CN112550298A - Driving behavior evaluation method, device and storage medium - Google Patents

Driving behavior evaluation method, device and storage medium Download PDF

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Publication number
CN112550298A
CN112550298A CN201910920246.3A CN201910920246A CN112550298A CN 112550298 A CN112550298 A CN 112550298A CN 201910920246 A CN201910920246 A CN 201910920246A CN 112550298 A CN112550298 A CN 112550298A
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time point
change rate
evaluation
average
standard deviation
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CN112550298B (en
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董晓晴
杨琼
王琪
丁奇珑
任维华
童荣辉
饶志明
陆超
沈亮
王斌
徐潘龙
屈肖迪
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SAIC Motor Corp Ltd
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SAIC Motor Corp Ltd
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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
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0604—Throttle position
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/18—Braking system
    • B60W2510/182—Brake pressure, e.g. of fluid or between pad and disc
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/12—Brake pedal position
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

Embodiments of the present application provide a driving behavior evaluation method, device, and storage medium to overcome the above problems. The driving behavior evaluation method comprises the following steps: the method comprises the steps of obtaining driving data of at least one time point of a target driver in the process of driving a vehicle, wherein the driving data of the at least one time point comprises at least one of the vehicle speed, the accelerator opening, the brake pedal opening, the brake pressure and the steering wheel angle of the at least one time point; inputting the driving data of at least one time point into an evaluation model to obtain evaluation data, wherein the evaluation data comprises at least one of the average throttle opening and the average vehicle speed ratio, the standard deviation of the throttle change rate, the standard deviation of the brake pedal opening change rate, the standard deviation of the brake pressure change rate and the standard deviation of the steering wheel angle change rate in at least one period of time; and determining the evaluation information of the target driver according to the evaluation data. The driving behavior of the driver in a period of time can be evaluated, and the evaluation result is more accurate and complete.

Description

Driving behavior evaluation method, device and storage medium
Technical Field
The embodiment of the application relates to the field of data processing algorithms, in particular to a driving behavior evaluation method and device and a storage medium.
Background
As the amount of vehicles kept increases year by year, driving vehicles is becoming more and more important in people's lives. The driving safety is also a topic of general attention because of the close connection with the life and property safety of people. The driving safety and the driving behavior of the driver are closely related, one driver obeys the driving behavior specification, and the probability of the driving accident of the driver with good driving behavior habit is far lower than that of the driver with poor driving behavior habit. Therefore, the driving behavior evaluation has very important reference value for evaluating a driver. In addition, the driving behavior evaluation also has important reference significance for insurance evaluation, vehicle maintenance evaluation, used vehicle evaluation and the like.
Currently, various methods for evaluating driving behavior have been developed, and many of them evaluate driving behavior by counting the number of times of bad acceleration and overspeed. The evaluation method evaluates the violent driving part which can be fed back by the whole vehicle, and cannot evaluate the non-violent driving part which cannot be reflected by the whole vehicle. In addition, the driving behavior is evaluated through the acceleration of the whole vehicle, and the evaluation accuracy is influenced and limited by the accuracy and the error of an acceleration test sensor.
Disclosure of Invention
In view of the above, one of the technical problems to be solved by the embodiments of the present invention is to provide a driving behavior evaluation method, device and storage medium, so as to overcome the above-mentioned problems.
In a first aspect, an embodiment of the present application provides a driving behavior evaluation method, which includes:
the method comprises the steps of obtaining driving data of at least one time point of a target driver in the process of driving a vehicle, wherein the driving data of the at least one time point comprises at least one of the vehicle speed, the accelerator opening, the brake pedal opening, the brake pressure and the steering wheel angle of the at least one time point;
inputting the driving data of at least one time point into an evaluation model to obtain evaluation data, wherein the evaluation data comprises at least one of the average throttle opening and the average vehicle speed ratio, the standard deviation of the throttle change rate, the standard deviation of the brake pedal opening change rate, the standard deviation of the brake pressure change rate and the standard deviation of the steering wheel angle change rate in at least one period of time;
and determining the evaluation information of the target driver according to the evaluation data.
Optionally, in an embodiment of the present application, inputting the driving data of at least one time point into the evaluation model to obtain evaluation data specifically includes:
and inputting the vehicle speed and the accelerator opening at least one time point into an acceleration evaluation model to obtain the average accelerator opening and the average vehicle speed ratio in at least one period, wherein the evaluation model comprises an acceleration evaluation model.
Optionally, in an embodiment of the present application, inputting the vehicle speed and the accelerator opening at least one time point into the acceleration evaluation model to obtain an average accelerator opening and an average vehicle speed ratio for at least one time period, specifically including:
calculating the average vehicle speed of at least one time point according to the vehicle speed of at least one time point; calculating the average throttle opening of at least one time point according to the throttle opening of at least one time point; and calculating the average throttle opening and the average vehicle speed ratio of at least one period according to the average throttle opening of at least one time point and the average vehicle speed of at least one time point.
Optionally, in an embodiment of the present application, inputting the driving data of at least one time point into the evaluation model to obtain evaluation data specifically includes:
and inputting the accelerator opening degree of at least one time point into an accelerator evaluation model to obtain the standard deviation of the accelerator change rate of at least one time period, wherein the evaluation model comprises an accelerator evaluation model.
Optionally, in an embodiment of the present application, inputting the accelerator opening at least one time point into the accelerator evaluation model to obtain a standard deviation of the accelerator change rate for at least one time period, specifically including:
calculating the accelerator change rate of each time point in at least one time point according to the accelerator opening of at least one time point, and obtaining the average accelerator change rate of at least one time point; and calculating the standard deviation of the throttle change rate of at least one period according to the throttle change rate of each time point and the average throttle change rate of at least one time point.
Optionally, in an embodiment of the present application, inputting the driving data of at least one time point into the evaluation model to obtain evaluation data specifically includes:
inputting the opening degree of the brake pedal at least one time point into a brake evaluation model to obtain the standard deviation of the opening degree of the brake pedal in at least one time period;
and/or inputting the brake pressure of at least one time point into a brake evaluation model to obtain the standard deviation of the brake pressure of at least one time period, wherein the evaluation model comprises the brake evaluation model.
Optionally, in an embodiment of the present application, the method further includes:
calculating the brake pedal opening change rate of each time point in at least one time point according to the brake pedal opening of at least one time point, and obtaining the average brake pedal opening change rate of at least one time point; calculating a standard deviation of the brake pedal opening change rate for at least one period of time according to the brake pedal opening change rate of each time point and the average brake pedal opening change rate of at least one time point; and/or calculating the brake pressure change rate of each time point in at least one time point according to the brake pressure of at least one time point, and obtaining the average brake pressure change rate of at least one time point; and calculating the standard deviation of the brake pressure change rate of at least one period according to the brake pressure change rate of each time point and the average brake pressure change rate of at least one time point.
Optionally, in an embodiment of the present application, inputting the driving data of at least one time point into the evaluation model to obtain evaluation data specifically includes:
and inputting the steering wheel angle of at least one time point into a steering evaluation model to obtain the standard deviation of the steering wheel angle change rate of at least one time period, wherein the evaluation model comprises a steering evaluation model.
Optionally, in an embodiment of the present application, the method further includes:
calculating the steering wheel angle change rate of each time point in at least one time point according to the steering wheel angle of at least one time point, and obtaining the average steering wheel angle change rate of at least one time point;
and calculating the standard deviation of the steering wheel angle change rate of at least one period according to the steering wheel angle change rate of each time point and the average steering wheel angle change rate of at least one time point.
Alternatively, in one embodiment of the present application, the evaluation information of the target driver is determined based on the evaluation data. The method specifically comprises the following steps:
comparing at least one of the standard deviation of the steering wheel angle change rate of at least one time period obtained by the steering evaluation model with big data in a database, determining the position of the user in a user group according to the comparison result, and further determining the evaluation information of the target driver.
In a second aspect, an embodiment of the present application provides a driving behavior evaluation device, including: the device comprises an acceleration evaluation module, an accelerator evaluation module, a braking evaluation module, a steering evaluation module and a comprehensive evaluation module.
The acceleration evaluation module is used for outputting the average throttle opening and the average vehicle speed ratio in at least one time period according to the input of the vehicle speed and the throttle opening at least one time point;
the accelerator evaluation module is used for outputting the standard deviation of the accelerator change rate in at least one time period according to the input of the accelerator opening at least one time point;
the brake evaluation module is used for outputting the standard deviation of the brake pedal opening change rate in at least one time period according to the brake pedal opening input of at least one time point;
and/or outputting a standard deviation of a brake pressure change rate for at least one period of time according to the brake pressure input at least one time point;
the steering evaluation module is used for outputting the standard deviation of the steering wheel angle change rate in at least one time period according to the steering wheel angle input of at least one time point;
and the comprehensive evaluation module is used for determining the evaluation information of the target driver.
In a third aspect, the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect or any one of the embodiments of the first aspect is implemented.
According to the embodiment of the application, at least one of driving data of a vehicle speed, an accelerator opening, a brake pedal opening, brake pressure and a steering wheel angle of a driver in the process of driving the vehicle is input into an evaluation model to obtain evaluation data, and evaluation information of a target driver is determined according to the evaluation data. The driving behavior of the driver in a period of time can be evaluated, and the evaluation result is more accurate and complete.
Drawings
Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart of a driving behavior evaluation method provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a driving behavior evaluation device according to an embodiment of the present application.
Detailed Description
The following further describes specific implementation of the embodiments of the present invention with reference to the drawings.
Example one
An embodiment of the present application provides a driving behavior evaluation method, as shown in fig. 1, the driving behavior evaluation method includes the following steps:
step 101, driving data of at least one time point of a target driver in the process of driving a vehicle is acquired.
The driving data of the at least one time point includes at least one of a vehicle speed, an accelerator opening, a brake pedal opening, a brake pressure, and a steering wheel angle of the at least one time point.
Optionally, in an embodiment of the present application, driving data of the target driver at least one point in time during driving of the vehicle is acquired. The method specifically comprises the following steps:
in any time period when a target driver drives a vehicle, driving data of the target driver on a real-time CAN (Controller Area Network) line acquired by a vehicle-mounted terminal is acquired at a preset acquisition frequency.
Here, the frequency of acquiring the driving data is explained in detail: generally, the higher the data acquisition frequency in a time period, the more samples are acquired, and the closer the result obtained after analyzing the samples is to the real result under the condition that the analysis method is correct under the same other conditions. Preferably, in order to improve the accuracy of the evaluation data, the frequency of collecting the driving data of the target driver is set to at least 10hz in the present application. Of course, the value of the acquisition frequency is only used as a reference value and does not represent that the application is limited thereto.
The accelerator opening refers to the opening of an accelerator pedal, namely the relative depth of stepping on the accelerator pedal. For example, the accelerator opening degree is 0 when no external force is applied to the accelerator pedal, and is 1 when an external force is applied to bring the accelerator pedal to the bottom.
The brake pedal opening is the relative depth of depression of the brake pedal. For example, the brake pedal opening degree is 0 in the case where no external force is applied to the brake pedal, and is 1 in the case where the external force is applied to just bottom the brake pedal.
The brake pressure is positively correlated with the opening of the brake pedal, and the larger the brake pressure is, the larger the opening of the brake pedal is; the smaller the brake pressure, the smaller the brake pedal opening. The standard deviation of the brake pressure change rate can be expressed as the standard deviation of the brake pedal opening change rate.
The steering wheel angle is an angle that the steering wheel turns at a certain time with respect to the steering wheel in a certain state. The steering wheel in a specific state is a state in which the steering wheel angle is 0 in a certain state set manually. For example, a state of a steering wheel on a vehicle that normally travels on a straight horizontal road surface may be set to a steering wheel angle of 0 in this state.
And 102, inputting the driving data of at least one time point into an evaluation model to obtain evaluation data.
The evaluation data comprises at least one of the average throttle opening and the average vehicle speed ratio, the standard deviation of the throttle change rate, the standard deviation of the brake opening change rate, the standard deviation of the brake pressure change rate and the standard deviation of the steering wheel rotation angle change rate in at least one period;
optionally, the evaluation model comprises: and accelerating the evaluation model.
The acceleration evaluation model is used for calculating the average speed of at least one time point according to the speed of at least one time point when a target driver drives the vehicle and the data of the opening degree of the accelerator at least one time point; calculating the average throttle opening of at least one time point according to the throttle opening of at least one time point; and calculating the average throttle opening and the average vehicle speed ratio of at least one period according to the average throttle opening of at least one time point and the average vehicle speed of at least one time point.
An accelerated evaluation model data calculation process is illustrated herein. The target driver drives the vehicle in the time period from T1 to Tn, the driving data acquisition frequency is T (unit: Hertz), and the vehicle speed data are respectively: v. of1，v2，vi，...，vn. The data of the accelerator opening degree are respectively as follows: ped1，ped2，pedi，...，pedn. The average vehicle speed at all time points in the time period is:
Figure BDA0002217330680000061
wherein
Figure BDA0002217330680000062
Representing the average vehicle speed at all time points within the time period.
The average throttle opening at all time points in the time period is as follows:
Figure BDA0002217330680000063
wherein
Figure BDA0002217330680000064
Representing the average throttle opening at all time points within the time period.
The average throttle opening and the average vehicle speed ratio of all time points in the time period are as follows:
Figure BDA0002217330680000065
wherein
Figure BDA0002217330680000066
And representing the average throttle opening and the average vehicle speed ratio of all time points in the time period.
Figure BDA0002217330680000067
Can reflect whether the target driver is appropriate to step on the accelerator, if so
Figure BDA0002217330680000068
Too large indicates that the target driver is too hard to pedal the throttle, if
Figure BDA0002217330680000069
Too small indicates that the target driver is not pressing on the accelerator enough.
Figure BDA00022173306800000610
Too large or too small indicates that the target driver is not stepping on the accelerator properly.
Optionally, the evaluation model further comprises: and (4) an accelerator evaluation model.
The accelerator evaluation model is used for calculating the accelerator change rate of each time point in at least one time point according to the accelerator opening data of at least one time point in the process of driving the vehicle by the target driver and obtaining the average accelerator change rate of at least one time point;
and calculating the standard deviation of the throttle change rate of at least one period according to the throttle change rate of each time point and the average throttle change rate of at least one time point.
Here, the accelerator evaluation model data calculation process is exemplified. Setting a target driver to drive a vehicle in a time interval from T1 to Tn, wherein the driving data acquisition frequency is T (unit: Hz), and the accelerator opening data are respectively as follows: ped1，ped2，pedi，...，pedn. The throttle change rate at each time point in the time period is:
Rped_i＝(pedi+1-pedi)×T
wherein R isped_iRepresenting the rate of change of throttle at each point in time during the time period.
The average throttle change rate at all time points over the period is:
Figure BDA00022173306800000611
wherein
Figure BDA00022173306800000612
Representing the average throttle rate of change at all time points over the period.
The standard deviation of the throttle change rate over a period of time is:
Figure BDA0002217330680000071
wherein sigmapedRepresenting the standard deviation of the throttle rate of change over a period of time. The standard deviation represents the dispersion of the throttle opening of the target driver in the period, and can reflect the stability of the gear shifting operation of the target driver in the period.
Optionally, the evaluation model further comprises: and (5) a braking evaluation model.
The brake evaluation model is used for calculating the brake pedal opening change rate of each time point in at least one time point according to the brake pedal opening data of at least one time point in the process of driving the vehicle by the target driver, and obtaining the average brake pedal opening change rate of at least one time point;
and calculating the standard deviation of the brake pedal opening change rate of at least one period according to the brake pedal opening change rate of each time point and the average brake pedal opening change rate of at least one time point.
The brake evaluation model data calculation process is exemplified here. Setting a target driver to drive the vehicle in a time period from T1 to Tn, wherein the driving data acquisition frequency is T (unit: Hz), and the brake pedal opening data are respectively as follows: brk1，brk2，brki，...，brkn. The rate of change of the brake pedal opening at each time point in the time period is:
Figure BDA0002217330680000072
wherein R isped_iRepresenting the rate of change of the brake pedal opening at each point in time during the period.
The average brake pedal opening change rate at all time points in the period is:
Figure BDA0002217330680000073
wherein
Figure BDA0002217330680000074
Represents the average brake pedal opening change rate at all time points within the time period.
The standard deviation of the brake pedal opening change rate in the time period is as follows:
Figure BDA0002217330680000075
wherein sigmabrkRepresenting the standard deviation of the rate of change of the brake pedal opening over the period of time. The standard deviation represents the dispersion of the opening degree of the brake pedal of the target driver in the period, and can reflect the stability of the braking operation of the target driver in the period.
Optionally, the evaluation model further comprises: and turning to an evaluation model.
The steering evaluation model is used for calculating the steering wheel angle change rate of each time point in at least one time point according to the steering wheel angle data of at least one time point in the process of driving the vehicle by the target driver, and obtaining the average steering wheel angle change rate of at least one time point;
and calculating the standard deviation of the steering wheel angle change rate of at least one period according to the steering wheel angle change rate of each time point and the average steering wheel angle change rate of at least one time point.
The steering evaluation model data calculation process is exemplified here. Set target driver at T1 to TnDriving the vehicle in the section, wherein the driving data acquisition frequency is T (unit: Hertz), and the steering wheel corner data are respectively as follows: swa1，swa2，swai，...，swan. The rate of change of the steering wheel angle at each time point in the time period is:
Figure BDA0002217330680000081
wherein R isped_iRepresenting the rate of change of the steering wheel angle at each point in time during the time period.
The average rate of change of the steering wheel angle at all time points in the time period is:
Figure BDA0002217330680000082
wherein
Figure BDA0002217330680000083
Representing the average rate of change of steering wheel angle at all time points in the time period.
The standard deviation of the rate of change of the steering wheel angle over a period of time is:
Figure BDA0002217330680000084
wherein sigmaswaRepresenting the standard deviation of the rate of change of the steering wheel angle over a period of time. The standard deviation represents the discreteness of the steering wheel angle of the target driver in the period, and can reflect the stability of the steering operation of the target driver in the period.
And step 103, determining the evaluation information of the target driver according to the evaluation data.
How to determine the evaluation information of the target driver is specifically explained here: comparing at least one of the standard deviation of the steering wheel angle change rate of at least one time period obtained by the steering evaluation model with big data in a database, determining the position of the user in a user group according to the comparison result, and further determining the evaluation information of the target driver.
The database is the driving behavior data of a certain number of user groups which are counted.
Optionally, in an embodiment of the present application, the evaluation information may be a certain level of a plurality of preset levels, may also be a certain score in a preset score, and may also be a segment of text evaluation, which is not limited in this application.
Here, how to determine the evaluation information of the target driver is exemplified. Setting a target driver as A, and obtaining the average throttle opening and the average vehicle speed ratio a of A in a certain period of time through an acceleration evaluation model1Obtaining the standard deviation a of the accelerator change rate of the time interval of A through an accelerator evaluation model2Obtaining the standard deviation a of the brake pedal opening change rate of the period A through the brake evaluation model3And or the standard deviation of the brake pressure change rate in the period, and the standard deviation a of the steering wheel angle change rate in the period of A is obtained through a steering evaluation model4. A is to1The ratio of average throttle opening to average vehicle speed in big data
Figure BDA0002217330680000091
Comparing a with a2Standard deviation from rate of change of throttle in big data
Figure BDA0002217330680000092
Comparing a with a3Standard deviation from brake pedal opening rate of change in big data
Figure BDA0002217330680000093
Comparing a with a4Standard deviation from rate of change of steering wheel angle in big data
Figure BDA0002217330680000094
The comparison can be a method of making a difference between the two data or making a quotient between the two data to reflect the relative size of the two data, and the like.
Example II,
A second embodiment of the present application provides a driving behavior evaluation device 20, as shown in fig. 2, fig. 2 is a functional schematic diagram of the driving behavior evaluation device 20 provided in the second embodiment of the present application, and the device includes: the system comprises an acceleration evaluation module 201, an accelerator evaluation module 202, a brake evaluation module 203, a steering evaluation module 204 and a comprehensive evaluation module 205.
The acceleration evaluation module 201, the accelerator evaluation module 202, the brake evaluation module 203, the steering evaluation module 204 and the comprehensive evaluation module 205 may be integrated into one evaluation module, which is divided into five virtual modules according to different functions and does not represent the actual hardware structure.
The acceleration evaluation module 201 is used for outputting the average throttle opening and the average vehicle speed ratio in at least one time period according to the vehicle speed and the throttle opening input of at least one time point.
The acceleration evaluation module 201 can implement all functions that can be implemented by the acceleration evaluation model in the first embodiment of the present application.
And the accelerator evaluation module 202 is used for outputting the standard deviation of the accelerator change rate in at least one time period according to the input of the accelerator opening at least one time point.
The accelerator evaluation module 202 can implement all functions that can be implemented by the accelerator evaluation model in the first embodiment of the present application.
And the brake evaluation module 203 is used for outputting the standard deviation of the brake opening degree change rate of at least one time period according to the brake opening degree input of at least one time point.
And/or outputting a standard deviation of a brake pressure change rate for at least one period of time according to the brake pressure input at least one point in time.
The brake evaluation module 203 can implement all functions that can be implemented by the brake evaluation model in the first embodiment of the present application.
And the steering evaluation module 204 is configured to output a standard deviation of the steering wheel angle change rate for at least one time period according to the steering wheel angle input at least one time point.
The steering evaluation module 204 can implement all functions that can be implemented by the steering evaluation model in the first embodiment of the present application.
And the comprehensive evaluation module 205 is used for determining evaluation information of the target driver.
Example III,
A second embodiment of the present application provides a storage medium, where a computer program is stored on the storage medium, and when a processor executes the computer program, the method described in the first embodiment can be implemented.
The storage medium of the embodiments of the present application exists in various forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio, video players (e.g., ipods), handheld game consoles, electronic books, and smart toys and portable car navigation devices.
(4) And other electronic equipment with data interaction function.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The application may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (12)

1. A driving behavior evaluation method characterized by comprising:
acquiring driving data of at least one time point of a target driver in the process of driving a vehicle, wherein the driving data of the at least one time point comprises at least one of vehicle speed, accelerator opening, brake pedal opening, brake pressure and steering wheel angle of the at least one time point;
inputting the driving data of the at least one time point into an evaluation model to obtain evaluation data, wherein the evaluation data comprises at least one of the average throttle opening and the average vehicle speed ratio, the standard deviation of the throttle change rate, the standard deviation of the brake pedal opening change rate, the standard deviation of the brake pressure change rate and the standard deviation of the steering wheel angle change rate of the at least one time period;
and determining the evaluation information of the target driver according to the evaluation data.
2. The method according to claim 1, wherein inputting the driving data at the at least one time point into an evaluation model to obtain evaluation data comprises:
and inputting the vehicle speed and the accelerator opening at the at least one time point into an acceleration evaluation model to obtain the average accelerator opening and the average vehicle speed ratio of the at least one time period, wherein the evaluation model comprises an acceleration evaluation model.
3. The method according to claim 2, wherein inputting the vehicle speed and the throttle opening at the at least one time point into an acceleration evaluation model to obtain an average throttle opening and an average vehicle speed ratio for the at least one time period comprises:
calculating the average vehicle speed of the at least one time point according to the vehicle speed of the at least one time point;
calculating the average throttle opening of the at least one time point according to the throttle opening of the at least one time point;
and calculating the average throttle opening and the average vehicle speed ratio of the at least one time period according to the average throttle opening of the at least one time point and the average vehicle speed of the at least one time point.
4. The method according to claim 1, wherein inputting the driving data at the at least one time point into an evaluation model to obtain evaluation data comprises:
and inputting the accelerator opening degree of the at least one time point into an accelerator evaluation model to obtain the standard deviation of the accelerator change rate of the at least one time period, wherein the evaluation model comprises an accelerator evaluation model.
5. The method according to claim 4, wherein inputting the throttle opening at the at least one time point into a throttle evaluation model to obtain a standard deviation of the throttle change rate for the at least one time period comprises:
calculating the accelerator change rate of each time point in the at least one time point according to the accelerator opening degree of the at least one time point, and obtaining the average accelerator change rate of the at least one time point;
and calculating the standard deviation of the throttle change rate of the at least one period according to the throttle change rate of each time point and the average throttle change rate of the at least one time point.
6. The method according to claim 1, wherein inputting the driving data at the at least one time point into an evaluation model to obtain evaluation data comprises:
inputting the opening degree of the brake pedal of the at least one time point into a brake evaluation model to obtain a standard deviation of the opening degree of the brake pedal of the at least one time period;
and/or inputting the brake pressure of the at least one time point into a brake evaluation model to obtain the standard deviation of the brake pressure of the at least one time period, wherein the evaluation model comprises the brake evaluation model.
7. The method of claim 6, wherein the method further comprises:
calculating the brake pedal opening change rate of each time point in the at least one time point according to the brake pedal opening of the at least one time point, and obtaining the average brake pedal opening change rate of the at least one time point;
calculating a standard deviation of the brake pedal opening change rate of the at least one period according to the brake pedal opening change rate of each time point and the average brake pedal opening change rate of the at least one time point;
and/or calculating the brake pressure change rate of each time point in the at least one time point according to the brake pressure of the at least one time point, and obtaining the average brake pressure change rate of the at least one time point;
and calculating the standard deviation of the brake pressure change rate of the at least one period according to the brake pressure change rate of each time point and the average brake pressure change rate of the at least one time point.
8. The method according to claim 1, wherein inputting the driving data at the at least one time point into an evaluation model to obtain evaluation data comprises:
and inputting the steering wheel angle of the at least one time point into a steering evaluation model to obtain the standard deviation of the steering wheel angle change rate of the at least one time period, wherein the evaluation model comprises a steering evaluation model.
9. The method of claim 8, wherein the method further comprises:
calculating the steering wheel angle change rate of each time point in the at least one time point according to the steering wheel angle of the at least one time point, and obtaining the average steering wheel angle change rate of the at least one time point;
and calculating the standard deviation of the steering wheel angle change rate of the at least one time period according to the steering wheel angle change rate of each time point and the average steering wheel angle change rate of the at least one time point.
10. The method according to claim 1, characterized in that the evaluation information of the target driver is determined on the basis of evaluation data. The method specifically comprises the following steps:
comparing at least one of the standard difference of the steering wheel angle change rate of the at least one time period obtained by the steering evaluation model with big data in a database, determining the position of the user in a user group according to the comparison result, and further determining the evaluation information of the target driver.
11. A driving behavior evaluation device characterized by comprising: the device comprises an acceleration evaluation module, an accelerator evaluation module, a braking evaluation module, a steering evaluation module and a comprehensive evaluation module.
The acceleration evaluation module is used for outputting the average throttle opening and the average vehicle speed ratio of the at least one time period according to the input of the vehicle speed and the throttle opening of the at least one time point;
the accelerator evaluation module is used for outputting the standard deviation of the accelerator change rate of the at least one time period according to the input of the accelerator opening at the at least one time point;
the brake evaluation module is used for outputting the standard deviation of the brake pedal opening change rate of the at least one time interval according to the brake pedal opening input of the at least one time point;
and/or outputting a standard deviation of the brake pressure change rate of the at least one period of time according to the brake pressure input of the at least one time point;
the steering evaluation module is used for outputting the standard deviation of the steering wheel angle change rate of the at least one time period according to the steering wheel angle input of the at least one time point;
and the comprehensive evaluation module is used for determining the evaluation information of the target driver.
12. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, carries out the method according to any one of claims 1-10.
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