the second time period from the second predetermined time before the current time to the third predetermined time after the current time;获取所述第二时间段内每个单位时间的车辆故障信号。Acquire a vehicle fault signal per unit time in the second time period.
- 根据权利要求5所述的监控方法,其特征在于,所述根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故包括:The monitoring method according to claim 5, wherein the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal comprises:根据预存储的驾驶员异常行为模型对所述驾驶员行为信号进行识别以判断驾驶员行为异常等级。The driver's behavior signal is identified according to a pre-stored abnormal driver behavior model to determine the abnormal level of the driver's behavior.
- 根据权利要求6所述的监控方法,其特征在于,所述驾驶员行为信号包括车辆上锁信号、双闪灯信号、挡位信号、加速踏板信号、主驾车门信号和方向盘转角信号,所述根据预存储的驾驶员 异常行为模型对所述驾驶员行为信号进行识别以判断驾驶员行为异常等级包括:The monitoring method according to claim 6, wherein the driver behavior signal includes a vehicle lock 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 signal according to the pre-stored abnormal driver behavior model to determine the abnormal level of driver behavior includes:根据所述主驾车门信号和所述车辆上锁信号判断是否存在下车后预定时长内未锁车的第一异常行为;Determine whether there is a first abnormal behavior of unlocking the vehicle within a predetermined period of time after getting off the vehicle according to the main driving door signal and the vehicle locking signal;根据所述挡位信号和所述双闪灯信号判断是否存在开启双闪灯的第二异常行为;Determine whether there is a second abnormal behavior of turning on the double flashing lights according to the gear signal and the double flashing light signal;根据所述加速踏板信号判断是否存在猛踩加速踏板的第三异常行为;judging whether there is a third abnormal behavior of slamming the accelerator pedal according to the accelerator pedal signal;根据所述方向盘转角信号判断是否存在反复操作方向盘的第四异常行为;Determine whether there is a fourth abnormal behavior of repeatedly operating the steering wheel according to the steering wheel angle signal;根据所述主驾车门信号和所述方向盘转角信号判断是否存在下车后方向盘未回正的第五异常行为;According to the main driving door signal and the steering wheel angle signal, determine whether there is a fifth abnormal behavior of the steering wheel not returning to the right position after getting off the car;根据预设定的评级规则、所述第一异常行为、所述第二异常行为、所述第三异常行为、所述第四异常行为和所述第五异常行为判断所述驾驶员行为异常等级。The abnormal level of the driver's behavior is judged 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 .
- 根据权利要求6所述的监控方法,其特征在于,所述根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故包括:The monitoring method according to claim 6, wherein the determining whether the vehicle has the sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal comprises:根据预存储的车辆陷坑模型对所述停车姿态、所述驾驶员行为异常等级以及所述车辆故障信号进行识别以判断所述车辆是否发生所述陷坑事故。The parking posture, the abnormal level of the driver's behavior and the vehicle fault signal are identified according to a pre-stored vehicle sinkhole model to determine whether the vehicle has the sinkhole accident.
- 根据权利要求8所述的监控方法,其特征在于,所述车辆故障信号包括打滑信号、底盘故障信号、胎压监控系统故障信号和电气系统故障信号,所述根据预存储的车辆陷坑模型对所述停车姿态、所述驾驶员行为异常等级以及所述车辆故障信号进行识别以判断所述车辆是否发生所述陷坑事故包括:The monitoring method according to claim 8, wherein the vehicle fault signal comprises a skid signal, a chassis fault signal, a tire pressure monitoring system fault signal and an electrical system fault signal, and the vehicle fault signal is determined according to a pre-stored vehicle sinkhole model. Identifying the parking posture, the abnormal level of the driver's behavior, and the vehicle fault signal to determine whether the vehicle has the sinkhole accident includes:根据所述根据预存储的车辆陷坑模型对由所述停车姿态、所述驾驶员行为异常等级以及所述车辆故障信号构成的特征向量进行处理以得到所述车辆发生所述陷坑事故的概率;According to the pre-stored vehicle sinkhole model, the feature vector formed by the parking posture, the abnormal level of the driver's behavior and the vehicle fault signal is processed to obtain the probability of the vehicle having the sinkhole accident;在所述概率大于预定阈值时确定所述车辆发生所述陷坑事故。It is determined that the sinkhole accident occurs to the vehicle when the probability is greater than a predetermined threshold.
- 根据权利要求1所述的监控方法,其特征在于,所述监控方法还包括:The monitoring method according to claim 1, wherein the monitoring method further comprises:在确定所述车辆发生所述陷坑事故的情况下,发送报警信号至所述车辆的服务商以使得所述服务商可根据所述报警信号实施救援。When it is determined that the vehicle has the sinkhole accident, an alarm signal is sent to a service provider of the vehicle so that the service provider can implement rescue according to the alarm signal.
- 一种车辆的监控装置,其特征在于,所述监控装置包括:A monitoring device for a vehicle, characterized in that the monitoring device comprises:获取模块,所述获取模块用于获取车辆行驶信号;an acquisition module, the acquisition module is used to acquire the vehicle driving signal;判断模块,所述判断模块用于根据所述车辆行驶信号判断所述车辆的停车姿态以确定所述停车姿态是否异常;a judging module, which is used for judging the parking posture of the vehicle according to the vehicle driving signal to determine whether the parking posture is abnormal;所述获取模块还用于在所述停车姿态异常的情况下,获取驾驶员行为信号和车辆故障信号;The obtaining module is further configured to obtain the driver behavior signal and the vehicle fault signal when the parking posture is abnormal;所述判断模块还用于根据所述停车姿态、所述驾驶员行为信号和所述车辆故障信号判断所述车辆是否发生所述陷坑事故。The judging module is further configured to judge whether the vehicle has the sinkhole accident according to the parking posture, the driver's behavior signal and the vehicle fault signal.
- 一种服务器,包括存储器和处理器,所述存储器存储有计算机程序,其特征在于,所述处 理器用于执行所述计算机程序时实现权利要求1-10任一项所述的车辆陷坑事故的监控方法。A server, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor is configured to implement the monitoring of the vehicle sinkhole accident according to any one of claims 1-10 when executing the computer program method.
- 一个或多个存储有计算机程序的非易失性计算机可读存储介质,当所述计算机程序被一个或多个处理器执行时,实现权利要求1-10中任意一项所述的车辆陷坑事故的监控方法。One or more non-volatile computer-readable storage media storing a computer program that, when executed by one or more processors, implements the vehicle sinkhole accident of any one of claims 1-10 monitoring method.
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