CN111942397A - Dangerous driving behavior monitoring method and device and storage medium - Google Patents
Dangerous driving behavior monitoring method and device and storage medium Download PDFInfo
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- CN111942397A CN111942397A CN202010785686.5A CN202010785686A CN111942397A CN 111942397 A CN111942397 A CN 111942397A CN 202010785686 A CN202010785686 A CN 202010785686A CN 111942397 A CN111942397 A CN 111942397A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0818—Inactivity or incapacity of driver
- B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
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Abstract
The application relates to a dangerous driving behavior monitoring method, a dangerous driving behavior monitoring device and a storage medium, wherein the method comprises the following steps: acquiring running data of a target vehicle and acquisition frequency of the running data; judging the data type of the driving data according to the acquisition frequency of the driving data; and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data. According to the data acquisition frequency, the dangerous driving behaviors needing to be monitored are determined, the meaningless invalid judgment of partial dangerous driving behaviors due to the difference of the acquisition frequency is reduced, the calculated amount is reduced, and the effectiveness and the timeliness are improved. In addition, dangerous driving behaviors are judged through multiple dimensions, the evaluation dimensions are comprehensive, and the judgment result is more accurate.
Description
Technical Field
The application relates to the technical field of safe driving, in particular to a dangerous driving behavior monitoring method, a dangerous driving behavior monitoring device and a dangerous driving behavior monitoring storage medium.
Background
With the improvement of the awareness of traffic safety, the development of vehicle technology, and the maturity of information technology, in order to enhance the driving safety, the research and development of various driving assistance systems have become a hot issue of traffic safety and vehicle intelligent research. The data shows that traffic accidents due to driver factors account for more than 70% of the total number of traffic accidents. How to rapidly and accurately detect dangerous driving behaviors of a driver is an important premise for predicting dangers, reminding and even intervening or intervening vehicle operation so as to improve driving safety.
At present, various technologies for detecting dangerous driving behaviors appear in markets at home and abroad. These techniques generally implement dangerous driving behavior detection or determination based on driving behavior (steering wheel angle, throttle, brake, clutch, gear, etc.) data. However, in the danger evaluation index system for driving behaviors in the prior art, no uniform risk evaluation standard exists for various dangerous behaviors; and different vehicle running states and different behavior risk degrees have differences, and the coupling relation between the vehicle running state and various dangerous driving behavior evaluation threshold values cannot be considered in the prior art, so that the evaluation is too much, the evaluation dimension is single and is not comprehensive enough, and the judgment result of the dangerous driving behavior is inaccurate or even one-sided.
Disclosure of Invention
In order to solve the technical problems of single dimension, one-sided performance and inaccurate result of detecting or evaluating dangerous driving behaviors, the embodiment of the application provides a dangerous driving behavior monitoring method, a dangerous driving behavior monitoring device and a storage medium.
In a first aspect, an embodiment of the present application provides a dangerous driving behavior monitoring method, where the method includes:
acquiring running data of a target vehicle and acquisition frequency of the running data;
judging the data type of the driving data according to the acquisition frequency of the driving data;
and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
Optionally, before determining the data type of the driving data according to the collection frequency of the driving data, the method further comprises:
and cleaning the running data according to a preset rule to delete the abnormal data.
Optionally, the data types include a first type of data and a second type of data;
judging whether the target vehicle has dangerous driving behaviors according to the driving data and the data type of the driving data, wherein the judging step comprises the following steps:
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors or not according to the running data when the running data is the first type data,
and when the driving data is the second type data, judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors or not according to the driving data.
Optionally, the driving data includes instantaneous speed, positioning information, and the dangerous driving behavior in the first monitoring behavior and the second monitoring behavior includes: overspeed;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors according to the driving data, both include:
acquiring the road speed limit of a target road driven by the target vehicle according to the positioning information,
acquiring the overspeed threshold value of the target vehicle according to the road speed limit,
and judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and an overspeed threshold value.
Optionally, the driving data further comprises acceleration, and the dangerous driving behavior in the first monitoring behavior further comprises: rapid acceleration and rapid deceleration;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
a sharp acceleration threshold or a sharp deceleration threshold of the target vehicle is obtained according to the instantaneous speed of the target vehicle,
and judging whether the target vehicle has a rapid acceleration behavior or a rapid deceleration behavior according to the rapid acceleration threshold or the rapid deceleration threshold of the target vehicle and the acceleration.
Optionally, the driving data further includes an instantaneous angular velocity and a vehicle driving trajectory, and the dangerous driving behavior in the first monitoring behavior further includes: sharp lane and sharp turn;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
judging the driving type of the target vehicle according to the driving track of the vehicle, wherein the driving type comprises lane changing behavior and turning behavior,
a sharp lane change threshold or a sharp turn threshold of the target vehicle is obtained according to the instantaneous speed of the target vehicle,
when the driving type is lane changing behavior, judging whether the target vehicle has lane changing behavior according to the instantaneous angular velocity of the target vehicle and a lane changing threshold,
and when the driving type is turning behavior, judging whether the target vehicle has the sharp turning behavior according to the instantaneous angular speed of the target vehicle and the sharp turning threshold value.
Optionally, the driving data further comprises a time window, and the dangerous driving behavior in the first monitoring behavior further comprises: driving with instability;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
obtaining the unstable driving index of the target vehicle according to the instantaneous speed of the target vehicle at each acquisition moment in the time window,
the jerk index is an average of absolute values of speed differences of adjacent instantaneous speeds within a time window,
and judging whether the target vehicle has the unstable driving behavior in the time window according to a preset unstable driving behavior threshold value and the unstable driving degree index of the target vehicle.
Optionally, the driving data further includes driving time, and the dangerous driving behavior in the first monitoring behavior and the second monitoring behavior further includes: fatigue driving;
whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors or not is judged according to the driving data, whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors or not is judged according to the driving data, and the method further comprises the following steps:
counting the driving time of the target vehicle in one day according to the driving time,
counting the continuous driving time of the target vehicle according to the driving time,
and if the driving time of the target vehicle in one day exceeds a first preset safe time, or any continuous driving time exceeds a second preset safe time, judging that the fatigue driving behavior of the target vehicle exists in the corresponding time.
In a second aspect, an embodiment of the present application provides a dangerous driving behavior monitoring apparatus, including:
the acquisition module is used for acquiring the running data of the target vehicle and the acquisition frequency of the running data;
the judging module is used for judging the data type of the driving data according to the acquisition frequency of the driving data;
and the analysis module is used for judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of the method according to any one of the preceding claims.
In a fourth aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to perform the steps of the method according to any of the preceding claims.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the technical scheme of the embodiment of the application, the driving data of the target vehicle and the acquisition frequency of the driving data are acquired; judging the data type of the driving data according to the acquisition frequency of the driving data; when the data type of the driving data is first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors or not according to the driving data; and when the data type of the driving data is second type data, judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors or not according to the driving data. The dangerous driving behaviors to be monitored are determined according to the data acquisition frequency, the meaningless invalid judgment of partial dangerous driving behaviors due to the difference of the acquisition frequency is reduced, the calculated amount is reduced, the dangerous driving behaviors are pertinently and effectively monitored, and the judgment timeliness is improved. Meanwhile, by establishing respective evaluation rules for various dangerous driving behaviors and judging the risk degree according to the running state of the target vehicle, the evaluation dimensionality is comprehensive, and the judgment result is more accurate. The method has wide application in danger prediction, reminding and even intervention or intervening vehicle operation so as to improve driving safety for quickly and accurately detecting dangerous driving behaviors of the driver.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a dangerous driving behavior monitoring method according to an embodiment;
FIG. 2 is a schematic structural diagram of a dangerous driving behavior monitoring device according to an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic flow chart of a dangerous driving behavior monitoring method in one embodiment. Referring to fig. 1, the method includes the steps of:
s100: and acquiring the running data of the target vehicle and the acquisition frequency of the running data.
Specifically, the driving data of the target vehicle may include, but is not limited to, speed (e.g., instantaneous speed), acceleration (e.g., instantaneous acceleration), angular velocity (e.g., instantaneous angular velocity), driving track (e.g., turning, lane changing, etc.), driving duration, positioning information of the target road, and the like. The driving data can be acquired by receiving data sent by the driving navigation positioning equipment. The target vehicle can be provided with a driving navigation positioning device and other data acquisition devices such as OBD devices, and the device installation should avoid or minimize physical or psychological interference to the driver to ensure that the driver is in a normal driving state.
The collection frequency of the driving data can be divided into a high-frequency collection frequency and a low-frequency collection frequency.
In a particular embodiment, the high frequency acquisition frequency is an acquisition frequency not lower than 1Hz and the low frequency acquisition frequency is an acquisition frequency lower than 1 Hz.
S200: and judging the data type of the driving data according to the acquisition frequency of the driving data.
Specifically, the travel data may be divided into high-frequency data and low-frequency data according to the collection frequency. The data acquired by the high-frequency acquisition frequency is high-frequency data, and the data acquired by the low-frequency acquisition frequency is low-frequency data.
S300: and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
In a particular embodiment, the data types include a first type of data, a second type of data. Step S300 specifically includes: when the data type of the driving data is first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors or not according to the driving data; and when the data type of the driving data is second type data, judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors or not according to the driving data.
Specifically, the first type of data is high frequency data, and the dangerous driving behavior in the first monitoring behavior may include overspeed, rapid acceleration, rapid deceleration, rapid lane change, sharp turn, jerky driving, fatigue driving, and the like.
The second type of data is low frequency data and the dangerous driving behavior of the second monitored behavior may include speeding, fatigue driving.
Because the second type of data is low-frequency data, the acquisition frequency is low, the acquisition interval is long, and for the monitoring behaviors of the target vehicle, such as rapid acceleration, rapid deceleration, rapid lane change, rapid turning and unstable driving, which need to intensively track the driving data of the target vehicle, whether the target vehicle has the disadvantages of rapid acceleration, rapid deceleration, rapid lane change, rapid turning and unstable driving is judged by the low-frequency data, the judgment accuracy is low, and the effectiveness is low. The dangerous driving behaviors needing to be monitored are determined according to the type of the driving data, unnecessary calculation can be reduced, and the dangerous driving behaviors can be judged more accurately.
In one embodiment, before step S200, the method further comprises the steps of:
and cleaning the running data according to a preset rule to delete the abnormal data.
Specifically, taking into full account various types of driving conditions that may occur during driving, data that meet the following conditions will be identified as abnormal data:
a) the speed is more than 200km/h
b) The speed is less than 0km/h
c) The absolute value of the acceleration is more than 12m/s2
d) The absolute value of the transverse acceleration is more than 12m/s2
e) The absolute value of the angular velocity is larger than 90 DEG/s
f) Positioning of road with obvious deviation of longitude and latitude from target
Wherein the speed comprises an instantaneous speed and an average speed; the acceleration includes an instantaneous acceleration, an average acceleration, and the angular velocity includes an instantaneous angular velocity.
The influence and the interference of the abnormal data on the judgment of the dangerous driving behaviors can be reduced by deleting the abnormal data, so that the judgment result is more accurate.
In one embodiment, the driving data includes instantaneous speed, location information, and the dangerous driving behavior of the first and second monitoring behaviors each includes: overspeed.
When the driving data is first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, wherein the method comprises the following steps:
acquiring the road speed limit of a target road driven by the target vehicle according to the positioning information,
acquiring the overspeed threshold value of the target vehicle according to the road speed limit,
and judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and an overspeed threshold value.
When the driving data is the second type data, judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors according to the driving data, wherein the method comprises the following steps:
acquiring the road speed limit of a target road driven by the target vehicle according to the positioning information,
acquiring the overspeed threshold value of the target vehicle according to the road speed limit,
and judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and an overspeed threshold value.
Specifically, the road on which the target vehicle runs can be obtained according to the positioning information, and then the road speed limit of the target road is obtained. The overspeed threshold value of the target vehicle can be obtained according to the road speed limit, and whether the target vehicle is overspeed or not can be judged by comparing the instantaneous speed with the overspeed threshold value. Specifically, the level of the overspeed may also be determined, and the level of the overspeed includes: safe, dangerous and dangerous.
The overspeed threshold is set to 2 and calculated according to the following formula:
overspeed threshold value 1 ═ Vlim
Overspeed threshold 2 ═ 1.1Vlim
Wherein, VlimIs the road speed limit of the target road. Road speed limits on different target roads may include: 120km/h, 100km/h, 80km/h, 60km/h, 40km/h, 30km/h, 20 km/h.
The rating of the overspeed behavior is divided in the following way:
wherein, R1 is overspeed behavior danger level, takes values of 0, 1 and 2, and respectively represents safety, relatively dangerous and dangerous; v is the instantaneous speed of the target vehicle.
In a particular embodiment, the driving data further includes acceleration, and the dangerous driving behavior in the first monitoring behavior further includes: rapid acceleration and rapid deceleration.
When the driving data is the first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising the following steps of:
a sharp acceleration threshold or a sharp deceleration threshold of the target vehicle is obtained according to the instantaneous speed of the target vehicle,
and judging whether the target vehicle has a rapid acceleration behavior or a rapid deceleration behavior according to the rapid acceleration threshold or the rapid deceleration threshold of the target vehicle and the acceleration.
Specifically, when the target vehicle is running with acceleration, a rapid acceleration threshold of the target vehicle is acquired according to the instantaneous speed of the target vehicle. Comparing the acceleration of the target vehicle with the rapid acceleration threshold may determine whether the target vehicle has rapid acceleration behavior. Specifically, the level of the rapid acceleration behavior may also be determined, and the level of the rapid acceleration behavior includes, for example: safe, safer, more dangerous, dangerous.
The number of the rapid acceleration threshold values is 3, and the rapid acceleration threshold values are calculated according to the following formula:
rapid acceleration threshold 1 ═ 0.0115V +2.58
The rapid acceleration threshold 2 is-0.0187V +4.15
The rapid acceleration threshold value of 3 is-0.0232V +5.18
The level of rapid acceleration behavior is divided in the following manner:
wherein, R2 is the danger level of the rapid acceleration behavior, takes values of 0, 1, 2 and 3, and respectively represents safety, safer, more dangerous and dangerous; a is the acceleration of the target vehicle. Specifically, a is the instantaneous acceleration of the target vehicle.
In another embodiment, whether the target vehicle has a sudden acceleration behavior may be determined based on the average acceleration of the target vehicle. In this case, the level of the rapid acceleration behavior includes, for example: safety and danger.
The number of the rapid acceleration threshold is 1, and the rapid acceleration threshold is calculated according to the following formula:
the rapid acceleration threshold value 4 is-0.0168V +3.63
The level of rapid acceleration behavior is divided in the following manner:
wherein, R3 is the danger level of the rapid acceleration behavior, takes values of 0 and 1, and respectively represents safety and danger;
of a target vehicleThe average acceleration. In particular, the amount of the solvent to be used,
is the average acceleration over a continuous period of time. The continuous time period may be specifically 3 seconds or more.
Specifically, when the target vehicle is running with deceleration, a rapid deceleration threshold of the target vehicle is acquired in accordance with the instantaneous speed of the target vehicle. Comparing the acceleration of the target vehicle with the rapid deceleration threshold may determine whether the target vehicle has rapid deceleration behavior. Specifically, the level of the sudden deceleration behavior may also be determined, and the level of the sudden deceleration behavior includes, for example: safe, safer, more dangerous, dangerous.
The number of the rapid deceleration threshold values is 3, and the rapid deceleration threshold values are calculated according to the following formula:
the rapid deceleration threshold value 1 is 0.0115V-2.08
The rapid deceleration threshold 2 is 0.0187V-3.66
The rapid deceleration threshold value 3 is 0.0232V-4.68
The level of the rapid deceleration behavior is divided in the following manner:
wherein, R4 is the danger level of the rapid deceleration action, takes values of 0, 1, 2 and 3, and respectively represents safety, safer, more dangerous and dangerous; a is the acceleration of the target vehicle. Specifically, a is the instantaneous acceleration of the target vehicle.
In another embodiment, whether the target vehicle has a sudden deceleration behavior may be determined based on the average acceleration of the target vehicle. In this case, the level of the rapid deceleration behavior includes, for example: safety and danger.
The number of the rapid deceleration threshold is 1, and the rapid deceleration threshold is calculated according to the following formula:
the rapid deceleration threshold value 4 is 0.0168V-3.13
The level of the rapid deceleration behavior is divided in the following manner:
wherein, R5 is the danger level of the rapid deceleration behavior, takes values of 0 and 1, and respectively represents safety and danger;
the average acceleration of the target vehicle. In particular, the amount of the solvent to be used,
is the average acceleration over a continuous period of time. The continuous time period may be specifically 3 seconds or more.
In a specific embodiment, the driving data further includes an instantaneous angular velocity and a vehicle driving trajectory, and the dangerous driving behavior of the first monitoring behavior further includes: sharp turns and sharp turns.
When the driving data is the first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising the following steps of:
acquiring a sharp lane change threshold or a sharp turning threshold of the target vehicle according to the instantaneous speed of the target vehicle;
judging the driving type of the target vehicle according to the driving track of the vehicle, wherein the driving type comprises lane changing behavior and turning behavior,
when the driving type is lane changing behavior, judging whether the target vehicle has lane changing behavior according to the instantaneous angular velocity of the target vehicle and a lane changing threshold,
and when the driving type is turning behavior, judging whether the target vehicle has the sharp turning behavior according to the instantaneous angular speed of the target vehicle and the sharp turning threshold value.
Specifically, when the target vehicle is traveling in a lane change, a lane change snap threshold of the target vehicle is obtained according to the instantaneous speed of the target vehicle. Comparing the instantaneous angular velocity of the target vehicle with the jerk threshold may determine whether jerk behavior exists with the target vehicle. Specifically, the level of the quick-change behavior may also be determined, and the level of the quick-change behavior includes, for example: safe, safer, more dangerous, dangerous.
The number of the sudden lane change threshold values is 3, and the sudden lane change threshold values are calculated according to the following formula:
quick change threshold 1 ═ 0.0352V +7.23
Quick change threshold 2-0.0554V +11.76
Quick lane change threshold 3 ═ 0.0689V +14.96
The grade of the quick lane change behavior is divided as follows:
wherein, R6 is the risk level of the behavior of the sudden change road, takes values of 0, 1, 2 and 3, and respectively represents safety, safer, more dangerous and dangerous; w is the instantaneous angular velocity of the target vehicle.
When the target vehicle is running in a curve, a sharp curve threshold value of the target vehicle is obtained according to the instantaneous speed of the target vehicle. Comparing the instantaneous angular velocity of the target vehicle with the tight turning threshold may determine whether there is tight turning behavior by the target vehicle. Specifically, the grade of the sharp turning behavior may also be determined, and the grade of the sharp turning behavior includes, for example: safe, safer, more dangerous, dangerous.
The number of the sharp turning threshold values is 3, and the sharp turning threshold values are calculated according to the following formula:
sharp turn threshold 1 ═ 0.0984V +15.50
Sharp turn threshold 2-0.1595V +24.95
Sharp turn threshold 3 ═ 0.1947V +31.18
The grade of sharp turn behavior is divided in the following way:
wherein, R7 is the danger level of sharp turn behavior, takes values of 0, 1, 2 and 3, and respectively represents safety, safer, more dangerous and dangerous; w is the instantaneous angular velocity of the target vehicle.
In a specific embodiment, the driving data further includes a time window, and the dangerous driving behavior in the first monitoring behavior further includes: driving is not smooth.
In a specific embodiment, the calculation formula of each threshold is obtained by fitting a large amount of data.
When the driving data is the first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
obtaining the unstable driving index of the target vehicle according to the instantaneous speed of the target vehicle at each acquisition moment in the time window,
the jerk index is an average of absolute values of speed differences of adjacent instantaneous speeds within a time window,
and judging whether the target vehicle has the unstable driving behavior in the time window according to a preset unstable driving behavior threshold value and the unstable driving degree index of the target vehicle.
Specifically, the unstable driving degree index is an average of absolute values of speed differences of adjacent instantaneous speeds during continuous running of the target vehicle within a time window. The calculation formula of the unstable driving degree index is as follows:
wherein phi isvAs an index of rough driving degree, viThe instantaneous speed of the target vehicle at the ith acquisition time in the time window; i is a positive integer greater than or equal to 2, and N is the total number of acquisition moments in the time window. In a specific embodiment, the time window may be set to be plural, the duration of the time window may be set to be 20s or 30s or 40s, etc., but is not limited thereto, and the acquisition time within the time window may be one instantaneous speed acquired every 1 s.
The unstable driving degree index of the target vehicle is compared with the unstable driving behavior threshold value to judge whether the target vehicle has unstable driving behavior. Specifically, the level of the unstable driving behavior may also be determined, and the level of the unstable driving behavior includes, for example: safe, safer, more dangerous, dangerous.
If the unstable driving index of the target vehicle is less than or equal to the unstable driving behavior threshold value, judging that the unstable driving behavior safety level of the target vehicle in the preset time window is safe; if the unstable driving index of the target vehicle is larger than the unstable driving behavior threshold value, judging that the unstable driving behavior safety level of the target vehicle in the preset time window is dangerous; and counting all preset time windows for judging the safety level of the unstable driving behavior of each target vehicle as dangerous to obtain the duration of the unstable driving behavior of each target vehicle.
The rough driving behavior threshold may be preset. In a specific embodiment, the number of the rough driving behavior threshold values is 3, and the values are respectively 3, 4 and 6. When the unstable driving degree index is within the (0, 3) interval, the grade of the unstable driving behavior of the target vehicle is judged to be safe, when the unstable driving degree index is within the (3, 4) interval, the grade of the unstable driving behavior of the target vehicle is judged to be safer, when the unstable driving degree index is within the (4, 6) interval, the grade of the unstable driving behavior of the target vehicle is judged to be more dangerous, and when the unstable driving degree index is within the (6, + ∞) interval, the grade of the unstable driving behavior of the target vehicle is judged to be dangerous.
In one embodiment, the driving data further includes driving time, and the dangerous driving behavior in the first monitoring behavior and the second monitoring behavior each further includes: fatigue driving.
When the driving data is the first type data, judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising the following steps of:
counting the driving time of the target vehicle in one day according to the driving time,
counting the continuous driving time of the target vehicle according to the driving time,
and if the driving time of the target vehicle in one day exceeds a first preset safe time, or any continuous driving time exceeds a second preset safe time, judging that the fatigue driving behavior of the target vehicle exists in the corresponding time.
When the driving data is the second type data, judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors according to the driving data, and further comprising the following steps of:
counting the driving time of the target vehicle in one day according to the driving time,
counting the continuous driving time of the target vehicle according to the driving time,
and if the driving time of the target vehicle in one day exceeds a first preset safe time, or any continuous driving time exceeds a second preset safe time, judging that the fatigue driving behavior of the target vehicle exists in the corresponding time.
In one embodiment, the first predetermined safe period of time is 8 hours and the second predetermined safe period of time is 4 hours. If the target vehicle is driven for more than 8 hours in a cumulative manner every day or the continuous driving time exceeds 4 hours, the driving behavior without stopping and resting for more than 20 minutes is considered as fatigue driving.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
FIG. 2 is a schematic structural diagram of a dangerous driving behavior monitoring device according to an embodiment; referring to fig. 2, the apparatus includes:
the acquisition module 100 is used for acquiring the running data of the target vehicle and the acquisition frequency of the running data;
the judging module 200 is used for judging the data type of the driving data according to the acquisition frequency of the driving data;
the analysis module 300: and the system is used for judging whether dangerous driving behaviors exist in the target vehicle according to the driving data and the data type of the driving data.
In a particular embodiment, the data types include a first type of data, a second type of data.
The analysis module 300 specifically includes:
the first analysis module is used for judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors or not according to the driving data when the data type of the driving data is the first type data;
and the second analysis module is used for judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors or not according to the driving data when the data type of the driving data is the second type data.
In one embodiment, the apparatus further comprises:
and the data cleaning module is used for cleaning the data of the running data according to a preset rule so as to delete the abnormal data.
In one embodiment, the driving data includes instantaneous speed, location information, and the dangerous driving behavior of the first and second monitoring behaviors each includes: overspeed.
The first analysis module specifically comprises:
a first positioning unit for acquiring the road speed limit of the target road on which the target vehicle runs according to the positioning information,
a first processing unit for obtaining the overspeed threshold value of the target vehicle according to the road speed limit,
and the first judgment unit is used for judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and the overspeed threshold value.
The second analysis module specifically comprises:
a second positioning unit for acquiring the road speed limit of the target road driven by the target vehicle according to the positioning information,
a second processing unit for obtaining the overspeed threshold value of the target vehicle according to the road speed limit,
and the second judging unit is used for judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and the overspeed threshold value.
In a particular embodiment, the driving data further includes acceleration, and the dangerous driving behavior in the first monitoring behavior further includes: rapid acceleration and rapid deceleration.
The first analysis module further comprises:
a third processing unit for obtaining a rapid acceleration threshold or a rapid deceleration threshold of the target vehicle according to the instantaneous speed of the target vehicle,
and the third judging unit is used for judging whether the target vehicle has the rapid acceleration behavior or the rapid deceleration behavior according to the rapid acceleration threshold or the rapid deceleration threshold of the target vehicle and the acceleration.
In a particular embodiment, the driving data further includes instantaneous angular velocity and a vehicle driving trajectory, and the dangerous driving behavior in the first monitoring behavior further includes: sharp turns and sharp turns.
The first analysis module further comprises:
a track analysis unit for judging the driving type of the target vehicle according to the running track of the vehicle, wherein the driving type comprises lane changing behavior and turning behavior,
a fourth processing unit for acquiring a sharp lane change threshold or a sharp turn threshold of the target vehicle according to an instantaneous speed of the target vehicle,
and the fourth judging unit is used for judging whether the target vehicle has sharp lane change behavior according to the instantaneous angular speed of the target vehicle and the sharp lane change threshold value when the driving type is lane change behavior, and judging whether the target vehicle has sharp turning behavior according to the instantaneous angular speed of the target vehicle and the sharp turning threshold value when the driving type is turning behavior.
In a specific embodiment, the driving data further includes an analysis time window, and the dangerous driving behavior in the first monitoring behavior further includes: driving is not smooth.
The first analysis module further comprises:
a fifth processing unit for obtaining an unstable driving degree index of the target vehicle according to the instantaneous speed of the target vehicle at each acquisition time within the analysis time window, the unstable driving degree index being an average value of absolute values of speed differences of adjacent instantaneous speeds within the analysis time window,
and the fifth judging unit is used for judging whether the target vehicle has the unstable driving behavior in the analysis time window according to the preset unstable driving behavior threshold value and the unstable driving degree index of the target vehicle.
In one embodiment, the driving data further includes driving time, and the dangerous driving behavior of the first and second monitoring behaviors further includes: fatigue driving.
The first analysis module further comprises:
a first statistical unit for counting the driving time of the target vehicle in one day according to the driving time,
a second statistical unit for counting a continuous driving time period of the target vehicle according to the driving time,
and the sixth judging unit is used for judging that the fatigue driving behavior of the target vehicle exists in the corresponding time if the driving time of the target vehicle in one day exceeds the first preset safe time or any continuous driving time exceeds the second preset safe time.
The second analysis module specifically further comprises:
a third statistical unit for counting the driving time of the target vehicle in one day according to the driving time,
a fourth statistical unit for counting a continuous driving time period of the target vehicle according to the driving time,
and the seventh judging unit is used for judging that the fatigue driving behavior of the target vehicle exists in the corresponding time if the driving time of the target vehicle in one day exceeds the first preset safe time or any continuous driving time exceeds the second preset safe time.
According to the method and the device, which dangerous driving behaviors need to be monitored are determined according to the acquisition frequency of the data, so that the meaningless invalid judgment of partial dangerous driving behaviors due to the difference of the acquisition frequency is reduced, the calculated amount is reduced, the dangerous driving behaviors are pertinently and effectively monitored, and the timeliness of the judgment is improved. Meanwhile, by establishing respective evaluation rules for various dangerous driving behaviors and judging the risk degree according to the running state of the target vehicle, the evaluation dimensionality is comprehensive, and the judgment result is more accurate. The method has wide application in danger prediction, reminding and even intervention or intervening vehicle operation so as to improve driving safety for quickly and accurately detecting dangerous driving behaviors of the driver.
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment. The computer equipment comprises a processor, a memory, a network interface, an input device and a display screen which are connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to implement the method of monitoring dangerous driving behavior. The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform a method of monitoring dangerous driving behavior. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the dangerous driving behavior monitoring apparatus provided in the present application may be implemented in the form of a computer program that is executable on a computer device such as the one shown in fig. 3. The memory of the computer device may store various program modules constituting the dangerous driving behavior monitoring apparatus, such as the collecting module 100, the judging module 200, and the analyzing module 300 shown in fig. 2. The respective program modules constitute computer programs that cause the processors to execute the steps in the dangerous driving behavior monitoring methods of the respective embodiments of the present application described in the present specification.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring the acquired running data of the target vehicle and the acquisition frequency of the running data; judging the data type of the driving data according to the acquisition frequency of the driving data; and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring the acquired running data of the target vehicle and the acquisition frequency of the running data; judging the data type of the driving data according to the acquisition frequency of the driving data; and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for monitoring dangerous driving behavior, the method comprising:
acquiring running data of a target vehicle and acquisition frequency of the running data;
judging the data type of the driving data according to the acquisition frequency of the driving data;
and judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
2. The method of claim 1, wherein prior to determining the data type of the travel data based on the frequency of collection of the travel data, the method further comprises:
and carrying out data cleaning on the driving data according to a preset rule so as to delete abnormal data.
3. The method of claim 2, wherein the data types include a first type of data, a second type of data;
judging whether the target vehicle has dangerous driving behaviors according to the driving data and the data type of the driving data, wherein the judging step comprises the following steps:
when the driving data is first type data, judging whether the target vehicle has dangerous driving behaviors in first monitoring behaviors or not according to the driving data,
and when the running data is second type data, judging whether the target vehicle has dangerous driving behaviors in second monitoring behaviors or not according to the running data.
4. The method of claim 3, wherein the driving data includes instantaneous speed, location information, and the dangerous driving behavior of the first and second monitoring behaviors each includes: overspeed;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors according to the driving data, both include:
acquiring the road speed limit of a target road driven by the target vehicle according to the positioning information,
acquiring the overspeed threshold value of the target vehicle according to the road speed limit,
and judging whether the target vehicle has overspeed behavior according to the instantaneous speed of the target vehicle and an overspeed threshold value.
5. The method of claim 4, wherein the driving data further includes acceleration, and the dangerous driving behavior of the first monitored behavior further comprises: rapid acceleration and rapid deceleration;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
acquiring a sharp acceleration threshold or a sharp deceleration threshold of the target vehicle according to the instantaneous speed of the target vehicle,
and judging whether the target vehicle has a rapid acceleration behavior or a rapid deceleration behavior according to the rapid acceleration threshold or the rapid deceleration threshold of the target vehicle and the acceleration.
6. The method of claim 5, wherein the driving data further comprises instantaneous angular velocity and vehicle driving trajectory, and wherein the dangerous driving behavior of the first monitoring behavior further comprises: sharp lane and sharp turn;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
judging the driving type of the target vehicle according to the vehicle running track, wherein the driving type comprises lane changing behavior and turning behavior,
obtaining a sharp lane change threshold or a sharp turn threshold of the target vehicle according to the instantaneous speed of the target vehicle,
when the driving type is lane changing behavior, judging whether the target vehicle has lane changing behavior according to the instantaneous angular velocity of the target vehicle and a lane changing threshold,
and when the driving type is turning behavior, judging whether the target vehicle has the sharp turning behavior according to the instantaneous angular speed of the target vehicle and the sharp turning threshold value.
7. The method of claim 6, wherein the driving data further comprises a time window, and the dangerous driving behavior of the first monitored behavior further comprises: driving with instability;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and further comprising:
obtaining the unstable driving index of the target vehicle according to the instantaneous speed of the target vehicle at each acquisition moment in the time window,
the unstable-drivability index is an average of absolute values of speed differences of adjacent instantaneous speeds within the time window,
and judging whether the target vehicle has the unstable driving behavior in the time window or not according to a preset unstable driving behavior threshold value and the unstable driving degree index of the target vehicle.
8. The method of claim 7, wherein the driving data further includes driving time, and the dangerous driving behavior of the first and second monitoring behaviors each further comprises: fatigue driving;
judging whether the target vehicle has dangerous driving behaviors in the first monitoring behaviors according to the driving data, and judging whether the target vehicle has dangerous driving behaviors in the second monitoring behaviors according to the driving data, wherein the method further comprises the following steps:
counting the driving time of the target vehicle in one day according to the driving time,
counting the continuous driving time of the target vehicle according to the driving time,
and if the driving time of the target vehicle in one day exceeds a first preset safe time, or any continuous driving time exceeds a second preset safe time, judging that the fatigue driving behavior of the target vehicle exists in the corresponding time.
9. A dangerous driving behavior monitoring apparatus, characterized in that the apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the running data of a target vehicle and the acquisition frequency of the running data;
the judging module is used for judging the data type of the driving data according to the acquisition frequency of the driving data;
and the analysis module is used for judging whether the target vehicle has dangerous driving behaviors or not according to the driving data and the data type of the driving data.
10. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1-8.
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