CN113954854A - Intelligent driving function optimization method and device, electronic equipment and storage medium - Google Patents

Intelligent driving function optimization method and device, electronic equipment and storage medium Download PDF

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Publication number
CN113954854A
CN113954854A CN202111450391.3A CN202111450391A CN113954854A CN 113954854 A CN113954854 A CN 113954854A CN 202111450391 A CN202111450391 A CN 202111450391A CN 113954854 A CN113954854 A CN 113954854A
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driving
intelligent
intelligent driving
driving function
parameters
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CN113954854B (en
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许俊海
李敏
韦景文
刘智睿
刘安然
古睿希
龙文
罗鸿
罗晟楠
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GAC Aion New Energy Automobile Co Ltd
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GAC Aion New Energy Automobile Co 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/04—Monitoring the functioning of the control system
    • B60W50/045—Monitoring control system parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The application provides an intelligent driving function optimization method, an intelligent driving function optimization device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring driving data, wherein the driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users respectively; for each intelligent driving function, scoring a driving parameter corresponding to the intelligent driving function according to a preset scoring condition and a driving scene characteristic corresponding to the intelligent driving function; the intelligent driving function is optimized according to the grading result, so that the problems that related parameters of the existing intelligent driving function are fixed and the applicability is low are solved.

Description

Intelligent driving function optimization method and device, electronic equipment and storage medium
Technical Field
The application relates to the field of intelligent driving, in particular to an intelligent driving function optimization method and device, electronic equipment and a storage medium.
Background
Along with the continuous development of intelligent driving, various intelligent driving functions are provided on more and more automobiles, and the intelligent driving functions provide good driving experience for users. However, most of the related parameters of the existing intelligent driving function are determined according to past experience or theoretical calculation, so that the setting of the related parameters of the intelligent driving function is relatively fixed, and the applicability is relatively low.
Disclosure of Invention
An object of the embodiments of the present application is to provide an intelligent driving function optimization method, apparatus, electronic device, and storage medium, so as to solve the problem that the related parameter setting of the existing intelligent driving function is fixed and the applicability is low.
In a first aspect, the present invention provides a method for optimizing an intelligent driving function, the method comprising: acquiring driving data, wherein the driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users respectively; for each intelligent driving function, scoring a driving parameter corresponding to the intelligent driving function according to a preset scoring condition and a driving scene characteristic corresponding to the intelligent driving function; and optimizing the intelligent driving function according to the grading result.
According to the intelligent driving function optimization method, the driving parameters and the corresponding driving scene characteristics set by different users are analyzed by acquiring the driving data, the driving parameters corresponding to the intelligent driving function are scored according to the preset scoring conditions and the driving scene characteristics corresponding to the intelligent driving function in the analysis form of big data, and then the intelligent driving function is optimized according to the scoring result, so that the applicability of the intelligent driving function is improved.
In an alternative embodiment, the driving scenario features include: the situation of the user using the intelligent driving function and the safety factor in the driving process.
In an alternative embodiment, the case where the user uses the smart driving function includes: the safety factor in the driving process comprises the following steps: the method comprises the following steps of adding the number of times of plugging and emergency braking, wherein the scoring condition is the weight corresponding to each feature in the driving scene features, and the scoring of the driving parameters corresponding to the intelligent driving function is carried out according to the preset scoring condition and the driving scene features corresponding to the intelligent driving function, and comprises the following steps: and determining the grade of the driving parameter corresponding to the intelligent driving function according to the driving distance, the driving time, the added times and the brake times multiplied by the corresponding weights.
In the embodiment of the application, the driving parameters corresponding to the intelligent driving function are scored according to the driving distance, the driving time, the number of times of being plugged and the number of times of braking, on one hand, the use habits of users are considered, on the other hand, whether potential safety hazards exist in the driving parameters or not is considered, and therefore scoring is more objective and has practical use value.
In an optional embodiment, the optimizing the intelligent driving function according to the scoring result includes: determining the highest scoring driving parameter in the plurality of scores; and taking the driving parameter with the highest score as the driving parameter of the intelligent driving function under the road condition information corresponding to the driving parameter with the highest score.
In the embodiment of the application, after the intelligent driving function is optimized, the optimized intelligent driving function is issued to the automobile end in a software form, so that when a subsequent user uses the intelligent driving function again, the intelligent driving function is automatically set as the driving parameter with the highest score. By analyzing the driving parameters set by different users and the corresponding driving scene characteristics, the driving parameters with the highest scores are adopted for driving, and the potential safety hazards can be effectively reduced.
In an optional embodiment, the optimizing the intelligent driving function according to the scoring result includes: determining the highest-grade driving parameter in the plurality of grades and road condition information corresponding to the highest-grade driving parameter; and determining the set parameters of the intelligent driving function according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, wherein the set parameters are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
In the embodiment of the application, after the intelligent driving function is optimized, the optimized intelligent driving function is issued to the automobile end in a software form, so that when a subsequent user reuses the intelligent driving function, the driving parameters are determined according to real-time road condition information and the setting parameters corresponding to the intelligent driving function, and the intelligent driving function is set as the driving parameters. The set parameters of the intelligent driving function are determined according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, so that the determined set parameters can be applied to various different road conditions, and the applicability of the intelligent driving function optimization method is improved.
In an optional embodiment, the traffic information includes: road speed limit information and/or road type.
In an alternative embodiment, after the acquiring the driving data, the method further comprises: and preprocessing the driving data, and clearing invalid data in the driving data.
In a second aspect, the present invention provides an intelligent driving function optimization apparatus, comprising: the driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users respectively; the scoring module is used for scoring the driving parameters corresponding to the intelligent driving function according to a preset scoring condition and the driving scene characteristics corresponding to the intelligent driving function aiming at each intelligent driving function; and the optimization module is used for optimizing the intelligent driving function according to the grading result.
In an alternative embodiment, the driving scenario features include: the situation of the user using the intelligent driving function and the safety factor in the driving process.
In an alternative embodiment, the case where the user uses the smart driving function includes: the safety factor in the driving process comprises the following steps: the scoring module is used for determining the scoring of the parameters corresponding to the intelligent driving function according to the driving distance, the driving time, the added and blocked times and the product of the braking times and the corresponding weight.
In an alternative embodiment, the optimization module is specifically configured to determine a highest-scoring driving parameter of the plurality of scores; and taking the driving parameter with the highest score as the driving parameter of the intelligent driving function under the road condition information corresponding to the driving parameter with the highest score.
In an optional embodiment, the optimization module is specifically configured to determine a highest-scoring driving parameter among the plurality of scores and road condition information corresponding to the highest-scoring driving parameter; and determining the set parameters of the intelligent driving function according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, wherein the set parameters are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
In an optional embodiment, the traffic information includes: road speed limit information and/or road type.
In an alternative embodiment, the apparatus further comprises: and the preprocessing module is used for preprocessing the driving data and clearing invalid data in the driving data.
In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a bus; the processor and the memory are communicated with each other through the bus; the memory stores program instructions executable by the processor, the processor being capable of executing the method of any one of the preceding embodiments when invoked by the processor.
In a fourth aspect, the present invention provides a storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform the method according to any of the preceding embodiments.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of an intelligent driving function optimization method provided in an embodiment of the present application;
fig. 2 is a block diagram illustrating a structure of an intelligent driving function optimizing apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Icon: 200-intelligent driving function optimizing device; 201-an acquisition module; 202-a scoring module; 203-an optimization module; 204-a pre-processing module; 300-an electronic device; 301-a processor; 302-a communication interface; 303-a memory; 304-bus.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart of an intelligent driving function optimization method according to an embodiment of the present disclosure, where the intelligent driving function optimization method may include the following steps:
step 101: and acquiring the running data.
Step 102: and for each intelligent driving function, scoring the driving parameters corresponding to the intelligent driving function according to a preset scoring condition and the driving scene characteristics corresponding to the intelligent driving function.
Step 103: and optimizing the intelligent driving function according to the grading result.
The intelligent driving function optimization method provided by the embodiment of the application can be applied to a server. The server is connected in communication with a plurality of automobiles (or automobile central control units) and acquires driving data from the automobiles. And for each intelligent driving function, scoring the driving parameters set by the user corresponding to the intelligent driving function according to the driving scene characteristics corresponding to the intelligent driving function in the driving data and preset scoring conditions, and optimizing the intelligent driving function according to a scoring result.
The above steps will be described with reference to examples.
Step 101: and acquiring the running data.
In this embodiment of the application, the driving data may be driving data of each user in a preset time period. The duration of the preset time period can be flexibly set, for example, a day, a week, a month, and the like, which is not limited in the present application.
The driving data can be driving data uploaded by the automobile in real time when the user drives the automobile; the driving data stored in the onboard memory may also be uploaded by the vehicle after a trip has ended. The driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users.
It should be noted that the driving scene features are various driving-related parameters collected by various sensors on the vehicle when a certain user uses the corresponding intelligent driving function. Specifically, the driving scene features include: the situation of the user using the intelligent driving function and the safety factor in the driving process. Wherein, the condition that the user uses the intelligent driving function may include: a travel distance (a travel distance when a certain intelligent driving function is used), a travel time (a travel time when a certain intelligent driving function is used), the number of times of active steering, a maximum distance to a preceding vehicle, a minimum distance to a preceding vehicle, and the like; the safety factor during driving may include: the number of times of being plugged, the number of times of emergency braking, the number of collision early warning times and the like. It is to be understood that the driving scenario features are exemplified herein for ease of understanding only and should not be construed as limiting the present application.
In one embodiment, a vehicle mounted intelligent Terminal (TBOX) is provided on the vehicle. The TBOX is connected with a vehicle body CAN bus and is used for collecting various information in the driving process of the vehicle. The TBOX is further connected with a cloud end, and various collected information is uploaded to the cloud end.
Because all can install TBOX on each car, and TBOX on each car all can be uploaded all kinds of information of gathering to the high in the clouds, however, not all data can be used for optimizing intelligent driving function in the raw data that uploads, consequently, need handle the raw data that receive, and then confirm the data of traveling. The processing of the raw data may include preprocessing, screening, and warehousing.
Specifically, the preprocessing of the raw data includes analyzing, marking and classifying the raw data, i.e., unpacking and processing the raw data into single data capable of distinguishing vehicle types, vehicle configurations, positions and time during signal acquisition, and the like.
The pre-processed original data is screened into single pre-processed data according to a preset filtering standard, and invalid data is eliminated. The preset filtering criteria can be that the intelligent driving functions are all in an exit state, and the vehicle type configuration does not have the intelligent driving function. Since the smart driving function is not provided to all vehicles, it is necessary to clear data of the vehicle not provided with the smart driving function. In addition, for vehicles equipped with smart driving functions, the vehicles may not turn on any smart driving function, and therefore, data corresponding to the vehicles that do not turn on any smart driving function needs to be cleared.
Warehousing: and storing the screened data in a warehouse according to different intelligent driving functions, functional parameters and user modes, so that the data can be conveniently analyzed and processed subsequently.
The intelligent driving function may be: AEB (automatic emergency braking), ILC (automatic lane change of vehicle can be completed), LCA (early warning of vehicle coming from behind), TJA (automatic following at low speed due to congestion), FCW (early warning of collision in front), LDW (early warning of lane departure), LKA (lane keeping assist), TSR (traffic sign recognition), ELK (emergency lane keeping assist), IAC (integrated cruise assist), and the like. It is to be understood that the examples of smart driving functionality herein are merely for ease of understanding and should not be construed as limiting the present application.
It should be noted that the driving scene features are various driving-related parameters collected by various sensors on the vehicle when a certain user uses the corresponding intelligent driving function. For example, the driving scenario features may be: the time for using a certain intelligent driving function, the driving distance under the intelligent driving function, the times of active lane change, the times of braking, the times of controlling a steering wheel, the speed limit of a road, the maximum distance from a front vehicle, the minimum distance from the front vehicle and the like. It is to be understood that the driving scenario features are exemplified herein for ease of understanding only and should not be construed as limiting the present application.
The driving parameters are related parameters set by a user when a certain intelligent driving function is used.
In one embodiment, taking ICA (integrated cruise assist) as an example, the ICA functions are: and automatically adjusting the distance between the vehicle and the front vehicle when the cruising speed is 0-120 km/h, and keeping the vehicle in the middle of the lane. When the vehicle speed is high (generally more than 60km/h), a transverse and longitudinal auxiliary intelligent driving function is provided for the driver. For the ICA intelligent driving function, the driving parameters are the speed per hour and the following distance set by the user.
In one embodiment, taking TJA (congestion low-speed automatic following) as an example, the function of TJA is as follows: when the vehicle speed is low (generally 0-60Km/h), the auxiliary control function of the transverse direction and the longitudinal direction is provided for the driver at the same time. The TJA function is used to control a vehicle to travel at a certain speed at a middle position of a lane line when there is a clear lane line on a road surface and there is no reference vehicle within a certain distance from the front of the vehicle. And if a reference vehicle exists in front of the vehicle, the TJA function is used for controlling the vehicle to run according to the running track of the front vehicle, actively controlling the acceleration and the deceleration of the own vehicle and controlling the vehicle and the front vehicle to keep a certain time interval. For the TJA intelligent driving function, the driving parameters are the speed per hour and the following distance set by a user.
As an optional implementation manner, after step 101, the method further includes:
and preprocessing the driving data and clearing invalid data in the driving data.
In the embodiment of the application, in the transmission process, abnormal running data may occur due to network reasons and the like, or data which does not meet follow-up processing conditions may exist in the running data. In order to improve the accuracy of the data, the running data is preprocessed, and invalid data in the running data is cleared. Specifically, the judgment criterion of the invalid data may include: the intelligent driving functions are all in an off state, obvious abnormity exists in driving data and the like.
Step 102: and for each intelligent driving function, scoring the driving parameters corresponding to the intelligent driving function according to a preset scoring condition and the driving scene characteristics corresponding to the intelligent driving function.
In the embodiment of the application, after the driving data is acquired, for each intelligent driving function, the driving parameters corresponding to the intelligent driving function are scored according to the preset scoring condition and the driving scene characteristics corresponding to the intelligent driving function.
The preset scoring condition may be a weighted value corresponding to each driving scene characteristic.
When different intelligent driving functions are graded, the grading conditions and the driving scene features are different. Step 102 is described below with reference to specific examples.
As an alternative embodiment, the driving scene features include: the driving distance, the driving time, the number of times of being plugged and the number of times of braking, and the scoring condition is a weight corresponding to each feature in the driving scene features, and the step 102 may include the following steps:
and determining the grade of the driving parameter corresponding to the intelligent driving function according to the driving distance, the driving time, the number of times of being plugged and the number of times of braking multiplied by the corresponding weight.
Specifically, the TJA smart driving function is described as an example. When the TJA intelligent driving function is evaluated, the corresponding driving scene characteristics are the driving distance S, the driving time T, the number of times N of being plugged and the number of times M of braking. The weight value corresponding to the travel distance S is a, the weight value corresponding to the travel time T is b, the weight value corresponding to the number of times N of being plugged is c, and the weight value corresponding to the number of times M of braking is d. The driving parameters are as follows: velocity V and following distance D. The corresponding driving parameters of the speed per hour V and the following distance D are divided into the following sections: and S + T + b-N + c-M + d.
Assuming that a user uses the TJA intelligent driving function in a certain road section, in the process of using the TJA intelligent driving function, the driving distance is 5 kilometers, the driving time is 30 minutes, the number of times of plugging is 3, and the number of times of braking is 2. The set driving parameters are as follows: the speed per hour is 20km/h and the following distance is 10 meters. The weighted values a, b, c and d are as follows in sequence: 10,1,5,5. The corresponding score of 20km/h per hour and 10 meters of following distance is as follows: 5 x 10+30 x 1-3 x 5-2 x 5 ═ 55.
It should be noted that specific values of the weight values of the driving scene features may be flexibly set, and this is not specifically limited in this application.
Step 103: and optimizing the intelligent driving function according to the grading result.
According to the embodiment of the application, after the scoring result is determined, the intelligent driving function is optimized according to the scoring result.
As an alternative implementation, step 103 may include the following steps:
step one, determining a driving parameter with the highest score in a plurality of scores;
and secondly, taking the driving parameter with the highest score as the driving parameter of the intelligent driving function under the road condition information corresponding to the driving parameter with the highest score.
In the embodiment of the application, for each intelligent driving function, the driving data of a plurality of users are obtained and scored. Considering different road condition information, the driving parameters can be different. And classifying the scores, classifying the scores of the same road condition information into one class, and selecting the driving parameter with the highest score in each class as the driving parameter of the intelligent driving function under the road condition information.
Alternatively, the traffic information may be the speed limit information and/or the type of the road.
The road speed limit information can be divided into: the speed limit is 20km/h, 60km/h, 80km/h, 100km/h and the like, which is not limited in the application and can be set according to the actual road condition.
The road types can be divided into: urban roads, rural roads, highway roads and the like, which are not limited by the application, can be set according to actual road conditions.
After the intelligent driving function is optimized, the optimized intelligent driving function is issued to the automobile end in a software form, so that when a subsequent user uses the intelligent driving function again, the intelligent driving function is automatically set as a driving parameter with the highest grade. By analyzing the driving parameters set by different users and the corresponding driving scene characteristics, the driving parameters with the highest scores are adopted for driving, and the potential safety hazards can be effectively reduced.
As another alternative, step 103 may include the following steps:
step one, determining the highest-grade driving parameter and road condition information corresponding to the highest-grade driving parameter in a plurality of grades;
and secondly, determining the set parameters of the intelligent driving function according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, wherein the set parameters are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
In the embodiment of the application, the highest-grade driving parameter and the road condition information corresponding to the highest-grade driving parameter in the plurality of grades are determined. Considering that the set driving parameters of the user are different under different road condition information. Therefore, when the highest-grade driving parameter is determined, the road condition information corresponding to the highest-grade driving parameter is also determined. It should be noted that, unlike the previous embodiment, the present embodiment does not classify the driving parameters according to the traffic information, but determines the driving parameter with the highest score from all scores of the driving parameters corresponding to all traffic information.
And after the driving parameter with the highest score is determined, determining the setting parameter of the intelligent driving function according to the driving parameter with the highest score and the road condition information corresponding to the driving parameter with the highest score. It should be noted that the setting parameters of the intelligent driving function are different from the driving parameters of the intelligent driving function in the foregoing embodiment, and the setting parameters of the intelligent driving function are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
Specifically, the setting parameters of the smart driving function may be determined according to a preset mathematical model. And inputting the driving parameter with the highest score and the road condition information corresponding to the driving parameter with the highest score into the mathematical model, and calculating the setting parameter of the corresponding intelligent driving function.
For example, the driving parameters corresponding to the ICA intelligent driving function are speed per hour and following distance, and the road condition information is road speed limit information. Assume that the highest scoring driving parameter is determined to be: the speed per hour is 90km/h, the following distance is 90m, and the corresponding road speed limit information is 100 km/h. And determining the set parameters to be 0.9 and 1 according to a preset mathematical model, wherein the determination formula of the driving parameters is as follows: the speed limit speed per hour of the road is 0.9km/h, and the distance between the vehicles is as follows: speed per hour 1 m. For example, when the road speed limit is 50km/h, the speed per hour of the ICA smart driving function is set as: 50 x 0.9 ═ 45 km/h; according to the distance 50 x 1-50 m. When the road speed limit is 80km/h, the speed per hour of the ICA intelligent driving function is set as follows: 80 × 0.9 ═ 72 km/h; according to the distance 72 x 1-72 m.
It should be noted that the preset mathematical model for determining the setting parameters of the intelligent driving function may be determined according to the existing research results of the corresponding intelligent driving function, which is not specifically limited in this application.
After the intelligent driving function is optimized, the optimized intelligent driving function is issued to the automobile end in a software form, so that when a subsequent user uses the intelligent driving function again, the driving parameters are determined according to the real-time road condition information and the setting parameters corresponding to the intelligent driving function, and the intelligent driving function is set as the driving parameters. The set parameters of the intelligent driving function are determined according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, so that the determined set parameters can be applied to various different road conditions, and the applicability of the intelligent driving function optimization method is improved.
To sum up, the method for optimizing the intelligent driving function provided by the embodiment of the application analyzes the driving parameters and the corresponding driving scene characteristics set by different users by acquiring the driving data, scores the driving parameters corresponding to the intelligent driving function according to the preset scoring conditions and the driving scene characteristics corresponding to the intelligent driving function by adopting a big data analysis form, and further optimizes the intelligent driving function according to the scoring result, thereby improving the applicability of the intelligent driving function.
Based on the same inventive concept, the embodiment of the application also provides an intelligent driving function optimization device. Referring to fig. 2, fig. 2 is a block diagram illustrating a structure of an intelligent driving function optimizing device according to an embodiment of the present application, where the intelligent driving function optimizing device 200 may include:
an obtaining module 201, configured to obtain driving data, where the driving data includes driving scene features and driving parameters set by different users for respective used intelligent driving functions;
the scoring module 202 is configured to score, for each intelligent driving function, a driving parameter corresponding to the intelligent driving function according to a preset scoring condition and a driving scene characteristic corresponding to the intelligent driving function;
and the optimizing module 203 is used for optimizing the intelligent driving function according to the grading result.
In an alternative embodiment, the driving scenario features include: the situation of the user using the intelligent driving function and the safety factor in the driving process.
In an alternative embodiment, the case where the user uses the smart driving function includes: the safety factor in the driving process comprises the following steps: the scoring module 202 is configured to determine a score of a parameter corresponding to the intelligent driving function according to the travel distance, the travel time, the number of times of being jammed, the number of times of being braked, and a product of the number of times of being jammed and the weight corresponding thereto.
In an alternative embodiment, the optimization module 203 is specifically configured to determine a highest-scoring driving parameter in the plurality of scores; and taking the driving parameter with the highest score as the driving parameter of the intelligent driving function under the road condition information corresponding to the driving parameter with the highest score.
In an optional embodiment, the optimization module 203 is specifically configured to determine a highest-scoring driving parameter in the plurality of scores and road condition information corresponding to the highest-scoring driving parameter; and determining the set parameters of the intelligent driving function according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, wherein the set parameters are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
In an optional embodiment, the traffic information includes: road speed limit information and/or road type.
In an alternative embodiment, the apparatus further comprises: and the preprocessing module 204 is used for preprocessing the driving data and clearing invalid data in the driving data.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present application, where the electronic device 300 includes: at least one processor 301, at least one communication interface 302, at least one memory 303, and at least one bus 304. Wherein the bus 304 is used for realizing direct connection communication of these components, the communication interface 302 is used for communicating signaling or data with other node devices, and the memory 303 stores machine readable instructions executable by the processor 301. When the electronic device 300 is in operation, the processor 301 communicates with the memory 303 via the bus 304, and the machine-readable instructions, when invoked by the processor 301, perform the intelligent driving function optimization method described above.
The processor 301 may be an integrated circuit chip having signal processing capabilities. The Processor 301 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. Which may implement or perform the various methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The Memory 303 may include, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that electronic device 300 may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof. In the embodiment of the present application, the electronic device 300 may be, but is not limited to, an entity device such as a desktop, a notebook computer, a smart phone, an intelligent wearable device, and a vehicle-mounted device, and may also be a virtual device such as a virtual machine. In addition, the electronic device 300 is not necessarily a single device, but may also be a combination of multiple devices, such as a server cluster, and the like.
In addition, the embodiment of the present application further provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a computer, the steps of the intelligent driving function optimization method in the above embodiment are executed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. An intelligent driving function optimization method, characterized in that the method comprises:
acquiring driving data, wherein the driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users respectively;
for each intelligent driving function, scoring a driving parameter corresponding to the intelligent driving function according to a preset scoring condition and a driving scene characteristic corresponding to the intelligent driving function;
and optimizing the intelligent driving function according to the grading result.
2. The method of claim 1, wherein the driving scenario features comprise: the situation of the user using the intelligent driving function and the safety factor in the driving process.
3. The method of claim 2, wherein the user's use of smart driving functionality comprises: the safety factor in the driving process comprises the following steps: the method comprises the following steps of adding the number of times of plugging and emergency braking, wherein the scoring condition is the weight corresponding to each feature in the driving scene features, and the scoring of the driving parameters corresponding to the intelligent driving function is carried out according to the preset scoring condition and the driving scene features corresponding to the intelligent driving function, and comprises the following steps:
and determining the grade of the driving parameter corresponding to the intelligent driving function according to the driving distance, the driving time, the added times and the brake times multiplied by the corresponding weights.
4. The method of claim 1, wherein optimizing the intelligent driving function based on the scoring comprises:
determining the highest scoring driving parameter in the plurality of scores;
and taking the driving parameter with the highest score as the driving parameter of the intelligent driving function under the road condition information corresponding to the driving parameter with the highest score.
5. The method of claim 1, wherein optimizing the intelligent driving function based on the scoring comprises:
determining the highest-grade driving parameter in the plurality of grades and road condition information corresponding to the highest-grade driving parameter;
and determining the set parameters of the intelligent driving function according to the driving parameters with the highest score and the road condition information corresponding to the driving parameters with the highest score, wherein the set parameters are used for determining the driving parameters of the intelligent driving function according to the real-time road condition information.
6. The method according to claim 4 or 5, wherein the traffic information comprises: road speed limit information and/or road type.
7. The method of claim 1, wherein after said obtaining travel data, the method further comprises:
and preprocessing the driving data, and clearing invalid data in the driving data.
8. An intelligent driving function optimization device, characterized in that the device comprises:
the driving data comprises driving scene characteristics and driving parameters set by different users aiming at the intelligent driving functions used by the users respectively;
the scoring module is used for scoring the driving parameters corresponding to the intelligent driving function according to a preset scoring condition and the driving scene characteristics corresponding to the intelligent driving function aiming at each intelligent driving function;
and the optimization module is used for optimizing the intelligent driving function according to the grading result.
9. An electronic device, comprising: a processor, a memory, and a bus; the processor and the memory are communicated with each other through the bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any one of claims 1-7.
10. A storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform the method of any one of claims 1-7.
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