CN112937591B - Driving safety monitoring method, device, equipment and computer readable storage medium - Google Patents
Driving safety monitoring method, device, equipment and computer readable storage medium Download PDFInfo
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- CN112937591B CN112937591B CN201911264187.5A CN201911264187A CN112937591B CN 112937591 B CN112937591 B CN 112937591B CN 201911264187 A CN201911264187 A CN 201911264187A CN 112937591 B CN112937591 B CN 112937591B
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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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
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Abstract
The application provides a driving safety monitoring method, a device, equipment and a computer readable storage medium, and the technical scheme is as follows: acquiring a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring period; calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters; calculating monitoring reference values corresponding to a plurality of parameter types respectively according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs; determining a driving safety index of a driver of the target vehicle in a monitoring period according to monitoring reference values respectively corresponding to the multiple parameter categories; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period if the driving safety index is within a predetermined numerical range. The embodiment of the application can achieve the purpose of improving the traveling safety of drivers and pedestrians.
Description
Technical Field
The present disclosure relates to the field of vehicle information processing, and more particularly, to a driving safety monitoring method, device, and apparatus, and a computer-readable storage medium.
Background
With the rapid development of social economy, the urbanization process is accelerated, the frequency of people going out is higher and higher, and the demand on the own transportation means is continuously increased. Therefore, the number of vehicles on the road is increasing. Although the use of the vehicle brings convenience to people for traveling, a lot of traffic pressure is increased at the same time, potential safety hazards are generated, and the road traffic safety situation is increasingly severe. The road traffic safety is influenced by road planning, driving criteria and driving environment, and is closely related to factors such as drivers and vehicles.
In recent years, the number of road traffic accidents is high, and most of the road traffic accidents are caused by the illegal driving behaviors of drivers. The illegal driving behaviors are usually generated due to poor driving consciousness of drivers and poor driving habits. Therefore, it is necessary to enhance the awareness of drivers of driving safely and driving civilized to ensure the safety of road traffic.
Different drivers have different driving habits, some driving habits have larger safety risks, but because the driving habits cannot be expressed quantitatively, a great amount of drivers have lucky psychology, the drivers are difficult to be alerted, the number of driving accidents is high, and the traveling safety of the drivers and pedestrians is damaged.
Disclosure of Invention
The embodiment of the application provides a driving safety monitoring method, a driving safety monitoring device, a driving safety monitoring equipment and a computer readable storage medium, which are used for solving the problems in the related technology, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a driving safety monitoring method, including:
acquiring a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring period; the driving behavior data comprises positioning data and acceleration data collected by a sensor in the target vehicle;
calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters;
calculating monitoring reference values corresponding to a plurality of parameter types respectively according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs; the plurality of parameter categories comprise one or more of a time period category parameter, a duration category parameter, a stability category parameter, a speed category parameter and an environment category parameter;
determining a driving safety index of a driver of the target vehicle in a monitoring period according to monitoring reference values respectively corresponding to the multiple parameter categories; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period if the driving safety index is within a predetermined numerical range.
In one embodiment, the evaluation model corresponding to a first vehicle state variable of the plurality of vehicle state variables is an inverse function of an exponential function integral of the first vehicle state variable.
In one embodiment, the evaluation model corresponding to the first vehicle state parameter is:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a first coefficient, a second coefficient, a third coefficient and ceiling, wherein I is the first vehicle state parameter, S is the monitoring reference value corresponding to the first vehicle state parameter, A is a preset first coefficient, B is a preset second coefficient, C is a preset third coefficient, and ceiling is an rounding-up function.
In one embodiment, the evaluation model corresponding to the first vehicle state parameter is:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a preset first coefficient, obtaining a preset second coefficient, obtaining driving mileage in a monitoring period, and obtaining ceiling function.
In one embodiment, the driving behavior data further comprises speed data and fuel consumption data; the plurality of parameter categories further comprise time period category parameters, duration category parameters and environment category parameters; the plurality of vehicle state parameters comprise oil consumption, and the evaluation model corresponding to the oil consumption is used for determining a monitoring reference value corresponding to the oil consumption according to a numerical value interval to which the oil consumption belongs.
In one embodiment, the time period class parameters include one or more of an early peak driving time period, a late peak driving time period, and a night driving time period;
the duration type parameters comprise the total driving duration and/or the total fatigue driving duration;
the stability parameters comprise the emergency braking times, the emergency acceleration times, the emergency turning times and the oil consumption;
the speed class parameters comprise one or more driving time lengths in a speed interval;
the environmental class parameters include weather and road conditions during driving.
In a second aspect, an embodiment of the present application further provides a driving safety monitoring device, including:
the acquisition module is used for acquiring a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring period; the driving behavior data comprises one or more of positioning data, speed data, acceleration data and fuel consumption data collected by a sensor in the target vehicle;
the first calculation module is used for calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters;
the second calculation module is used for calculating the monitoring reference values corresponding to the multiple parameter types according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs; the plurality of parameter categories comprise one or more of time period category parameters, duration category parameters, stability category parameters, speed category parameters and environment category parameters;
the determining module is used for determining a driving safety index of a driver of the target vehicle in a monitoring period according to the monitoring reference values respectively corresponding to the multiple parameter types; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period if the driving safety index is within a predetermined numerical range.
In one embodiment, the evaluation model corresponding to a first vehicle state variable of the plurality of vehicle state variables is an inverse function of an exponential function integral of the first vehicle state variable.
In a third aspect, an embodiment of the present application provides an apparatus for a driving safety monitoring method, including: the driving safety monitoring system comprises a processor and a memory, wherein instructions are stored in the memory and loaded and executed by the processor so as to realize the driving safety monitoring method provided by any embodiment of the application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the driving safety monitoring method provided in any embodiment of the present application is implemented.
The advantages or beneficial effects in the above technical solution at least include: the driving behavior habit of the driver is reasonably quantized, the obtained driving safety index can intuitively reflect the safety risk of the driver, an explicit warning effect can be formed on the driver, the lucky psychology of the driver is avoided, and the purpose of improving the trip safety of the driver and pedestrians is achieved.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a flowchart of a driving safety monitoring method according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing the relationship between the number of rapid accelerations per kilometer and the accident rate;
FIG. 3 is a schematic diagram showing a relationship between a number of rapid accelerations per kilometer and a monitoring reference value;
FIG. 4 is a flow chart of a driving safety monitoring method according to an embodiment of the present application;
fig. 5 is a block diagram of a driving safety monitoring device according to an embodiment of the present application;
fig. 6 is a block diagram of a device according to an embodiment of the present application.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present application. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
Fig. 1 shows a flow chart of a driving safety monitoring method according to an embodiment of the present application. As shown in fig. 1, the method may include:
step S101, obtaining a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring time period.
The driving behavior data comprises various data capable of reflecting the behavior of the driver in the driving process.
For example, the driving behavior data may include one or more of positioning data, speed data, acceleration data, and fuel consumption data collected by various sensors of the subject vehicle. The data collected by the sensors can reflect the driving state of the vehicle during driving, so that the driving decision of the driver such as rapid acceleration, sharp turning, steady speed driving and the like can be reflected. Illustratively, the positioning data may include longitude and latitude, and the velocity data may include linear velocity and angular velocity; the acceleration data may include longitudinal acceleration, lateral acceleration, and vertical acceleration; the fuel consumption data may include instantaneous fuel consumption. In general, a vehicle includes various sensors for acquiring data, for example, a GPS (Global Positioning System) sensor for acquiring longitude and latitude, a gyroscope for acquiring angular velocity, an accelerometer for acquiring longitudinal acceleration, lateral acceleration, or vertical acceleration, and an oil amount sensor for acquiring instantaneous oil consumption. The data collected by these sensors can be transmitted to a T-BOX (Telematics BOX) device or an OBD (On Board Diagnostics) device. The onboard T-BOX device or the OBD device can upload the data to the Internet of vehicles, so that the driving behavior data can be acquired through the Internet of vehicles.
As another example, the driving behavior data may also include travel time and environment data reflecting the driver's selected behavior with respect to travel time and environment. The environmental data may include weather, road conditions, and the like.
As another example, the driving behavior data may also include status data of electronic devices in the vehicle, such as airbags, windows, door locks, brake pedals, headlamps, and the like.
Using the driving behavior data, vehicle state parameters may be obtained. The vehicle state parameters may include a plurality of parameter categories, such as one or more of a time period category parameter, a duration category parameter, a steadiness category parameter, a speed category parameter, and an environment category parameter. Wherein the time period class parameters may include one or more of an early peak driving time period, a late peak driving time period, and a night driving time period. The duration class parameters may include a total duration of driving and/or a total duration of fatigue driving. The stationary type parameters may include the number of hard brakes, the number of hard accelerations, the number of hard turns, and the amount of oil consumption. The speed class parameters may include one or more driving durations within a speed interval, such as a low speed driving duration, a high speed driving duration. The environment-like parameters may include weather and road conditions during driving.
To improve the objectivity of the driving safety monitoring, the monitoring period may be a period of several weeks or months in duration.
Step S102, calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters.
The monitoring reference value may be used as a reference for evaluating a safety degree corresponding to the vehicle state parameter. For different vehicle state parameters, different evaluation models can be used for calculating monitoring reference values corresponding to the vehicle state parameters. The evaluation model corresponding to the vehicle state parameter may be exemplified as follows:
example one, the evaluation model corresponding to a first vehicle state parameter of the plurality of vehicle state parameters is an inverse function of an exponential function integral of the first vehicle state parameter.
By analyzing a large amount of traffic accident data, the relationship between random behaviors such as rapid acceleration, rapid turning and the like and the accident rate of traffic safety accidents is found to be in exponential distribution. Therefore, the functional relationship between the accident occurrence rate and the vehicle state parameters such as the number of rapid accelerations, the number of rapid turns, and the number of rapid brakes may be an exponential function. For example, a graph of the relationship between the number of rapid accelerations per kilometer and the accident rate shown in fig. 2 can be obtained by counting a large amount of travel data generated by 6000 vehicles over a period of time. The evaluation model can acquire a monitoring reference value for evaluating the safety degree, and the accident occurrence rate and the safety degree are in negative correlation, so that the evaluation model is set as an inverse function of an accident occurrence rate integral result, namely the inverse function of the exponential function integral of the vehicle state parameter, and can be close to the real data condition, and the accuracy of evaluating the safety degree is improved. Fig. 3 is a schematic diagram showing the relationship between the number of rapid accelerations per kilometer and the monitoring reference value.
For example, the evaluation model corresponding to the first vehicle state parameter may be:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a preset first coefficient, obtaining a preset second coefficient, obtaining a preset third coefficient, and obtaining an rounding-up function.
For another example, the evaluation model corresponding to the first vehicle state parameter may be:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a preset first coefficient, obtaining a preset second coefficient, obtaining driving mileage within a monitoring period, and obtaining an rounding-up function. The driving mileage calculation mode is as follows:
L＝∑(v i *(t i -t i-1 ) Wherein v) is i For the speed of the ith speed acquisition node, t i For the ith velocity acquisition node time value, t i-1 And acquiring the time value of the node for the (i-1) th speed.
By collecting a large amount of sample data and training the evaluation model by using the sample data, each coefficient in the evaluation model can be determined.
In example two, the evaluation model corresponding to the second vehicle state parameter in the plurality of vehicle state parameters determines the corresponding monitoring reference value in the numerical range to which the second vehicle state parameter belongs.
For example, the second vehicle state parameter may be an oil consumption amount in a period of time or a period of mileage obtained from the instantaneous oil consumption, and the evaluation model corresponding to the oil consumption amount is a monitoring reference value corresponding to the oil consumption amount determined in a numerical range to which the oil consumption amount belongs. The oil consumption represents the driving smoothness, and the influence of the oil consumption on the driving safety is different from the influence of random behaviors on the driving safety. The accident occurrence rate increases as the oil consumption per unit mileage increases, and therefore, the monitoring reference value corresponding to the oil consumption can be determined by the numerical value to which the oil consumption belongs. For example, the value range of oil consumption per hundred kilometers is divided into 7 ranges of [0,9], [9,10.5], [10.5,14], [14,17], [17,20], [20,22] and [22, + ∞ ] by 9,10.5, 14,17, 20 and 22 liters, and the monitoring reference values corresponding to the ranges are 2, 4, 6, 8, 10, 12, 16 and 20, respectively. And the oil consumption per hundred kilometers is 8, and the corresponding monitoring reference value is 2.
Step S103, calculating monitoring reference values corresponding to a plurality of parameter types according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs.
For example, the monitoring reference values corresponding to the vehicle state parameters in each parameter category may be summed or averaged to obtain the monitoring reference value corresponding to each parameter category. Wherein the summation of the monitoring reference values may be a weighted summation.
In the embodiment, the monitoring reference value is calculated by respectively utilizing the corresponding evaluation models for each vehicle state parameter, so that the driving safety monitoring method can objectively and accurately consider the influence of different vehicle state parameters on the driving safety in different degrees. And the monitoring reference values of the vehicle state parameters of different types are respectively calculated according to the types, so that the influence proportion of the vehicle state parameters of different types on the driving safety is regulated and balanced again, and the objectivity and the accuracy of the driving safety monitoring can be improved.
Step S104, determining a driving safety index of a driver of the target vehicle in a monitoring period according to the monitoring reference values respectively corresponding to the multiple parameter types; the driving safety index is used to determine that the driver of the target vehicle is safely driving within the monitoring period if within a predetermined numerical range.
The driving safety index of the driver of the target vehicle in the monitoring period can be determined according to the numerical value interval to which the mean value of the monitoring reference values respectively corresponding to the multiple parameter categories belongs. For example, the driving safety indexes corresponding to the average values of the monitoring reference values [96, 100], [90, 95], [80, 89], [60, 79], [40 to 59], [20 to 39], [0 to 19] are 0.5, 0.75, 1, 1.5, 2, 3, and 5, respectively. The predetermined numerical range may be greater than 3, and when the driving safety index is greater than 3, it is determined that the driver of the target vehicle is safely driving within the monitoring period.
Carry out reasonable quantization with driver's driving behavior custom, the driving safety index that obtains can reflect driver's safety risk height directly perceivedly, can form clear warning effect to the driver, can avoid the driver to appear the luck psychology, reaches the purpose that improves driver and pedestrian's trip safety.
A specific application example of the embodiment of the present application is provided below.
As shown in fig. 4, the driving safety monitoring method may include:
and step S401, acquiring driving behavior data. The driving behavior data includes driver information, vehicle information, weather information, road condition information, and vehicle sensor data. Specifically, the driving behavior data may include: the vehicle comprises a vehicle frame number, a server data receiving time, a GPS longitude, a GPS latitude, a driving mileage, a data generating time, a vehicle speed, a longitudinal acceleration, a transverse acceleration, a vertical acceleration, an air bag state, a left front wheel tire pressure, a right rear wheel tire pressure, a left rear wheel tire pressure, an instant oil consumption, a left front vehicle window, a right front vehicle window, a left rear vehicle window, a right rear vehicle window, a skylight, a driver side door, a passenger side door, a rear row right door, a rear row left door, a rear door, a driver side door lock, a passenger side door lock, a left rear door lock, a right rear door lock, an engine cabin cover, a trunk lock, a right turning state, a left turning state, a far beam lamp, a low beam lamp, a front fog lamp, a rear lamp, a motor rotating speed, a traction pedal percentage and a brake pedal percentage, wherein the data items accurately mark the change of the vehicle state during driving.
And S402, acquiring various vehicle state parameters by using the driving behavior data.
The vehicle state parameters to be utilized for the driving safety monitoring include 5 parameter classes, 14 vehicle state parameters:
environmental parameters including weather and road conditions;
the stability parameters comprise emergency braking times, emergency acceleration times, emergency turning times and hundred kilometers of oil consumption;
time period parameters including the early peak driving time, the late peak driving time and the night driving time;
speed parameters including low-speed driving time, medium-speed driving time and overspeed driving time;
and the duration parameters comprise the total driving duration and the fatigue driving duration.
The following table 1 lists the manner of obtaining the vehicle state parameters:
TABLE 1
And S403, calculating a monitoring reference value corresponding to each vehicle state parameter.
For the period class parameter:
if the early peak driving time length is greater than 0, the corresponding monitoring reference value S1 is 12-10/ceiling (the early peak driving time length is 5) 0.5; if the early peak driving time period is less than or equal to 0, the corresponding monitoring reference value S1 is 0.
If the late peak driving time length is greater than 0, the corresponding monitoring reference value S2 is 15-12/ceiling (the late peak driving time length is 5) 0.5; if the late peak driving time period is less than or equal to 0, the corresponding monitoring reference value S2 is 0.
If the night driving time is more than 0, the corresponding monitoring reference value S3 is 30-15/ceiling (night driving time 4) ^0.5; if the night driving time is less than or equal to 0, the corresponding monitoring reference value S3 is 0
For the duration class parameter:
if the total driving duration is greater than 0, the corresponding monitoring reference value S4 is 30-28/ceiling (total driving duration 30) ^0.2; if the total driving time is less than or equal to 0, the corresponding monitoring reference value S4 is 0.
If the fatigue driving time is more than 0, the corresponding monitoring reference value S5 is 20-10/ceiling (the fatigue driving time is 30) 0.2; if the fatigue driving time period is less than or equal to 0, the corresponding monitoring reference value S5 is 0.
For the stationary class parameters:
if the number of rapid acceleration times per kilometer is greater than 0, the corresponding monitoring reference value S6 is 15-12/(the number of rapid acceleration times per kilometer multiplied by the travel mileage) ^0.2; if the number of rapid accelerations per kilometer is less than or equal to 0, the corresponding monitoring reference value S6 is 0.
If the number of times of emergency braking per kilometer is greater than 0, the corresponding monitoring reference value S7 is 20-16/(the number of times of emergency braking per kilometer multiplied by the travel mileage) ^0.2; and if the number of sudden braking per kilometer is less than or equal to 0, the corresponding monitoring reference value S7 is 0.
If the number of sharp turns per kilometer is more than 0, the corresponding monitoring reference value S8 is 15-12/number of sharp turns per kilometer multiplied by travel mileage) 0.2; if the number of sharp turns per kilometer is less than or equal to 0, the corresponding monitoring reference value S8 is 0
The value interval of oil consumption per hundred kilometers is divided into 7 intervals by 9 liters, 10.5 liters, 14 liters, 17 liters, 20 liters and 22 liters, which are respectively [0,9], [9,10.5], [10.5,14], [14,17], [17,20], [20,22] and [22, + ∞ ], and monitoring reference values S9 corresponding to the oil consumption per hundred kilometers in each interval are respectively 2, 4, 6, 8, 10, 12, 16 and 20.
For speed class parameters:
if the overspeed driving time length is more than 0, the corresponding first reference S10 value is 30-20/ceiling (the overspeed driving time length is 5; if the overspeed driving time length is less than or equal to 0, the corresponding monitoring reference S10 value is 0
If the low-speed driving time is longer than 0, the corresponding monitoring reference value S11 is 15-12/ceiling (low-speed driving time 10) ^0.2; if the low-speed driving time period is less than or equal to 0, the corresponding monitoring reference value S11 is 0.
S12 is equal to: if the unobstructed driving time is greater than 0, the corresponding monitoring reference value S12 is-12 +8/ceiling (unobstructed driving time 10) ^0.2; if the unobstructed driving period is less than or equal to 0, the corresponding monitoring reference value S12 is 0.
For the environment class parameters:
the monitoring reference value S13 corresponding to the weather and the monitoring reference value corresponding to the road condition may be fixed values 87.
And S404, calculating a monitoring reference value corresponding to each parameter type.
The monitoring reference value score _1 corresponding to the time interval type parameter is 100-round (S1 + S2+ S3).
And monitoring reference values score _2=100-round (S4 + S5) corresponding to the time-length-class parameters.
The stationary parameter corresponds to the monitor reference value score _3=100-round (S6 + S7+ S8+ S9).
The monitoring reference value score _4= score 4 of 100 if round (S10 + S11+ S12) is less than 0, score _4of 100, and score _4of 100-round (S10 + S11+ S12) if round (S10 + S11+ S12) is greater than or equal to 0.
The monitoring reference value score _5=87 corresponding to the environment type parameter.
Where round means rounding off the logarithmic value.
And S404, averaging the monitoring reference values respectively corresponding to the parameter types, and determining the driving safety index according to the numerical value interval to which the average value belongs.
Mean value Total Score = round ((Score _1+ Score_2 + Score_3 + Score_4 + Score_5)/5)
The mean falls within [96, 100], [90, 95], [80, 89], [60, 79], [40, 59], [20, 39] or [0, 19], corresponding to a driving safety index of 0.5, 0.75, 1, 1.5, 2, 3 or 5. The higher the driving safety index, the safer the behavior during driving. Determining that a driver of the target vehicle is safely driven within a monitoring period when the driving safety index is higher than a preset threshold; the driver of the target vehicle may be alerted when the driving safety index is below a preset threshold.
Like this, carry out reasonable quantization with driver's driving behavior custom, the driving safety index that obtains can reflect driver's safety risk height directly perceivedly, can form clear warning effect to the driver, avoids the driver to appear the lucky psychology, reaches the purpose that improves driver and pedestrian's trip safety.
It should be noted that, although the safe driving method is described above by taking specific data as an example, those skilled in the art will understand that the present application should not be limited thereto. In fact, the user is completely flexible in setting various embodiments and coefficients according to personal preferences and/or actual application scenarios.
Fig. 5 shows a block diagram of a driving safety monitoring device according to an embodiment of the present application. As shown in fig. 5, the apparatus may include:
an obtaining module 510, configured to obtain a plurality of vehicle state parameters according to driving behavior data of a target vehicle in a monitoring period; the driving behavior data comprises one or more of positioning data, speed data, acceleration data and fuel consumption data collected by a sensor in the target vehicle;
a first calculating module 520, configured to calculate a monitoring reference value corresponding to each vehicle state parameter by using an evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters;
a second calculating module 530, configured to calculate, according to the monitoring reference value corresponding to each vehicle state parameter and the parameter category to which each vehicle state parameter belongs, monitoring reference values corresponding to multiple parameter categories, respectively; the plurality of parameter categories comprise one or more of a time period category parameter, a duration category parameter, a stability category parameter, a speed category parameter and an environment category parameter;
the determining module 540 is configured to determine a driving safety index of a driver of the target vehicle within a monitoring period according to the monitoring reference values respectively corresponding to the multiple parameter categories; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period if the driving safety index is within a predetermined numerical range.
In one embodiment, the evaluation model corresponding to a first vehicle state variable of the plurality of vehicle state variables is an inverse function of an exponential function integral of the first vehicle state variable.
In one embodiment, the evaluation model corresponding to the first vehicle state parameter is:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a first coefficient, a second coefficient, a third coefficient and ceiling, wherein I is the first vehicle state parameter, S is the monitoring reference value corresponding to the first vehicle state parameter, A is a preset first coefficient, B is a preset second coefficient, C is a preset third coefficient, and ceiling is an rounding-up function.
In one embodiment, the evaluation model corresponding to the first vehicle state parameter is:
the method comprises the following steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a preset first coefficient, obtaining a preset second coefficient, obtaining driving mileage in a monitoring period, and obtaining ceiling function.
In one embodiment, the plurality of vehicle state parameters include oil consumption, and the evaluation model corresponding to the oil consumption determines the monitoring reference value corresponding to the oil consumption in the numerical range to which the oil consumption belongs.
In one embodiment, the time period class parameters include one or more of an early peak driving time period, a late peak driving time period, and a night driving time period;
the duration type parameters comprise the total driving duration and/or the total fatigue driving duration;
the stability parameters comprise the emergency braking times, the emergency acceleration times, the emergency turning times and the oil consumption;
the speed class parameters comprise one or more driving time lengths in a speed interval;
the environment-like parameters include weather and road conditions during driving.
The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.
Fig. 6 shows a block diagram of a device according to an embodiment of the present application. As shown in fig. 6, the apparatus includes: a memory 610 and a processor 620, the memory 610 having stored therein computer programs operable on the processor 620. The processor 620, when executing the computer program, implements the driving safety monitoring method in the above-described embodiment. The number of the memory 610 and the processor 620 may be one or more.
The apparatus further comprises:
the communication interface 630 is used for communicating with an external device to perform data interactive transmission.
If the memory 610, the processor 620 and the communication interface 630 are implemented independently, the memory 610, the processor 620 and the communication interface 630 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
Optionally, in an implementation, if the memory 610, the processor 620, and the communication interface 630 are integrated on a chip, the memory 610, the processor 620, and the communication interface 630 may complete communication with each other through an internal interface.
Embodiments of the present application provide a computer-readable storage medium storing a computer program, which when executed by a processor implements the method provided in the embodiments of the present application.
The embodiment of the present application further provides a chip, where the chip includes a processor, and is configured to call and execute the instruction stored in the memory from the memory, so that the communication device in which the chip is installed executes the method provided in the embodiment of the present application.
An embodiment of the present application further provides a chip, including: the system comprises an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the application.
It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct bus RAM (DR RAM).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the method of the above embodiments may be implemented by hardware that is configured to be instructed to perform the relevant steps by a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module may also be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. A driving safety monitoring method, comprising:
acquiring a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring period; the driving behavior data comprises positioning data and acceleration data collected by sensors in the target vehicle;
calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters;
calculating monitoring reference values corresponding to a plurality of parameter types according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs; the plurality of parameter categories include a stationary category parameter and a speed category parameter;
determining a driving safety index of a driver of the target vehicle in the monitoring period according to the monitoring reference values respectively corresponding to the multiple parameter categories; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period under the condition that the driving safety index is within a preset numerical value range;
wherein the evaluation model corresponding to a first vehicle state parameter of the plurality of vehicle state parameters is:
or the like, or, alternatively,
the method comprises the steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a driving mileage in a monitoring time period, obtaining coefficients A, B and C in an evaluation model corresponding to the first vehicle state parameter, obtaining the coefficients A, B and C in the evaluation model according to collected sample data, and obtaining the parameters A, B and C in the evaluation model according to the collected sample data.
2. The method according to claim 1, wherein the driving behavior data further comprises oil consumption data, the plurality of vehicle state parameters comprise oil consumption, and the evaluation model corresponding to the oil consumption is a value interval to which the oil consumption belongs to determine a monitoring reference value corresponding to the oil consumption.
3. The method of claim 1, wherein the driving behavior data further comprises speed data and fuel consumption data;
the multiple parameter classes further comprise a time period class parameter, a duration class parameter and an environment class parameter;
the time period class parameters comprise one or more of an early peak driving time period, a late peak driving time period and a night driving time period;
the duration type parameters comprise total driving duration and/or total fatigue driving duration;
the stable parameters comprise emergency braking times, emergency acceleration times, emergency turning times and oil consumption;
the speed class parameters comprise driving time length in one or more speed intervals;
the environment type parameters include weather and road conditions during driving.
4. A driving safety monitoring device, comprising:
the acquisition module is used for acquiring a plurality of vehicle state parameters according to the driving behavior data of the target vehicle in the monitoring period; the driving behavior data comprises one or more of positioning data, speed data, acceleration data and fuel consumption data collected by a sensor in the target vehicle;
the first calculation module is used for calculating a monitoring reference value corresponding to each vehicle state parameter by using the evaluation model corresponding to each vehicle state parameter in the plurality of vehicle state parameters;
the second calculation module is used for calculating the monitoring reference values corresponding to the multiple parameter types according to the monitoring reference value corresponding to each vehicle state parameter and the parameter type to which each vehicle state parameter belongs; the plurality of parameter categories comprise one or more of a time period category parameter, a duration category parameter, a stability category parameter, a speed category parameter and an environment category parameter;
the determining module is used for determining a driving safety index of a driver of the target vehicle in the monitoring time period according to the monitoring reference values respectively corresponding to the multiple parameter categories; the driving safety index is used for determining that the driver of the target vehicle is safely driven in the monitoring period under the condition that the driving safety index is within a preset numerical range;
wherein the evaluation model corresponding to a first vehicle state parameter in the plurality of vehicle state parameters is:
or the like, or, alternatively,
the method comprises the steps of obtaining a first vehicle state parameter, obtaining a monitoring reference value corresponding to the first vehicle state parameter, obtaining a driving mileage in a monitoring time period, obtaining coefficients A, B and C in an evaluation model corresponding to the first vehicle state parameter, obtaining the coefficients A, B and C in the evaluation model according to collected sample data, and obtaining the parameters A, B and C in the evaluation model according to the collected sample data.
5. An apparatus for a driving safety monitoring method, characterized by comprising: comprising a processor and a memory, said memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of claims 1 to 3.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-3.
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| CN113283548B (en) * | 2021-07-22 | 2021-10-19 | 天津所托瑞安汽车科技有限公司 | Vehicle safety scoring method, device, equipment and storage medium |
| CN114093199B (en) * | 2021-10-28 | 2023-02-10 | 上海集度汽车有限公司 | Vehicle actuator dynamic monitoring method and device, vehicle and storage medium |
| CN114426025B (en) * | 2022-03-17 | 2023-11-14 | 一汽解放汽车有限公司 | Driving assistance method, driving assistance device, computer device, and storage medium |
| CN115148029B (en) * | 2022-06-29 | 2023-09-19 | 交通运输部公路科学研究所 | Method, medium and electronic equipment for predicting pedestrian traffic accident |
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