CN117985025A - Driving behavior evaluation method, device, equipment and storage medium - Google Patents

Driving behavior evaluation method, device, equipment and storage medium Download PDF

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Publication number
CN117985025A
CN117985025A CN202410170599.7A CN202410170599A CN117985025A CN 117985025 A CN117985025 A CN 117985025A CN 202410170599 A CN202410170599 A CN 202410170599A CN 117985025 A CN117985025 A CN 117985025A
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China
Prior art keywords
driving
user
score
vehicle
behavior
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CN202410170599.7A
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Chinese (zh)
Inventor
黄仕勋
李育方
张波
邓莉婷
黄梅兰
林怡
李金贵
温伟峰
陈子邮
杨世刚
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Dongfeng Liuzhou Motor Co Ltd
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Dongfeng Liuzhou Motor Co Ltd
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Priority to CN202410170599.7A priority Critical patent/CN117985025A/en
Publication of CN117985025A publication Critical patent/CN117985025A/en
Pending legal-status Critical Current

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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
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
    • G07C5/0825—Indicating performance data, e.g. occurrence of a malfunction using optical means
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841—Registering performance data
    • 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

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The application discloses a driving behavior evaluation method, a device, equipment and a storage medium, and relates to the technical field of driving behavior scoring, wherein the method comprises the following steps: acquiring running state data acquired by each electronic control system in the vehicle during running of the vehicle; scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on real driving conditions and user return visits; and generating a driving evaluation based on the driving score, and displaying the driving evaluation to a user. In the application, the driving behavior of the user driving the vehicle is scored by taking the driving state data as a reference by utilizing the preset driving scoring mechanism which is adjusted in real time, so that the situation that the driving scoring cannot be convinced by the user due to the fact that the preset driving scoring mechanism is separated from the working condition of the actual vehicle is avoided, the convincing degree of the user on scoring is increased, and the effect of improving the driving behavior of the user by utilizing the driving behavior scoring is improved.

Description

Driving behavior evaluation method, device, equipment and storage medium
Technical Field
The present application relates to the technical field of driving behavior scoring, and in particular, to a driving behavior scoring method, device, equipment and storage medium.
Background
With the rapid development of the logistics industry, commercial vehicles play a role in the logistics industry, the freight rate of the logistics industry fluctuates greatly at present, and the running cost of the commercial vehicles determines the income of users. The operation condition of the commercial vehicle is complex, the driving habit and the level of the driver are different, the poor driving behavior can increase the fuel consumption of the vehicle, and the operation cost of the vehicle can be directly increased.
In order to better correct bad driving behaviors of drivers, some automobile manufacturers fuse the positioning technology of the vehicle with driving data acquired by an accelerometer, and acquire various driving behaviors from the fused driving state data of the vehicle so as to score the driving behaviors according to the occurrence times of the driving behaviors and the driving mileage of the vehicle. However, after the scoring mechanism is set, the scoring mechanism may deviate from the actual driving condition, so that the user does not notice scoring advice, and the effect of improving the driving behavior of the user is low.
Disclosure of Invention
The application mainly aims to provide a driving behavior evaluation method, a device, equipment and a storage medium, and aims to solve the technical problems that in the prior art, deviation possibly exists between the driving behavior evaluation method, the device, the equipment and the storage medium and actual driving conditions, so that a user cannot score suggestions carelessly, and the effect of improving the driving behavior of the user is low.
In order to achieve the above object, the present application provides a driving behavior evaluation method including:
acquiring running state data acquired by each electronic control system in the vehicle in the running process of the vehicle;
Scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on the actual vehicle working condition and the user return visit;
and generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user.
Optionally, before the step of acquiring the running state data acquired by each electronic control system in the vehicle during running, the method further includes:
Acquiring reference data of driving behaviors of the vehicle under different vehicle working conditions, return visit data of the user and a current scoring mechanism in real time;
and adjusting the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism.
Optionally, the step of adjusting the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism includes:
Determining an initial adjustment scheme of the current scoring mechanism based on the reference data and the return visit data;
acquiring big data of the vehicle working condition;
Adjusting the initial adjustment scheme based on the big data to obtain a final adjustment scheme;
and adjusting the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.
Optionally, the step of determining an initial adjustment scheme of the current scoring mechanism based on the reference data and the return data includes:
sorting the required target driving behaviors and the occurrence times of each target driving behavior under different driving conditions based on the reference data;
analyzing adjustment suggestions of historical users using the current scoring mechanism based on the return visit data;
and determining an initial adjustment scheme of the score and the scoring coefficient corresponding to each driving behavior in the current scoring mechanism based on the target driving behavior, the occurrence frequency and the adjustment suggestion.
Optionally, the step of scoring the driving behavior of the user driving the vehicle based on the preset scoring mechanism and the driving state data to obtain the driving score of the user includes:
Judging whether the driving behavior of the user is bad behavior or not based on the driving state data;
If the bad behavior is the bad behavior, judging whether the bad behavior meets a preset deduction threshold value or not;
If the score and the score coefficient corresponding to the bad behavior are not met, obtaining the score and the score coefficient corresponding to the bad behavior from a preset scoring mechanism;
Determining a score to be increased for the user based on the score and the scoring score;
and adding the added score with the original score of the user to obtain the driving score of the user.
Optionally, after the step of determining whether the bad behavior meets the preset deduction threshold if the bad behavior is the bad behavior, the method further includes:
And if so, subtracting the score corresponding to the bad behavior from the original score to obtain the driving score of the user.
Optionally, if the vehicle is in a fleet mode, the step of generating a driving evaluation based on the driving score and displaying the driving evaluation to the user includes:
generating driving behavior data of the user based on the driving score;
Acquiring the formation of the motorcade;
And generating driving evaluation of each team member of the vehicle team based on the team shape and the driving behavior data, and displaying the driving evaluation to a management user of the vehicle team.
In addition, in order to achieve the above object, the present application also provides a driving behavior evaluation device including:
the first acquisition module is used for acquiring driving state data acquired by each electronic control system in the vehicle in the driving process of the vehicle;
The scoring module is used for scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, and the preset scoring mechanism is adjusted in real time through big data analysis based on the actual driving condition and the user return visit;
And the prompt module is used for generating a driving evaluation based on the driving score and displaying the driving evaluation to the user.
In addition, in order to achieve the above object, the present application also proposes a driving behavior evaluation apparatus including: a memory, a processor and a driving behaviour assessment program stored on the memory and executable on the processor, the driving behaviour assessment program being configured to implement the steps of the driving behaviour assessment method as described above.
In addition, in order to achieve the above object, the present application also proposes a storage medium having stored thereon a driving behavior evaluation program which, when executed by a processor, implements the steps of the driving behavior evaluation method as described above.
Compared with the prior art that deviation possibly exists between the actual driving condition and the actual driving condition, the method, the device and the storage medium for evaluating the driving behavior provided by the application have the advantages that a user cannot score suggestions in mind, and the effect of improving the driving behavior of the user is low, in the method, the driving state data acquired by each electronic control system in the vehicle in the driving process of the vehicle are acquired; scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on the actual vehicle working condition and the user return visit; and generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user. According to the method and the device for grading the driving behavior of the user, the driving behavior of the user driving the vehicle is graded by taking the driving state data as a reference by utilizing the preset driving grading mechanism which is adjusted in real time, so that the situation that the preset driving grading mechanism is separated from the working condition of the actual vehicle and the driving grading cannot lead the user to trust is avoided, the reliability of the grading of the user is improved, and the effect of improving the driving behavior of the user by utilizing the driving behavior grading is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a driving behavior evaluation device in a hardware operating environment according to an embodiment of the present application;
Fig. 2 is a network topology diagram of the driving behavior evaluation apparatus of the present application;
FIG. 3 is a flowchart of a driving behavior evaluation method according to a first embodiment of the present application;
FIG. 4 is a table of scores and coefficients corresponding to a portion of driving behaviors in the economy scoring mechanism in the driving behavior assessment method of the present application;
FIG. 5 is a flowchart of a driving behavior evaluation method according to a second embodiment of the present application;
FIG. 6 is a flowchart of a driving behavior evaluation method according to a third embodiment of the present application;
FIG. 7 is a flowchart of a driving behavior evaluation method according to a fourth embodiment of the present application;
fig. 8 is a schematic structural configuration diagram of the driving behavior evaluation device of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a driving behavior evaluation device in a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the driving behavior evaluation apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a wireless FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the driving behavior evaluation apparatus, and may include more or fewer components than shown, or may combine certain components, or may be a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a driving behavior evaluation program may be included in the memory 1005 as one type of storage medium.
In the driving behavior evaluation device shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the driving behavior evaluation apparatus of the present application may be provided in the driving behavior evaluation apparatus, which invokes the driving behavior evaluation program stored in the memory 1005 through the processor 1001, and executes the driving behavior evaluation method provided by the embodiment of the present application.
Referring to fig. 2, fig. 2 is a network topology diagram of the driving behavior evaluation apparatus of the present application.
As shown in fig. 2, in a specific implementation, the driving behavior evaluation device further includes an electronic control system such as a vehicle-mounted terminal, a vehicle networking platform, a gateway, an EMS (ENGINE MANAGEMENT SYSTEM, an engine management system), a TCU (Transmission Control Unit, a transmission control unit), an ABS (Anti-lock Brake System, an Anti-lock brake system), a meter, and an MMI (Multimedia Interface, a multimedia system), and wires connecting the electronic control systems. Wherein TCU, EMS, ABS, MMI and the instrument are connected with a gateway through wires, the gateway is connected with a vehicle-mounted terminal, and the vehicle-mounted terminal is wirelessly connected with a vehicle networking platform.
In a specific implementation, the vehicle-mounted terminal is used for receiving signals of all parts sent by the gateway, judging whether the vehicle triggers driving behavior events, including events such as long-time idling, large accelerator, low-gear high speed, overhigh rotating speed, economic vehicle speed running, neutral sliding, long-time driving, sudden braking, sharp turning and the like, uploading the events to a vehicle network platform, and simultaneously, sending the driving behavior events to a CAN network (Controller Area Network, a controller area network); and the system is also used for receiving the data of the statistics returned by the Internet of vehicles platform and sending the data to the CAN network. The vehicle networking platform is used for receiving driving behavior event information uploaded by the vehicle-mounted terminal and carrying out statistics and comprehensive scoring; and the method is also used for issuing the statistical data to the vehicle-mounted terminal. The EMS is used for sending signals such as an engine rotating speed signal, an accelerator opening signal, an engine torque mode, a clutch pedal state and the like to the bus. The TCU is used to send the current gear signal of the gearbox to the bus. The gateway is used for forwarding signals required by each part. ABS is used to transmit brake pedal status signals, vehicle speed signals, etc. The MMI is used for receiving the data returned by the vehicle-mounted terminal and displaying the data through graphics. The instrument is used for sending a vehicle speed signal, a speed signal of an output shaft of the gearbox and the like to the bus, and simultaneously prompting a user to correct or maintain the current driving behavior in time by receiving a driving behavior event sent by the vehicle-mounted terminal.
An embodiment of the application provides a driving behavior evaluation method, referring to fig. 3, and fig. 3 is a schematic flow chart of a first embodiment of the driving behavior evaluation method of the application.
It should be noted that, the execution body of the embodiment may be the driving behavior evaluation device, and the driving behavior evaluation device may be an electronic device such as a personal computer, a smart phone, a tablet computer, etc. connected to the internet of vehicles and extended, or may be other devices capable of implementing the same or similar functions, which is not limited in this embodiment, and in the present embodiment and the following embodiments, the driving behavior evaluation method of the present application is described by taking the driving behavior evaluation device as an example.
In this embodiment, the driving behavior evaluation method includes:
step S10, acquiring running state data acquired by each electronic control system in the vehicle during running.
Each electronic control system may include a vehicle terminal, EMS, TCU, ABS, an instrument, an MMI, and the like, which is not particularly limited.
The driving state data comprises, but is not limited to, signals such as an engine rotating speed signal, an accelerator opening degree signal, an engine torque mode, a clutch pedal state, a transmission current gear signal, a brake pedal state signal, a vehicle speed signal, a transmission output shaft rotating speed and the like.
It should be noted that, since the engine speed and the vehicle speed can be used to determine whether the vehicle is idling for a long time; the throttle opening, the engine rotating speed, the engine torque mode and the vehicle speed signal can be used for judging the throttle opening; the current gear of the gearbox, the current acceleration of the vehicle, the opening of the accelerator, the engine speed and the economic speed corresponding to the engine model can be used for judging whether the vehicle is in a low-gear high-speed state or not; the rotating speed of the engine can be judged to be too fast through the current rotating speed of the engine, the economic rotating speed of the engine model and the opening degree of the accelerator; the vehicle can be judged whether to be in a neutral sliding state or not through the vehicle speed, the clutch pedal state, the engine torque request mode, the engine rotating speed and the transmission ratio of the gearbox, so that the running state data can be acquired in a targeted manner through each electronic control system.
If the other driving behaviors need to be judged, the other driving state data acquired by the corresponding electronic control system can be acquired, so that the user can judge the required driving behaviors according to the requirements.
In the specific implementation, the travel state data acquired by each electronic control system is forwarded to the vehicle-mounted terminal by using the gateway in the vehicle running process, and finally the travel state data uploaded by the vehicle-mounted terminal is received, so that the driving behavior of the user is scored according to the travel state data.
And step S20, scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on the actual driving condition and the user return visit.
The preset scoring mechanism may be further divided into an economy scoring mechanism and a security scoring mechanism.
The driving behavior may include long-time idling, large accelerator, low gear high speed, too high rotational speed, economical vehicle speed running, neutral coasting, and the like, which is not particularly limited.
It should be noted that, the driving data and the driving score obtained by the economy scoring mechanism can prompt the user to drive in a standardized way so as to reduce the oil consumption of the vehicle and reduce the transportation cost of the vehicle; the driving data and the driving score obtained through the safety scoring mechanism can prompt the user to safely drive the vehicle.
It should be noted that, when the preset scoring mechanism is initially set, the preset scoring mechanism is generally set according to theoretical data after investigation, and because the driving experiences of users are different when driving different vehicles and the driving conditions are also different, the situation that the final scoring cannot truly reflect the driving behavior of the users due to the fact that the scoring mechanism set after investigation deviates from the actual driving conditions exists, so that the users cannot trust the scoring, and in order to enable the preset scoring mechanism to reasonably reflect the driving behavior of the users according to the actual driving conditions, the preset scoring mechanism can be adjusted in real time according to the user revisions and the actual driving conditions, and the adjustment can be fine adjustment, so that the preset scoring mechanism is more reasonable.
In a specific implementation, the scoring of the driving behavior of the user by the economy scoring mechanism may be that when the engine speed of the vehicle is in an idle state, the vehicle speed is kept at 0km/h, and the battery voltage and the water temperature are in a normal interval for a predetermined time, the vehicle-mounted terminal determines that the vehicle is idle for a long time, at this time, the meter prompts the user to stop the vehicle for waiting, and starts to count time, after the predetermined time, the vehicle is still in an idle state, and then the scoring is performed according to the corresponding score of the idle for the long time in fig. 4, that is, S N＝SN-1 -a. If the vehicle is in a flameout state within the predetermined time, S N＝SN-1+α1 a. If no long-time idle event occurs in the driving process, S N＝SN-1 +A is carried out. Wherein S N is the current driving score of the user, S N-1 is the last driving score, km/h is the unit kilometer per hour of speed.
When the opening of the accelerator of the vehicle is larger than a set value, the vehicle-mounted terminal judges whether a large accelerator event is triggered by combining an engine rotating speed signal, an engine torque mode and a meter speed signal, and when the large accelerator event is triggered, the meter prompts a user to reduce the accelerator in time. Within a specified mileage (e.g., 100 km), the number of times of triggering the large throttle event exceeds a specified number of times (e.g., 5 times), the large throttle is scored according to the large throttle corresponding score in fig. 4, that is, S N＝SN-1 -B. If the trigger times of the large throttle event is less than the prescribed times (such as 2 times) within the prescribed mileage, S N＝SN-1+α2 B is performed; if the vehicle does not trigger a large throttle event within the specified mileage, S N＝SN-1 +B.
The vehicle-mounted terminal monitors signals such as the current gear of the gearbox, the current acceleration of the vehicle, the opening degree of the accelerator, the engine speed and the like at intervals, judges whether the vehicle is in a low gear and a high speed according to the economic speed of the engine model, triggers an event if the vehicle is in the low gear and the high speed, and prompts a user to upshift or reduce the accelerator. When the number of low-grade high-speed event triggering times exceeds the specified number (e.g. 5) or the continuous low-grade high-speed triggering times exceeds the specified time (e.g. 10 min) within the specified mileage (e.g. 100 km), the scoring is performed according to the low-grade high-speed corresponding score in fig. 4, namely S N＝SN-1 -C. If the trigger times of the low-gear high-speed events are less than the prescribed times (such as 2 times) within the prescribed mileage, S N＝SN-1+α3 C; if the low-gear high-speed event is not triggered within the specified mileage, S N＝SN-1 +C.
When the vehicle-mounted terminal monitors that the current rotating speed of the engine is larger than a certain value of the economic rotating speed of the engine model, the opening degree of the accelerator is synchronously judged, whether an event with overhigh rotating speed is triggered or not is comprehensively judged, and when the event is triggered, the instrument prompts a user to timely reduce the accelerator. In a predetermined mileage (e.g. 100 km), when the number of times of triggering the over-rotation event exceeds a predetermined number of times (e.g. 5 times) or the duration of triggering the over-rotation event exceeds a predetermined time (e.g. 10 min), the score is based on the corresponding score of the over-rotation in fig. 4, that is, S N＝SN-1 -D. If the trigger frequency of the event with the too high rotating speed is less than the prescribed frequency (such as 2 times) in the prescribed mileage, sn=s N-1+α4 D; if the too high rotation speed event is not triggered in the specified mileage, S N＝SN-1 +D is performed.
When the vehicle-mounted terminal monitors that the vehicle is in an economic speed interval, an economic speed driving event is triggered, the instrument prompts a user to recommend to keep the vehicle speed driving, and after the vehicle keeps the economic speed driving T1 (S), the vehicle-mounted terminal scores according to the corresponding score of the economic speed driving in the figure 4, namely S N＝SN-1+α5 E, and after the vehicle keeps the economic speed driving T2 (S), S N＝SN-1+α5(1+α5) E (T2 is more than T1).
When the vehicle speed is greater than a certain set value, the vehicle-mounted terminal judges signals such as a clutch state, an engine torque request mode, an engine rotating speed, a gearbox transmission ratio and the like, and confirms whether the vehicle slides in neutral. When a neutral coast event is triggered, the meter prompts the user that neutral coast is taking care of safety. Meanwhile, the corresponding score of the hollow gear sliding is scored according to the figure 4, namely S N＝SN-1+α6 F.
In a specific implementation, the scoring of the driving behavior of the user by the security scoring mechanism may be, for example, neutral coasting, which is an economical driving behavior, and also a driving behavior with risk, and the event is triggered by suggesting that the user pay attention to security, for example, if the user triggers too frequently (for example, 10 km 5 times) in a certain trip, and the security score is reduced. If the triggering times are smaller than the specified value or not triggered in a certain stroke, the safety score is increased. When the vehicle-mounted terminal detects that the rotation angle of the vehicle is larger than theta (such as 15 degrees) and the vehicle speed is larger than a set value (such as 30 km/h) continuously for N times, the vehicle-mounted terminal judges that the vehicle turns suddenly, a sudden turning event is triggered at the moment, and the instrument can promote the user to pay attention to the sudden turning and recommend the user to slow down. If the sharp turn event is triggered more than a specified number of times within a certain trip, the security score decreases. If the number of times of the sharp turning event triggering is smaller than a specified value or is not triggered in a certain stroke, the safety score is increased. The driving behaviors needing to be scored through the security scoring mechanism can also comprise emergency braking, emergency turning and other security behavior events, and the scoring method of the emergency braking, the emergency turning and other security behavior events is basically consistent with the scoring method of neutral gear sliding.
And step S30, generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user.
It should be noted that, an improvement suggestion may be generated according to the driving score and the corresponding score data, and the improvement suggestion, the driving score and the score data are summarized into a driving score, and the driving score is displayed to the user through the MMI or the mobile terminal, so that the user may improve the driving behavior in time according to the driving score.
It should be noted that, in order to avoid that the driving behavior event is triggered too frequently under certain working conditions, so as to cause the user to feel objectionable, the instrument increases the driving behavior evaluation prompt switch, and the user can select to close all or close the prompt of a certain event.
In a specific implementation, an improvement suggestion is generated according to a driving score and corresponding score data, the improvement suggestion, the driving score and the score data are assembled into a driving score, the driving score is displayed to a user through an MMI if an evaluation prompt switch of the instrument is on, and the driving score can be sent to an associated mobile terminal if the evaluation prompt switch of the instrument is off, so that the user can check through the mobile terminal when resting.
Compared with the prior art that the driving behavior is possibly deviated from the actual driving condition, so that a user cannot score suggestions in mind, and the effect of improving the driving behavior of the user is low, the driving behavior evaluation method provided by the embodiment of the application is used for acquiring the driving state data acquired by each electronic control system in the vehicle in the driving process of the vehicle; scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on the actual vehicle working condition and the user return visit; and generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user. According to the method and the device for grading the driving behavior of the user, the driving behavior of the user driving the vehicle is graded by taking the driving state data as a reference by utilizing the preset driving grading mechanism which is adjusted in real time, so that the situation that the preset driving grading mechanism is separated from the working condition of the actual vehicle and the driving grading cannot lead the user to trust is avoided, the reliability of the grading of the user is improved, and the effect of improving the driving behavior of the user by utilizing the driving behavior grading is improved.
Referring to fig. 5, fig. 5 is a flowchart illustrating a driving behavior evaluation method according to a second embodiment of the present application.
Based on the foregoing embodiment, in this embodiment, before the step of acquiring the running state data acquired by each electronic control system in the vehicle during running of the vehicle, the method further includes:
Step S01, reference data of driving behaviors of a vehicle under different vehicle working conditions, return visit data of the user and a current scoring mechanism are obtained in real time;
step S02, the current scoring mechanism is adjusted based on the reference data and the return visit data, and the latest preset scoring mechanism is obtained.
The reference data can be data recorded by each electronic control system when different vehicles run normally under different vehicle working conditions.
The current scoring mechanism may be understood as a scoring mechanism currently in use, that is, a preset scoring mechanism latest after the last adjustment.
The return visit data may include a score of a certain driving behavior being too high or too low, or a score threshold of a certain driving behavior being too high or too low, etc.
It should be noted that, the current scoring mechanism is adjusted by using the reference data of the driving behavior under different driving conditions and the return visit data of the user, so that the preset scoring mechanism can conform to the wish of the user, the final driving scoring can be more reasonable, the user can trust more easily, and the user can wish to improve the driving behavior according to the driving rating.
It should be noted that, because the preset scoring mechanism is a scoring mechanism, in order to avoid frequent adjustment of the preset scoring mechanism, the return visit data may be set, and the adjustment accuracy may also be set, for example, when the return visit data may be set to a certain number (100) and the adjustment accuracy meets a certain accuracy range (the adjustment accuracy needs to be greater than or equal to 0.1), the preset scoring mechanism is adjusted. The setting of the return visit data can avoid frequent adjustment of a preset scoring mechanism, data extreme caused by too few return visit data can also be avoided, and the setting of the adjustment precision can also avoid frequent adjustment of the preset scoring mechanism, so that the reduction of the scoring reference value of the preset scoring mechanism is avoided by avoiding frequent adjustment of the preset scoring mechanism and the extreme of the used data.
In specific implementation, reference data recorded by each electronic control system, return visit data of a user and a preset scoring mechanism currently used under driving behaviors of a vehicle under different vehicle working conditions are obtained in real time, the score and scoring coefficient required to be adjusted for each driving behavior are determined by utilizing the reference data and the return visit data, or a scoring threshold required to be adjusted for each driving behavior is determined, and the current scoring mechanism is adjusted according to the score and the scoring coefficient required to be adjusted or the scoring threshold required to be adjusted, so that the latest preset scoring mechanism is obtained.
Optionally, the step of adjusting the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism includes:
Step S021, determining an initial adjustment scheme of the current scoring mechanism based on the reference data and the return visit data;
Step S022, acquiring big data of the working condition of the vehicle;
step S023, adjusting the initial adjustment scheme based on the big data to obtain a final adjustment scheme;
And step S024, adjusting the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.
It should be noted that, since the reference data and the return visit number cannot represent all users, after the initial adjustment scheme of the current scoring mechanism is determined according to the reference data and the return visit data, big data of the vehicle working condition needs to be obtained, so that the final adjustment scheme of all users can be represented through big data analysis, and all users can be used as much as possible by the current scoring mechanism adjusted according to the final adjustment scheme.
In specific implementation, the times of driving behaviors needed by each vehicle working condition to different driving behaviors are sorted according to the reference data, adjustment suggestions of the user for scoring each driving behavior are analyzed according to the return visit data, an initial adjustment scheme of a current scoring mechanism is determined according to the driving behaviors needed by each vehicle working condition, the times of different driving behaviors and the adjustment suggestions, the initial adjustment scheme is adjusted according to big data of each acquired vehicle working condition, a final adjustment scheme is obtained, and finally the current scoring mechanism is adjusted according to the final adjustment scheme, so that the latest preset scoring mechanism is obtained.
Further, the step of determining an initial adjustment scheme for the current scoring mechanism based on the reference data and the return data includes:
Step S0211, sorting the required target driving behaviors and the occurrence times of each target driving behavior when passing through different driving working conditions based on the reference data;
Step S0212, analyzing the adjustment suggestions of the historical users using the current scoring mechanism based on the return visit data;
step S0213, based on the target driving behavior, the occurrence times and the adjustment advice, determining an initial adjustment scheme of the score and the scoring coefficient corresponding to each driving behavior in the current scoring mechanism.
In a specific implementation, the times of driving behaviors needed by each vehicle working condition to pass through different driving behaviors are sorted according to the reference data, and the adjustment suggestions of the user for scoring each driving behavior are analyzed according to the return visit data, so that the initial adjustment scheme of the corresponding score and the scoring coefficient of the driving behavior in the current scoring mechanism is determined according to the driving behaviors needed by each vehicle working condition, the times of different driving behaviors and the adjustment suggestions.
Referring to fig. 6, fig. 6 is a flowchart illustrating a third embodiment of the driving behavior evaluation method according to the present application.
Based on the above embodiment, in this embodiment, the step of scoring the driving behavior of the user driving the vehicle based on the preset scoring mechanism and the driving state data to obtain the driving score of the user includes:
Step S21, judging whether the driving behavior of the user is bad behavior or not based on the driving state data;
Step S22, if the bad behavior is the bad behavior, judging whether the bad behavior meets a preset deduction threshold value or not;
step S23, if not, obtaining the score and the scoring coefficient corresponding to the bad behavior from a preset scoring mechanism;
Step S24, determining a score to be increased of the user based on the score and the scoring score;
and S25, adding the added score to the original score of the user to obtain the driving score of the user.
After the bad driving behavior of the user is judged, judging that the bad driving behavior meets the withholding threshold, if the bad driving behavior does not meet the withholding threshold, adding an encouraging score to the original score of the user so as to encourage the user to continuously improve, improving the driving behavior information of the user and increasing the driving behavior improving power of the user.
In specific implementation, judging whether the driving behavior of the user is bad behavior according to the driving state data, if not, adding the score of the driving behavior pair in a preset scoring mechanism to the original score of the user to obtain the driving score of the user; if the driving behavior of the user is bad behavior, judging whether the bad behavior meets a preset deduction threshold again, if not, determining an increase score according to the corresponding score and the score coefficient, and adding the original score and the increase score to obtain the driving score of the user so as to encourage the user.
Further, after the step of determining whether the bad behavior meets the preset deduction threshold if the bad behavior is the bad behavior, the method further includes:
And step S23a, if the driving score is satisfied, subtracting the score corresponding to the bad behavior from the original score to obtain the driving score of the user.
In a specific implementation, if the bad behavior does not meet a preset deduction threshold in a certain time or a certain stroke, the score corresponding to the bad behavior is decelerated by the original score, and the driving score of the user is obtained, so that the user is warned to improve the driving behavior.
Referring to fig. 7, fig. 7 is a flowchart illustrating a driving behavior evaluation method according to a fourth embodiment of the present application.
Based on the above embodiment, in this embodiment, if the vehicle is in a fleet mode, the step of generating a driving evaluation based on the driving score and displaying the driving evaluation to the user includes:
Step S1, generating driving behavior data of the user based on the driving score;
s2, acquiring the formation of the motorcade;
And step S3, generating driving evaluation of each team member of the motorcade based on the formation and the driving behavior data, and displaying the driving evaluation to a management user of the motorcade.
If the vehicle is in the fleet, the fleet mode is started, and when the driving evaluation is displayed to the user, the driving evaluation of all the teams in the fleet can be displayed to the fleet management user, so that the management user can more clearly check the operation condition of the fleet, correct the driver with lower driving level in time, and facilitate the fleet management.
It should be noted that, when vehicles in different positions of the same fleet pass through the section road, the corresponding working conditions are different, for example, when the fleet encounters and turns, the front part of the fleet may not have vehicles or is far away from the front vehicle, the situation that the front part of the fleet is led to the vehicles need not be considered when the user of the vehicle turns, the relative driving behavior may be less, the user of the vehicle in the middle of the fleet needs to consider the rear vehicle and the front vehicle when the user of the vehicle in the middle of the fleet turns, therefore, the relative driving behavior may be more, the user of the last vehicle at the tail part of the fleet does not need to consider the rear vehicle, and the driving behavior of the user also has deviation from the driving behavior of the first user of the fleet, so when the vehicle is in the fleet mode, the shape of the fleet can also be used as a scoring reference, and the scoring is more suitable for the fleet at the moment.
The present application also provides a driving behavior evaluation device, referring to fig. 8, the driving behavior evaluation device includes:
a first obtaining module 801, configured to obtain driving state data collected by each electronic control system in a vehicle during a driving process of the vehicle;
The scoring module 802 is configured to score a driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data, and obtain a driving score of the user, where the preset scoring mechanism is adjusted in real time through big data analysis based on a real driving condition and a user return visit;
and the prompting module 803 is used for generating a driving evaluation based on the driving score and displaying the driving evaluation to the user.
Optionally, the driving behavior evaluation device further includes:
The second obtaining module 804 is configured to obtain, in real time, reference data of driving behavior of the vehicle under different vehicle working conditions, return visit data of the user, and a current scoring mechanism;
and an adjusting module 805, configured to adjust the current scoring mechanism based on the reference data and the return visit data, so as to obtain the latest preset scoring mechanism.
Optionally, the adjusting module 805 is further configured to determine an initial adjustment scheme of the current scoring mechanism based on the reference data and the return data; acquiring big data of the vehicle working condition; adjusting the initial adjustment scheme based on the big data to obtain a final adjustment scheme; and adjusting the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.
Optionally, the adjusting module 805 is further configured to sort, based on the reference data, the target driving behaviors required by different driving conditions and the occurrence times of each of the target driving behaviors; analyzing adjustment suggestions of historical users using the current scoring mechanism based on the return visit data; and determining an initial adjustment scheme of the score and the scoring coefficient corresponding to each driving behavior in the current scoring mechanism based on the target driving behavior, the occurrence frequency and the adjustment suggestion.
Optionally, the scoring module 802 is further configured to determine whether the driving behavior of the user is bad behavior based on the driving state data; if the bad behavior is the bad behavior, judging whether the bad behavior meets a preset deduction threshold value or not; if the score and the score coefficient corresponding to the bad behavior are not met, obtaining the score and the score coefficient corresponding to the bad behavior from a preset scoring mechanism; determining a score to be increased for the user based on the score and the scoring score; and adding the added score with the original score of the user to obtain the driving score of the user.
Optionally, the scoring module 802 is further configured to subtract the score corresponding to the bad behavior from the original score if the score is satisfied, to obtain a driving score of the user.
Optionally, if the vehicle is in a fleet mode, the prompting module 803 is further configured to generate driving behavior data of the user based on the driving score; acquiring the formation of the motorcade; and generating driving evaluation of each team member of the vehicle team based on the team shape and the driving behavior data, and displaying the driving evaluation to a management user of the vehicle team.
The specific embodiment of the driving behavior evaluation device of the present application is basically the same as the embodiments of the driving behavior evaluation method described above, and will not be described herein again.
The embodiment of the application provides a storage medium, and the storage medium stores one or more programs, and the one or more programs are further executable by one or more processors to implement the steps of the driving behavior evaluation method of any one of the above.
The specific embodiment of the storage medium of the present application is substantially the same as the embodiments of the driving behavior evaluation method described above, and will not be described herein.
It should be noted that, in this document, 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 a" or "comprising an" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method of the embodiments of the present application.
The foregoing description of the preferred embodiments of the present application should not be taken as limiting the scope of the application, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the application as defined by the following description and drawings.

Claims (10)

1. A driving behavior evaluation method, characterized by comprising:
acquiring running state data acquired by each electronic control system in the vehicle in the running process of the vehicle;
Scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, wherein the preset scoring mechanism is adjusted in real time through big data analysis based on the actual vehicle working condition and the user return visit;
and generating a driving evaluation based on the driving score, and displaying the driving evaluation to the user.
2. The driving behavior evaluation method according to claim 1, wherein the step of acquiring the running state data acquired by each electronic control system in the vehicle during running of the vehicle further comprises:
Acquiring reference data of driving behaviors of the vehicle under different vehicle working conditions, return visit data of the user and a current scoring mechanism in real time;
and adjusting the current scoring mechanism based on the reference data and the return visit data to obtain the latest preset scoring mechanism.
3. The driving behavior evaluation method according to claim 2, wherein the step of adjusting the current scoring mechanism based on the reference data and the return visit data to obtain a latest preset scoring mechanism comprises:
Determining an initial adjustment scheme of the current scoring mechanism based on the reference data and the return visit data;
acquiring big data of the vehicle working condition;
Adjusting the initial adjustment scheme based on the big data to obtain a final adjustment scheme;
and adjusting the current scoring mechanism based on the final adjustment scheme to obtain the latest preset scoring mechanism.
4. A driving behavior evaluation method according to claim 3, wherein the step of determining an initial adjustment scheme of the current scoring mechanism based on the reference data and the return visit data includes:
sorting the required target driving behaviors and the occurrence times of each target driving behavior under different driving conditions based on the reference data;
analyzing adjustment suggestions of historical users using the current scoring mechanism based on the return visit data;
and determining an initial adjustment scheme of the score and the scoring coefficient corresponding to each driving behavior in the current scoring mechanism based on the target driving behavior, the occurrence frequency and the adjustment suggestion.
5. The driving behavior evaluation method according to any one of claims 1 to 4, characterized in that the step of scoring a driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data, and obtaining a driving score of the user, comprises:
Judging whether the driving behavior of the user is bad behavior or not based on the driving state data;
If the bad behavior is the bad behavior, judging whether the bad behavior meets a preset deduction threshold value or not;
If the score and the score coefficient corresponding to the bad behavior are not met, obtaining the score and the score coefficient corresponding to the bad behavior from a preset scoring mechanism;
Determining a score to be increased for the user based on the score and the scoring score;
and adding the added score with the original score of the user to obtain the driving score of the user.
6. The driving behavior evaluation method according to claim 5, wherein the step of judging whether the bad behavior satisfies a preset deduction threshold if the bad behavior is the bad behavior further comprises:
And if so, subtracting the score corresponding to the bad behavior from the original score to obtain the driving score of the user.
7. The driving behavior evaluation method according to claim 1, wherein the step of generating a driving evaluation based on the driving score and presenting the driving evaluation to the user if the vehicle is in a fleet mode, comprises:
generating driving behavior data of the user based on the driving score;
Acquiring the formation of the motorcade;
And generating driving evaluation of each team member of the vehicle team based on the team shape and the driving behavior data, and displaying the driving evaluation to a management user of the vehicle team.
8. A driving behavior evaluation device, characterized by comprising:
the first acquisition module is used for acquiring driving state data acquired by each electronic control system in the vehicle in the driving process of the vehicle;
The scoring module is used for scoring the driving behavior of a user driving the vehicle based on a preset scoring mechanism and the driving state data to obtain the driving score of the user, and the preset scoring mechanism is adjusted in real time through big data analysis based on the actual driving condition and the user return visit;
And the prompt module is used for generating a driving evaluation based on the driving score and displaying the driving evaluation to the user.
9. A driving behavior evaluation apparatus, characterized by comprising: a memory, a processor, and a driving behavior evaluation program stored on the memory and executable on the processor, the driving behavior evaluation program being configured to implement the steps of the driving behavior evaluation method according to any one of claims 1 to 7.
10. A storage medium, characterized in that a program for realizing the driving behavior evaluation method is stored on the storage medium, the program for realizing the driving behavior evaluation method being executed by a processor to realize the steps of the driving behavior evaluation method according to any one of claims 1 to 7.
CN202410170599.7A 2024-02-06 2024-02-06 Driving behavior evaluation method, device, equipment and storage medium Pending CN117985025A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118953373A (en) * 2024-07-26 2024-11-15 中国第一汽车股份有限公司 A driving data analysis method, system and vehicle
CN119477064A (en) * 2024-10-31 2025-02-18 江铃汽车股份有限公司 ADAS driving evaluation method, system, storage medium and vehicle

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118953373A (en) * 2024-07-26 2024-11-15 中国第一汽车股份有限公司 A driving data analysis method, system and vehicle
CN119477064A (en) * 2024-10-31 2025-02-18 江铃汽车股份有限公司 ADAS driving evaluation method, system, storage medium and vehicle

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