CN112793578A - Driving behavior processing method and device and vehicle - Google Patents
Driving behavior processing method and device and vehicle Download PDFInfo
- Publication number
- CN112793578A CN112793578A CN201911115457.6A CN201911115457A CN112793578A CN 112793578 A CN112793578 A CN 112793578A CN 201911115457 A CN201911115457 A CN 201911115457A CN 112793578 A CN112793578 A CN 112793578A
- Authority
- CN
- China
- Prior art keywords
- driving behavior
- index
- driving
- parameter
- total
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Abstract
The present disclosure relates to a driving behavior processing method, a device and a vehicle, wherein the method comprises the steps of obtaining driving behavior data of the vehicle in a preset time period, wherein the driving behavior data comprises one or more driving behaviors; acquiring the occurrence frequency of each driving behavior in the driving behavior data; and acquiring the standard degree of the driving behavior data according to the occurrence frequency of each driving behavior, wherein the standard degree is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving standard. Therefore, the normative degree of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the driving behavior can be accurately analyzed and considered, and reliable data basis can be provided for improving the driving behavior.
Description
Technical Field
The disclosure relates to the technical field of vehicles, in particular to a driving behavior processing method and device and a vehicle.
Background
Along with the extension of the service life of a vehicle, the driving mileage is increased, the phenomenon of vehicle damage is inevitable, and traffic accidents caused by careless driving are common in the driving process of the vehicle. The bad driving behavior is the main reason for causing vehicle damage and traffic accidents, so the proper supervision and examination of the driving behavior of the driver can improve the driving behavior, reduce the traffic accidents and reduce the vehicle damage rate. However, there is no practical and reliable monitoring method available for considering the driving behavior of the driver, so as to provide a reliable data basis for improving the driving behavior of the driver.
Disclosure of Invention
The invention aims to provide a driving behavior processing method and device and a vehicle.
In order to achieve the above object, a first aspect of the present disclosure provides a driving behavior processing method, the method including:
the method comprises the steps of obtaining driving behavior data of a vehicle in a preset time period, wherein the driving behavior data comprise one or more driving behaviors;
acquiring the occurrence frequency of each driving behavior in the driving behavior data;
and acquiring the normalization of the driving behavior data according to the occurrence frequency of each driving behavior, wherein the normalization is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving norm.
Optionally, the obtaining the normative degree of the driving behavior data according to the occurrence number of each driving behavior includes:
acquiring an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior;
and determining the normative degree of the driving behavior data according to the influence parameter index.
Optionally, the influence parameter indexes include an economic index, a safety index and a vehicle loss index, and obtaining the influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior includes:
determining a normative parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior;
calculating to obtain the economic indicator according to the normative parameter of each driving behavior and the economic indicator weight corresponding to each driving behavior;
calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior;
and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
Optionally, the determining the normative parameter corresponding to each driving behavior according to the number of occurrences of each driving behavior and the deduction weight corresponding to each driving behavior includes:
when the driving behavior is determined to be a preset target driving behavior, obtaining the allowable times of the driving behavior;
and determining the normative parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior.
Optionally, the determining the normative degree of the driving behavior data according to the influence parameter index includes:
calculating the sum of the economic indicators corresponding to each driving behavior to obtain the total economic indicator of the driving behavior data;
calculating the sum of safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data;
calculating the sum of vehicle loss indexes corresponding to each driving behavior to obtain a total vehicle loss index of the driving behavior data;
and determining the standard degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
In a second aspect of the present disclosure, there is provided a driving behavior processing apparatus, the apparatus including:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring driving behavior data of a vehicle in a preset time period, and the driving behavior data comprises one or more driving behaviors;
the second acquisition module is used for acquiring the occurrence frequency of each driving behavior in the driving behavior data;
and the third acquisition module is used for acquiring the standardization of the driving behavior data according to the occurrence frequency of each driving behavior, and the standardization is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving standard.
Optionally, the third obtaining module includes:
the obtaining submodule is used for obtaining an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior;
and the determining submodule is used for determining the normalization of the driving behavior data according to the influence parameter index.
Optionally, the influence parameter indicators include an economic indicator, a safety indicator and a vehicle loss indicator, and the obtaining sub-module is configured to:
determining a normative parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior;
calculating to obtain the economic indicator according to the normative parameter of each driving behavior and the economic indicator weight corresponding to each driving behavior;
calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior;
and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
Optionally, the obtaining sub-module is configured to:
when the driving behavior is determined to be a preset target driving behavior, obtaining the allowable times of the driving behavior;
and determining the normative parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior.
Optionally, the determining sub-module is configured to:
calculating the sum of the economic indicators corresponding to each driving behavior to obtain the total economic indicator of the driving behavior data;
calculating the sum of safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data;
calculating the sum of vehicle loss indexes corresponding to each driving behavior to obtain a total vehicle loss index of the driving behavior data;
and determining the standard degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
In a third aspect of the present disclosure, there is provided a vehicle including the driving behavior processing device described in the above second aspect.
According to the technical scheme, the driving behavior data of the vehicle in the preset time period are obtained, wherein the driving behavior data comprise one or more driving behaviors; acquiring the occurrence frequency of each driving behavior in the driving behavior data; and acquiring the normalization of the driving behavior data according to the occurrence frequency of each driving behavior, wherein the normalization is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving norm. Therefore, the normalization of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the driving behaviors can be accurately analyzed and considered, and reliable data basis can be provided for improving the driving behaviors.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a driving behavior processing method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a driving behavior processing method according to another exemplary embodiment of the present disclosure;
fig. 3 is a block diagram of a driving behavior processing apparatus shown in still another exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Before introducing the embodiment of the disclosure, an application scenario of the disclosure is explained, and the disclosure can be applied to a process of monitoring and analyzing and considering the driving behavior of a driver, so that a practical and effective driving behavior analysis method is provided for a manager of a commercial vehicle fleet, the driving behavior habits of the driver are scored, and a reliable data basis is provided for improving the driving behavior. At present, with the improvement of living standard, vehicles become essential products for every family to go out, because the service life of the vehicles is prolonged, the driving mileage is increased, the phenomenon of vehicle damage is inevitable, and traffic accidents caused by careless driving are common in the driving process of the vehicles. The bad driving behavior is the main reason for causing vehicle damage and traffic accidents, so the proper supervision and examination of the driving behavior of the driver can improve the driving behavior, reduce the traffic accidents and reduce the vehicle damage rate. However, there is no practical and reliable monitoring method available for analyzing and considering the driving behavior of the driver, so as to provide a reliable data basis for improving the driving behavior of the driver.
In order to solve the technical problem, the present disclosure provides a driving behavior processing method, a driving behavior processing device and a vehicle, wherein the driving behavior processing method includes acquiring driving behavior data of the vehicle within a preset time period, wherein the driving behavior data includes one or more driving behaviors; acquiring the occurrence frequency of each driving behavior in the driving behavior data; and acquiring the standard degree of the driving behavior data according to the occurrence frequency of each driving behavior, wherein the standard degree is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving standard. Therefore, the normative degree of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the driving behavior can be accurately analyzed and considered, and reliable data basis can be provided for improving the driving behavior.
FIG. 1 is a flow chart illustrating a driving behavior processing method according to an exemplary embodiment of the present disclosure; referring to fig. 1, the method may include the steps of:
Wherein the driving behavior data comprises one or more driving behaviors. The driving behavior may be: the emergency brake system is characterized by comprising one of emergency acceleration, emergency braking, overlong idling, unpredictable running, accelerator stepping in a parking state, cold vehicle running, long-time clutch and long-time braking, immediate flameout during parking, emergency turning, neutral sliding, overspeed running, fatigue driving, engine overrun, no safety belt fastening during running and no turn signal lamp during turning.
It should be noted that the driving behavior data may be acquired and stored by communicating with a vehicle controller via a speed sensor, a position sensor, a temperature sensor, an on-vehicle GPS (Global Positioning System) System, an on-vehicle electronic map, an angle sensor, and a vehicle communication device mounted on the vehicle. For example, the speed, acceleration, deceleration of the vehicle may be determined by a speed sensor; the throttle opening, gear state, engine speed and the like of the vehicle can be determined through data on the vehicle controller; it may be determined by the angle sensor that the vehicle is a steering wheel angle; the coolant temperature may be determined by a temperature sensor; whether the vehicle is in a foreseeable driving state or not can be determined through the vehicle-mounted electronic map; the seat belt status may be determined by a position sensor; the measured position and the current speed may also be determined by the system.
For example, see table 1, which table 1 is a comparison table of driving behavior judgment criteria; when the deceleration of the vehicle is greater than 12km/h/s and the duration is greater than or equal to 1 second, determining that the driving behavior is sudden braking behavior; when the acceleration of the vehicle is more than 6km/h/s and the duration is more than or equal to 1 second, determining that the driving behavior is a rapid acceleration behavior; when the speed of the vehicle is zero, the rotating speed of the engine is 300-850 rpm (revolutions per minute), the temperature of the cooling liquid is greater than or equal to 40 ℃, and the duration time is greater than or equal to 300 seconds, determining that the driving behavior is an overlong idle speed; when the speed of the vehicle is zero, the rotating speed of the engine is greater than 200rpm, the opening degree of the accelerator is greater than or equal to 50%, and the duration is greater than 3 seconds, determining that the driving behavior is the stepping on of the accelerator in a parking state; when the coolant temperature is less than 40 ℃, the vehicle speed is greater than or equal to 30km/h, and the duration is greater than 2 seconds, determining that the driving behavior is cold vehicle running; if the clutch signal is collected for more than 5 seconds, determining that the driving behavior is a long-time clutch behavior; if the braking signal is collected for more than 5 seconds, determining that the driving behavior is a long-time braking behavior; if the time when the vehicle speed is zero is less than or equal to 5 seconds, the state of the vehicle ignition lock is in a closed state, and the driving behavior is determined to be stopping and flameout immediately; if the turning angle of the steering wheel is greater than or equal to 30 degrees and the duration is greater than 3 seconds, determining that the driving behavior is a sharp turn; if the neutral gear signal is obtained for more than 20 seconds and the speed of the vehicle is greater than or equal to 30km/h, determining that the driving behavior is neutral gear sliding; if the vehicle speed exceeds 120km/h for more than 10 seconds, determining that the vehicle is overspeed driving; if the vehicle speed is greater than or equal to 30km/h for 2 continuous hours, determining that the vehicle belongs to fatigue driving behaviors; if the rotating speed of the engine exceeds 3000rpm and the duration is greater than or equal to 5 seconds, determining that the engine overturning behavior is achieved; if the driving behavior is not belted for 60 seconds, determining that the driving behavior is the non-belted behavior; if the corresponding steering lamp signal is not acquired when the turning angle of the steering wheel is greater than or equal to 30 degrees, determining that the driving behavior is that the turning lamp is not turned; if the vehicle enters a predictable driving mode, but no driving signal matching the predictable driving signal is acquired for more than 10 seconds in the predictable driving mode, i.e., the vehicle is artificially exited from the predictable driving mode or a driving action not matching the predictable driving is taken, it is determined that the driving action is an unpredictable driving action.
TABLE 1
And 102, acquiring the occurrence frequency of each driving behavior in the driving behavior data.
In this step, the driving behaviors included in the driving behavior data may be acquired according to the judgment basis described in table 2, and the number of occurrences corresponding to each driving behavior may be counted.
Illustratively, a vehicle with a license plate number of AABBC is given a time of 2019, 10 months, 1 day 0: the driving behavior data of 24:00 of 10 months, 7 days and 2019 years comprise 5 times of overspeed driving, 2 times of emergency acceleration, 1 time of emergency braking and 2 times of neutral sliding.
And 103, acquiring the normative degree of the driving behavior data according to the occurrence frequency of each driving behavior.
And the standard degree is used for representing the matching degree of the driving behavior in the driving behavior data and the preset driving standard.
One possible implementation in this step is: acquiring an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior; and determining the normative degree of the driving behavior data according to the influence parameter index.
It should be noted that the influence parameter index includes: economic indicators, safety indicators and vehicle loss indicators. The influence parameter index can be obtained by the following method:
determining a standard parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior; calculating to obtain the economic index according to the standard parameter of each driving behavior and the economic index weight corresponding to each driving behavior; calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior; and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
The deduction weight is the deduction proportion of the total score of the driving behavior when the driving behavior appears each time; the economic index weight of each driving behavior is used for representing the proportion of the driving behavior in the economic aspect; the safety index weight of each driving behavior is used for representing the proportion of the driving behavior in the aspect of safety; the vehicle loss index weight of each driving behavior is used for representing the proportion of the driving behavior in the aspect of vehicle loss; the deduction weight, the economic index weight, the safety index weight and the vehicle loss index weight corresponding to each driving behavior can be preset according to actual requirements, and the preset mode is more in the prior art, and the disclosure does not limit the deduction weight, the economic index weight, the safety index weight and the vehicle loss index weight. The sum of the economic index weight, the safety index weight and the vehicle loss index weight of all driving behaviors is 1.
After the economic indicator, the safety indicator, and the vehicle loss indicator are acquired in the above manner, the degree of normalization of the driving behavior data is determined through the following steps S11 to S14.
And S11, calculating the sum of the economic indexes corresponding to each driving behavior to obtain the total economic index of the driving behavior data.
Wherein the total economic indicator is used for representing the value of the driving behavior data in the economic aspect.
And S12, calculating the sum of the safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data.
Wherein the total safety index is used for representing the value of the driving behavior data in safety.
And S13, calculating the sum of the vehicle loss indexes corresponding to each driving behavior to obtain the total vehicle loss index of the driving behavior data.
Wherein the total vehicle loss indicator is used to characterize the value of the driving behavior data in terms of vehicle loss.
And S14, determining the normative degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
One possible implementation manner in this step is: and acquiring preset total economic index weight, total safety index weight and total vehicle loss index weight, and determining the standard degree of the driving behavior data according to the total economic index, the total safety index, the total vehicle loss index, the total economic index weight, the total safety index weight and the total vehicle loss index weight. The calculation method of the normalization can refer to the following formula:
the standard degree is the total economic index multiplied by the total economic index weight + the total safety index multiplied by the total safety index weight + the total vehicle loss index multiplied by the total vehicle loss index weight.
Therefore, the normative degree of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the driving behavior can be accurately analyzed and considered, and reliable data basis can be provided for improving the driving behavior.
FIG. 2 is a flow chart illustrating a driving behavior processing method according to another exemplary embodiment of the present disclosure; referring to fig. 2, the method may include the steps of:
Wherein the driving behavior data comprises one or more driving behaviors. The driving behavior may be: the emergency brake system is characterized by comprising one of emergency acceleration, emergency braking, overlong idling, unpredictable running, accelerator stepping in a parking state, cold vehicle running, long-time clutch and long-time braking, immediate flameout during parking, emergency turning, neutral sliding, overspeed running, fatigue driving, engine overrun, no safety belt fastening during running and no turn signal lamp during turning.
And step 203, acquiring an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior.
Wherein the impact parameter index includes: economic indicators, safety indicators and vehicle loss indicators.
It should be noted that the influence parameter index can be obtained through the following steps S21-S24:
and step S21, determining the normative parameters corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior.
The deduction weight is the deduction proportion of the total score of the driving behavior every time the driving behavior appears. The deduction weight corresponding to each driving behavior can be preset according to actual requirements, and the preset mode is more in the prior art, and the deduction weight is not limited by the disclosure.
One possible implementation manner in this step is: and determining whether the driving behavior is a preset target driving behavior, wherein the preset target driving behavior is a driving behavior containing allowable times, and the allowable times are preset according to the supervision and detection requirements. When the driving behavior is a non-preset target driving travel, the specification parameter corresponding to each driving behavior may be determined according to the following formula:
the normalized parameter is the preset total score-the number of occurrences x the deduction weight x the preset total score;
for example, if the sudden braking is a non-preset target driving behavior, the deduction weight of the sudden braking is 10%, 10% of the total sudden braking is deducted every time the sudden braking occurs, and if the full score (preset total score) corresponding to the sudden braking is 100 and the number of occurrences of the sudden braking in certain driving behavior data is 2, the normative parameter corresponding to the sudden braking is 100-2 × 10% × 100 ═ 80.
It should be noted that, if the specification parameter is less than zero, that is, the total score is negative due to too many times of driving behavior, the specification parameter is determined to be zero.
Another possible implementation manner in this step is: when the driving behavior is determined to be the preset target driving behavior, obtaining the allowable times of the driving behavior; and determining the standard parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior. When it is determined that the driving behavior is the preset target driving behavior, determining the normative parameter according to the following formula:
the normalized parameter is preset total score- (occurrence times-allowed times) x deduction weight x preset total score;
it should be noted that the preset target driving behavior may be one or more driving behaviors in table 1. And when the occurrence times are less than the allowed times, determining the specification parameter as a preset total score, when the specification parameter is a negative number, determining the specification parameter as 0, and when the occurrence times are more than the allowed times, calculating the specification parameter according to the calculation formula.
For example, the preset target driving behavior includes engine over-rotation, the driving behavior data includes 5 times of engine over-rotation driving, the number of times of engine over-rotation driving is 3 times, the corresponding deduction weight is 3%, the corresponding preset total score is 100, and the corresponding specification parameters of the engine over-rotation driving are as follows: 100- (5-3). times.3%. times.100.
And S22, calculating the economic index according to the normative parameter of each driving behavior and the economic index weight corresponding to each driving behavior.
The economic indicator weight of each driving behavior is used for representing the proportion of the driving behavior in the economic aspect, the economic indicator weight corresponding to each driving behavior can be preset according to specific consideration requirements, and the economic indicator corresponding to each driving behavior can be determined according to the following formula:
the economic index is the standard parameter multiplied by the economic index weight;
for example, the weight of the economic indicator of sudden braking is 18.7%, and the normative parameter of sudden braking obtained according to the above step S1 is 80 (see the example in step S1 for a specific calculation process), then the economic indicator corresponding to sudden braking is 80 × 18.7%.
And S23, calculating the safety index according to the normative parameter of each driving behavior and the safety index weight corresponding to each driving behavior.
The safety index weight of each driving behavior is used for representing the proportion of the driving behavior in the aspect of safety, the safety index weight corresponding to each driving behavior can be preset according to actual requirements, and the safety index corresponding to each driving behavior can be determined according to the following formula:
the safety index is the standard parameter multiplied by the safety index weight;
for example, if the safety index weight of the sudden braking is 9.9%, and the normative parameter of the sudden braking obtained according to the step S1 is 80%, the economic index corresponding to the sudden braking is 80 × 9.9%.
And S24, calculating the vehicle loss index according to the normative parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
The vehicle loss index weight of each driving behavior is used for representing the proportion of the driving behavior in the aspect of vehicle loss, the vehicle loss index weight corresponding to each driving behavior can be preset according to actual requirements, and the vehicle loss index corresponding to each driving behavior can be determined according to the following formula:
the vehicle loss index is the standard parameter multiplied by the vehicle loss index weight;
for example, if the vehicle loss index weight of the sudden braking is 18.1%, and the specification parameter of the sudden braking obtained in the above step S1 is 80%, the vehicle loss index corresponding to the sudden braking is 80 × 18.1%.
For example, referring to table 2, table 2 is a table of all driving behaviors to be evaluated and analyzed and a parameter setting table corresponding to each driving behavior, which is shown in an exemplary embodiment of the present disclosure. Wherein, 16 driving behaviors are included in total, the preset total of each driving behavior is 100, and the preset target driving behavior comprises overlong idling (allowable times are 3), stepping on the accelerator in a parking state (allowable times are 3), cold vehicle driving (allowable times are 1), long-time clutch (allowable times are 1), long-time brake (allowable times are 1), neutral coasting (allowable times are 3), overspeed driving (allowable times are 3), transmitter overrun (allowable times are 3), and unpredictable driving (allowable times are 3). If the driving behavior data in a certain period of time comprises sudden braking for 1 time, overlength idling for 2 times, long-time braking for 2 times, overspeed driving for 4 times, and the rest times are 0 times, and the preset total of each driving behavior is 100, the corresponding standard parameter of the sudden braking is 100-10% multiplied by 100 which is equal to 90; the occurrence number corresponding to the overlength idling does not exceed the allowable number, so the specification parameter of the overlength idling is 100; the corresponding standard parameter of the long-time brake is 100- (2-1) × 3% × 100 ═ 97; the specification parameter for the speeding is 100- (4-3) × 3% × 100 ═ 97, and the specification parameters for the other driving behaviors are all 100. The total economic indicators corresponding to the driving behavior data are as follows:
90×18.70％+100×13.50％+100×11.50％+100×8.90％+100×9.50％+97×9.30％+97×9.70％+100×10％+100×10％＝98.56。
the total safety indexes corresponding to the driving behavior data are as follows:
90×9.90％+100×8.90％+100×7.20％+100×17.80％+100×7.20％+97×12.70％7+100×13.9％+100×7.70％+100×7.70％+100×7.0％＝98.629。
the total vehicle loss index corresponding to the driving behavior data is as follows:
90×18.10％+100×15.90％+100×15.60％+97×13.00％+100×15.20％+100×12.20％1+100×10.0％＝97.8。
TABLE 2
And step 204, determining the normative degree of the driving behavior data according to the influence parameter index.
One embodiment of this step is: calculating the sum of the economic indexes corresponding to each driving behavior to obtain the total economic index of the driving behavior data; calculating the sum of the safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data; calculating the sum of the vehicle loss indexes corresponding to each driving behavior to obtain the total vehicle loss index of the driving behavior data; and determining the normative degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
Wherein the total economic indicator is used for representing the economic value of the driving behavior data; the total safety index is used for representing the value of the driving behavior data in safety; the total vehicle loss indicator is used to characterize the value of the driving behavior data in terms of vehicle loss.
It should be noted that: the preset total economic indicator weight, total safety indicator weight and total vehicle loss indicator weight can be obtained firstly, and then the normative degree of the driving behavior data is determined according to the total economic indicator, the total safety indicator, the total vehicle loss indicator, the total economic indicator weight, the total safety indicator weight and the total vehicle loss indicator weight. The calculation method of the normalization can refer to the following formula:
the standard degree is the total economic index multiplied by the total economic index weight + the total safety index multiplied by the total safety index weight + the total vehicle loss index multiplied by the total vehicle loss index weight.
For example, still taking the embodiment shown in table 2 as an example, if the total preset economic indicator weight is 40%, the total preset safety indicator weight is 40%, and the total preset vehicle loss indicator weight is 20%, the normalization degree is:
98.56×40％+98.629×40％+97.8×20％＝98.4356。
therefore, the normalization of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the normalization of the driving behavior can be accurately considered, and reliable data basis can be provided for improving the driving behavior.
Fig. 3 is a block diagram of a driving behavior processing apparatus shown in still another exemplary embodiment of the present disclosure; referring to fig. 3, the apparatus includes:
a first obtaining module 301, configured to obtain driving behavior data of a vehicle within a preset time period, where the driving behavior data includes one or more driving behaviors;
a second obtaining module 302, configured to obtain the occurrence frequency of each driving behavior in the driving behavior data;
the third obtaining module 303 obtains the normalization of the driving behavior data according to the occurrence frequency of each driving behavior, where the normalization is used to represent the matching degree between the driving behavior in the driving behavior data and the preset driving norm.
Therefore, the normative degree of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the driving behavior can be accurately analyzed and considered, and reliable data basis can be provided for improving the driving behavior.
Optionally, the third obtaining module 303 includes:
the obtaining submodule 3031 is used for obtaining an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior;
a determining submodule 3032, configured to determine a normalization of the driving behavior data according to the influence parameter indicator.
Optionally, the impact parameter indicators include an economic indicator, a safety indicator and a vehicle loss indicator, and the obtaining submodule 3031 is configured to:
determining a standard parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior;
calculating to obtain the economic index according to the standard parameter of each driving behavior and the economic index weight corresponding to each driving behavior;
calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior;
and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
Optionally, the obtaining submodule 3031 is configured to:
when the driving behavior is determined to be the preset target driving behavior, obtaining the allowable times of the driving behavior;
and determining the standard parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior.
Optionally, the determining submodule 3032 is configured to:
calculating the sum of the economic indexes corresponding to each driving behavior to obtain the total economic index of the driving behavior data;
calculating the sum of the safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data;
calculating the sum of the vehicle loss indexes corresponding to each driving behavior to obtain the total vehicle loss index of the driving behavior data;
and determining the normative degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
Therefore, the normalization of the driving behavior data is obtained according to the occurrence frequency of each driving behavior, the normalization of the driving behavior can be accurately considered, and reliable data basis can be provided for improving the driving behavior.
Yet another exemplary embodiment of the present disclosure shows a vehicle including the driving behavior processing apparatus described above in fig. 3.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (11)
1. A driving behavior processing method, characterized by comprising:
the method comprises the steps of obtaining driving behavior data of a vehicle in a preset time period, wherein the driving behavior data comprise one or more driving behaviors;
acquiring the occurrence frequency of each driving behavior in the driving behavior data;
and acquiring the normalization of the driving behavior data according to the occurrence frequency of each driving behavior, wherein the normalization is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving norm.
2. The method according to claim 1, wherein the obtaining the normative degree of the driving behavior data according to the occurrence number of each driving behavior comprises:
acquiring an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior;
and determining the normative degree of the driving behavior data according to the influence parameter index.
3. The method according to claim 2, wherein the influence parameter indexes comprise an economic index, a safety index and a vehicle loss index, and the obtaining of the influence parameter index corresponding to each driving behavior according to the occurrence number of each driving behavior comprises:
determining a standard parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior;
calculating to obtain the economic index according to the normative parameter of each driving behavior and the economic index weight corresponding to each driving behavior;
calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior;
and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
4. The method of claim 3, wherein the determining the normative parameter for each driving behavior according to the number of occurrences of each driving behavior and the deduction weight for each driving behavior comprises:
when the driving behavior is determined to be a preset target driving behavior, obtaining the allowable times of the driving behavior;
and determining the normative parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior.
5. The method of claim 3 or 4, wherein the determining the degree of normalization of the driving behavior data according to the impact parameter indicator comprises:
calculating the sum of the economic indicators corresponding to each driving behavior to obtain the total economic indicator of the driving behavior data;
calculating the sum of safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data;
calculating the sum of vehicle loss indexes corresponding to each driving behavior to obtain a total vehicle loss index of the driving behavior data;
and determining the standard degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
6. A driving behavior processing apparatus, characterized in that the apparatus comprises:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring driving behavior data of a vehicle in a preset time period, and the driving behavior data comprises one or more driving behaviors;
the second acquisition module is used for acquiring the occurrence frequency of each driving behavior in the driving behavior data;
and the third acquisition module is used for acquiring the standardization of the driving behavior data according to the occurrence frequency of each driving behavior, and the standardization is used for representing the matching degree of the driving behavior in the driving behavior data and a preset driving standard.
7. The apparatus of claim 6, wherein the third obtaining module comprises:
the obtaining submodule is used for obtaining an influence parameter index corresponding to each driving behavior according to the occurrence frequency of each driving behavior;
and the determining submodule is used for determining the normalization of the driving behavior data according to the influence parameter index.
8. The apparatus of claim 7, wherein the impact parameter indicators include an economic indicator, a safety indicator, and a vehicle loss indicator, and the obtaining sub-module is configured to:
determining a normative parameter corresponding to each driving behavior according to the occurrence frequency of each driving behavior and the deduction weight corresponding to each driving behavior;
calculating to obtain the economic indicator according to the normative parameter of each driving behavior and the economic indicator weight corresponding to each driving behavior;
calculating to obtain the safety index according to the standard parameter of each driving behavior and the safety index weight corresponding to each driving behavior;
and calculating to obtain the vehicle loss index according to the standard parameter of each driving behavior and the vehicle loss index weight corresponding to each driving behavior.
9. The apparatus of claim 8, wherein the acquisition sub-module is configured to:
when the driving behavior is determined to be a preset target driving behavior, obtaining the allowable times of the driving behavior;
and determining the normative parameter corresponding to the driving behavior according to the occurrence frequency of the driving behavior, the allowable frequency and the deduction weight corresponding to the driving behavior.
10. The apparatus of claim 8 or 9, wherein the determination submodule is configured to:
calculating the sum of the economic indicators corresponding to each driving behavior to obtain the total economic indicator of the driving behavior data;
calculating the sum of safety indexes corresponding to each driving behavior to obtain the total safety index of the driving behavior data;
calculating the sum of vehicle loss indexes corresponding to each driving behavior to obtain a total vehicle loss index of the driving behavior data;
and determining the standard degree of the driving behavior data according to the total economic index, the total safety index and the total vehicle loss index.
11. A vehicle characterized by comprising the driving behavior processing apparatus according to any one of claims 6 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911115457.6A CN112793578A (en) | 2019-11-14 | 2019-11-14 | Driving behavior processing method and device and vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911115457.6A CN112793578A (en) | 2019-11-14 | 2019-11-14 | Driving behavior processing method and device and vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112793578A true CN112793578A (en) | 2021-05-14 |
Family
ID=75803952
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911115457.6A Withdrawn CN112793578A (en) | 2019-11-14 | 2019-11-14 | Driving behavior processing method and device and vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112793578A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080255722A1 (en) * | 2006-05-22 | 2008-10-16 | Mcclellan Scott | System and Method for Evaluating Driver Behavior |
| CN102055802A (en) * | 2010-12-14 | 2011-05-11 | 杭州鸿泉数字设备有限公司 | Driver behavior monitoring method and device |
| CN106022561A (en) * | 2016-05-05 | 2016-10-12 | 广州星唯信息科技有限公司 | Driving comprehensive evaluation method |
| CN107444404A (en) * | 2017-07-25 | 2017-12-08 | 武汉依迅北斗空间技术有限公司 | A kind of evaluation method and device of driving behavior standard degree |
| WO2019013852A1 (en) * | 2017-07-11 | 2019-01-17 | Siemens Industry, Inc. | Method and system for monitoring and evaluating a performance of a driver of a vehicle |
-
2019
- 2019-11-14 CN CN201911115457.6A patent/CN112793578A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080255722A1 (en) * | 2006-05-22 | 2008-10-16 | Mcclellan Scott | System and Method for Evaluating Driver Behavior |
| CN102055802A (en) * | 2010-12-14 | 2011-05-11 | 杭州鸿泉数字设备有限公司 | Driver behavior monitoring method and device |
| CN106022561A (en) * | 2016-05-05 | 2016-10-12 | 广州星唯信息科技有限公司 | Driving comprehensive evaluation method |
| WO2019013852A1 (en) * | 2017-07-11 | 2019-01-17 | Siemens Industry, Inc. | Method and system for monitoring and evaluating a performance of a driver of a vehicle |
| CN107444404A (en) * | 2017-07-25 | 2017-12-08 | 武汉依迅北斗空间技术有限公司 | A kind of evaluation method and device of driving behavior standard degree |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103700160B (en) | Carried on vehicle terminal and driving behavior determination methods based on microsensor | |
| CA2736855C (en) | Vehicle telemetry system and method for evaluating and training drivers | |
| US9747730B2 (en) | Driver measurement and incentive system for improving fuel-efficiency | |
| CN107226025B (en) | Real-time monitoring method of system for monitoring driving behaviors in real time | |
| CN106097709B (en) | Driving behavior recognition methods based on intelligent vehicle mounted terminal | |
| US9047721B1 (en) | Driver log generation | |
| CN107680204B (en) | Vehicle data processing system for analyzing driving behaviors | |
| US20170103101A1 (en) | System for database data quality processing | |
| EP2714473A2 (en) | Method and apparatus for estimating the fuel consumption of a vehicle | |
| CN107433921B (en) | Vehicle running state monitoring method, device and system | |
| CN105216748A (en) | Realize ofCondition Monitoring System for Vehicle and method | |
| DE10211069A1 (en) | Method and device for providing information | |
| CN110705423A (en) | Real-time driving behavior reminding method and device, electronic equipment and storage medium | |
| US20210031782A1 (en) | Using ISA System to Immobilize Truck for Security, Regulatory Compliance, or Maintenance | |
| CN113442935B (en) | Method and system for judging poor driving behavior of commercial vehicle | |
| CN110723148A (en) | Method and device for identifying bad driving behaviors | |
| CN115689774A (en) | Method and system for optimizing a vehicle event process | |
| CN109493446A (en) | Data processing method and device | |
| CN112793578A (en) | Driving behavior processing method and device and vehicle | |
| US20210031765A1 (en) | Simulating Braking When Speeding on Cruise Control to Facilitate Use of ISA System | |
| CN203659073U (en) | Microsensor-based vehicle-mounted terminal for analyzing driving behavior of motor vehicle driver | |
| KR20180029210A (en) | Premium rate offering system with high reliability | |
| US20200160348A1 (en) | Server device and information processing method | |
| CN115909752B (en) | Method for identifying and counting sharp turns based on historical data of vehicle users | |
| EP3891512B1 (en) | System and method for providing an indication of driving performance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210514 |