CN114132333A - Intelligent driving system optimization method and device and computer readable storage medium - Google Patents
Intelligent driving system optimization method and device and computer readable storage medium Download PDFInfo
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- CN114132333A CN114132333A CN202111530536.0A CN202111530536A CN114132333A CN 114132333 A CN114132333 A CN 114132333A CN 202111530536 A CN202111530536 A CN 202111530536A CN 114132333 A CN114132333 A CN 114132333A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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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
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
Abstract
The embodiment of the invention relates to the technical field of intelligent driving, and discloses an intelligent driving system optimization method, an intelligent driving system optimization device and a computer readable storage medium, wherein the method comprises the following steps: when the intelligent driving system is not started, acquiring first driving behavior data of a driver in different scenes, self-learning the first driving behavior data to obtain second driving behavior data, generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters. By applying the technical scheme of the invention, the vehicle controlled by the intelligent driving system can be closer to the artificial driving style, and the performance is more intelligent and more humanized.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent driving, in particular to an intelligent driving system optimization method and device and a computer readable storage medium.
Background
With the rapid development of automobile intelligent driving assistance technology, passenger cars begin to popularize the driving assistance function of L2 level or higher, and how an intelligent driving system can assist drivers to drive more humanized in order to cope with various complex road conditions becomes an increasing topic of discussion.
However, how to automatically import the algorithm of the intelligent driving system by continuously learning the driving habits of the driver so that the intelligent driving system is closer to the driving style of the driver when controlling the vehicle is a problem which needs to be solved at present.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present invention provide an intelligent driving system optimization method, apparatus and computer-readable storage medium, which are used to solve the problem that an intelligent driving system in the prior art cannot be close to the driving style of a driver.
According to an aspect of an embodiment of the present invention, there is provided a smart driving system optimization method, including:
when the intelligent driving system is not started, acquiring first driving behavior data of a driver in different scenes;
self-learning is carried out on the first driving behavior data to obtain second driving behavior data;
and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
In an optional manner, before the step of collecting first driving behavior data of the driver in different scenes, the method further comprises: and presetting intelligent driving parameters.
In an optional manner, the step of collecting first driving behavior data of the driver in different scenes specifically includes: and acquiring the first driving behavior data of the driver under different scenes based on an intelligent driving controller.
In an optional manner, the step of self-learning the first driving behavior data to obtain the second driving behavior data specifically includes: and uploading the collected first driving behavior data to the intelligent driving controller for self-learning to obtain second driving behavior data.
In an optional manner, after the step of optimizing the intelligent driving system according to the vehicle control parameters, the method further comprises the following steps:
and generating a real-time vehicle control parameter curve based on the first driving behavior data of the driver acquired in real time, and correcting the vehicle control parameter according to the real-time vehicle control parameter curve.
In an alternative form, the first driving behavior data includes: longitudinal acceleration and deceleration, steering wheel rotation angle and transverse rotation angle rate under different scenes.
In an alternative form, the vehicle control parameter profile includes: a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve under different scenes.
According to another aspect of the embodiments of the present invention, there is provided an intelligent driving system optimization apparatus, including:
the intelligent driving system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring first driving behavior data of a driver in different scenes when the intelligent driving system is not started;
the processing module is used for self-learning the first driving behavior data to obtain second driving behavior data;
and the operation module is used for generating vehicle control parameters according to the second driving behavior data and optimizing the intelligent driving system according to the vehicle control parameters.
In an optional manner, before the acquisition module, the apparatus further comprises:
and the pre-configuration module is used for presetting intelligent driving parameters.
According to a further aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having at least one executable instruction stored therein, the executable instruction causing an intelligent driving system optimization device to perform the operations of the intelligent driving system optimization method.
According to the embodiment of the invention, when the intelligent driving system is not started, a large amount of driving data of the driver are collected, and the driving behavior and style of the driver are continuously learned through the intelligent driving system, so that the vehicle controlled by the intelligent driving system is closer to the artificial driving style, and the intelligent driving system is more intelligent in performance and more humanized.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
Drawings
The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of a smart driving system optimization method provided by the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a second embodiment of the intelligent driving system optimization method provided by the present invention;
FIG. 3 is a schematic flow chart diagram illustrating a third embodiment of the intelligent driving system optimization method provided by the present invention;
fig. 4 shows a schematic structural diagram of a first embodiment of the intelligent driving system optimization device provided by the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein.
Fig. 1 shows a flowchart of a first embodiment of an intelligent driving system optimization method provided by the present invention. As shown in fig. 1, the method comprises the steps of:
step 110: when the intelligent driving system is not started, first driving behavior data of a driver under different scenes are collected.
Wherein, different scenes include: when the traffic light intersection starts and the front vehicle brakes and decelerates. The driving behavior data includes: the intelligent driving system collects driving data such as longitudinal acceleration and deceleration, steering wheel rotation angle, transverse rotation angle rate and the like of a driver in the driving process in different scenes.
It should be noted that different scenarios include, but are not limited to, the above scenario.
Step 120: and self-learning the first driving behavior data to obtain second driving behavior data.
When the intelligent driving system is not started, the intelligent driving system uploads first driving behavior data of drivers collected in different scenes to the intelligent driving system through a vehicle management network for self-learning, and finally, second driving behavior data obtained after self-learning are imported into a control algorithm of the intelligent driving system.
Step 130: and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
And the intelligent driving system obtains the most common state of the driver according to the second driving behavior data, and optimizes the intelligent driving system by taking the most common state as the vehicle control parameter.
For example, when starting at a traffic light intersection, some drivers like the back-pushing feeling brought by the floor oil, but the intelligent driving system generally selects a relatively stable starting mode, and after the drivers get used to the habit, the driving style of the drivers can be simulated according to the driving styles of different drivers, so that the intelligent driving system is closer to the driving of the drivers and is more intelligent. For another example, when the front vehicle brakes and decelerates, some drivers like to start to decelerate slowly very early, and some drivers are used to habit and the like to perform a faster braking mode, at this time, the system can learn the style of the drivers according to the following driving habit of the drivers when driving at ordinary times, and finally imitate the drivers to control the vehicle in a driver habit mode.
This embodiment is through when intelligent driving system does not start, gathers a large amount of drivers' driving data to constantly study driver driving behavior and style through intelligent driving system, can make the vehicle of intelligent driving system control more press close to with artificial driving style, the performance is more intelligent, more humanized.
Fig. 2 shows a flowchart of a second embodiment of the intelligent driving system optimization method provided by the present invention. As shown in fig. 2, the method comprises the steps of:
step 210: and presetting intelligent driving parameters.
Wherein, preset intelligent driving parameter includes: the acceleration and deceleration of longitudinal control, the turning angle rate of transverse control and the like ensure the normal function of the intelligent driving system.
Step 220: when the intelligent driving system is not started, first driving behavior data of a driver under different scenes are collected.
Step 230: and self-learning the first driving behavior data to obtain second driving behavior data.
Step 240: and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
The intelligent driving parameters are preset for the intelligent driving system, and when the intelligent driving system is not started, the driving behavior data of the driver are collected and the intelligent driving parameters are optimized, so that the normal driving of the vehicle can be guaranteed, and the intelligent driving system can be closer to the driving style of the driver when controlling the vehicle.
On the basis of the first embodiment or the second embodiment of the intelligent driving system optimization method provided by the invention, the method further comprises the following steps:
The intelligent driving controller is a part of an intelligent driving system and can control the vehicle to drive under the set vehicle control parameters.
The steps 120 and 230 specifically include: and uploading the collected first driving behavior data to the intelligent driving controller for self-learning to obtain second driving behavior data.
This embodiment is through when intelligent driving system does not start, based on the driving data of a large amount of drivers of intelligent driving controller collection among the intelligent driving system to constantly learn driver's driving behavior and style through the intelligent driving controller among the intelligent driving system, can make the vehicle of intelligent driving controller control among the intelligent driving system more press close to with artificial driving style, the performance is more intelligent, more humanized.
Fig. 3 shows a flowchart of a third embodiment of the intelligent driving system optimization method provided by the present invention. As shown in fig. 3, the method comprises the steps of:
step 310: and presetting intelligent driving parameters.
Step 320: when the intelligent driving system is not started, first driving behavior data of a driver under different scenes are collected.
Step 330: and self-learning the first driving behavior data to obtain second driving behavior data.
Step 340: and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
Step 350: and generating a real-time vehicle control parameter curve based on the first driving behavior data of the driver acquired in real time, and correcting the vehicle control parameter according to the real-time vehicle control parameter curve.
The intelligent driving system obtains the most common state of the driver through the collected first driving behavior data when a large number of drivers drive the vehicle, generates a real-time vehicle control parameter curve along with the increase of the data volume, continuously corrects the existing parameters, and finally forms a set of parameters which can be continuously and continuously optimized.
On the basis of the embodiment, the embodiment acquires the first driving behavior data of the driver in real time, self-learns through the intelligent driving system, continuously obtains new vehicle control parameters, and corrects the vehicle control parameters based on the vehicle control parameter curve, so that the vehicle control parameters are more accurately close to the driving style of the driver, and the intelligent driving system is more intelligent and humanized in performance.
On the basis of the third embodiment described above, the first driving behavior data includes: longitudinal acceleration and deceleration, steering wheel rotation angle and transverse rotation angle rate under different scenes. The vehicle control parameter profile includes: a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve under different scenes.
By acquiring the longitudinal acceleration and deceleration, the steering wheel corner and the transverse corner rate of the driver in different scenes and generating a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve continuously based on the real-time acquired data, the driving process of the vehicle controlled by the intelligent driving controller can better accord with the driving style of the driver, and the experience of the driver is improved.
Fig. 4 shows a schematic structural diagram of a first embodiment of the intelligent driving system optimization device provided by the invention. As shown in fig. 4, the apparatus 400 includes: an acquisition module 410, a processing module 420, and an execution module 430.
The intelligent driving system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring first driving behavior data of a driver in different scenes when the intelligent driving system is not started;
the processing module is used for self-learning the first driving behavior data to obtain second driving behavior data;
and the operation module is used for generating vehicle control parameters according to the second driving behavior data and optimizing the intelligent driving system according to the vehicle control parameters.
This embodiment is through when intelligent driving system does not start, gathers a large amount of drivers' driving data to constantly study driver driving behavior and style through intelligent driving system, can make the vehicle of intelligent driving system control more press close to with artificial driving style, the performance is more intelligent, more humanized.
The second embodiment of the intelligent driving system optimization device provided by the invention, on the basis of the first embodiment, further comprises: before the acquisition module, the apparatus further comprises:
and the pre-configuration module is used for presetting intelligent driving parameters.
The intelligent driving parameters are preset for the intelligent driving system, and when the intelligent driving system is not started, the driving behavior data of the driver are collected and the intelligent driving parameters are optimized, so that the normal driving of the vehicle can be guaranteed, and the intelligent driving system can be closer to the driving style of the driver when controlling the vehicle.
On the basis of the above embodiment, the acquisition module is specifically configured to: and acquiring the first driving behavior data of the driver under different scenes based on an intelligent driving controller.
The processing module is specifically configured to: and uploading the collected first driving behavior data to the intelligent driving controller for self-learning to obtain second driving behavior data.
This embodiment is through when intelligent driving system does not start, based on the driving data of a large amount of drivers of intelligent driving controller collection among the intelligent driving system to constantly learn driver's driving behavior and style through the intelligent driving controller among the intelligent driving system, can make the vehicle of intelligent driving controller control among the intelligent driving system more press close to with artificial driving style, the performance is more intelligent, more humanized.
The third embodiment of the intelligent driving system optimization device provided by the present invention, on the basis of the first embodiment or the second embodiment, further includes, after the operation module: and the correction module is used for generating a real-time vehicle control parameter curve based on the first driving behavior data of the driver collected in real time and correcting the vehicle control parameter according to the real-time vehicle control parameter curve.
On the basis of the embodiment, the embodiment acquires the first driving behavior data of the driver in real time, self-learns through the intelligent driving system, continuously obtains new vehicle control parameters, and corrects the vehicle control parameters based on the vehicle control parameter curve, so that the vehicle control parameters are more accurately close to the driving style of the driver, and the intelligent driving system is more intelligent and humanized in performance.
On the basis of the third embodiment described above, the first driving behavior data includes: longitudinal acceleration and deceleration, steering wheel rotation angle and transverse rotation angle rate under different scenes; the vehicle control parameter profile includes: a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve under different scenes.
By acquiring the longitudinal acceleration and deceleration, the steering wheel corner and the transverse corner rate of the driver in different scenes and generating a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve continuously based on the real-time acquired data, the driving process of the vehicle controlled by the intelligent driving controller can better accord with the driving style of the driver, and the experience of the driver is improved.
An embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores at least one executable instruction, and when the executable instruction runs on an intelligent driving system optimization device, the intelligent driving system optimization device is enabled to execute an intelligent driving system optimization method in any method embodiment described above.
The executable instructions may be specifically configured to cause the intelligent driving system optimization device to perform the following:
when the intelligent driving system is not started, acquiring first driving behavior data of a driver in different scenes;
self-learning is carried out on the first driving behavior data to obtain second driving behavior data;
and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
In an optional manner, before the step of collecting first driving behavior data of the driver in different scenes, the method further comprises: and presetting intelligent driving parameters.
In an optional manner, the step of collecting first driving behavior data of the driver in different scenes specifically includes: and acquiring the first driving behavior data of the driver under different scenes based on an intelligent driving controller.
In an optional manner, the step of self-learning the first driving behavior data to obtain the second driving behavior data specifically includes: and uploading the collected first driving behavior data to the intelligent driving controller for self-learning to obtain second driving behavior data.
In an optional manner, after the step of optimizing the intelligent driving system according to the vehicle control parameters, the method further comprises the following steps:
and generating a real-time vehicle control parameter curve based on the first driving behavior data of the driver acquired in real time, and correcting the vehicle control parameter according to the real-time vehicle control parameter curve.
In an alternative form, the first driving behavior data includes: longitudinal acceleration and deceleration, steering wheel rotation angle and transverse rotation angle rate under different scenes.
In an alternative form, the vehicle control parameter profile includes: a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve under different scenes.
This embodiment is through when intelligent driving system does not start, gathers a large amount of drivers' driving data to constantly study driver driving behavior and style through intelligent driving system, can make the vehicle of intelligent driving system control more press close to with artificial driving style, the performance is more intelligent, more humanized.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. In addition, embodiments of the present invention are not directed to any particular programming language.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. Similarly, in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. Where the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. Except that at least some of such features and/or processes or elements are mutually exclusive.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. An intelligent driving system optimization method is characterized by comprising the following steps:
when the intelligent driving system is not started, acquiring first driving behavior data of a driver in different scenes;
self-learning is carried out on the first driving behavior data to obtain second driving behavior data;
and generating vehicle control parameters according to the second driving behavior data, and optimizing the intelligent driving system according to the vehicle control parameters.
2. The intelligent driving system optimization method according to claim 1, further comprising, before the step of collecting first driving behavior data of a driver in different scenarios: and presetting intelligent driving parameters.
3. The intelligent driving system optimization method according to claim 1, wherein the step of collecting first driving behavior data of a driver in different scenes specifically comprises: and acquiring the first driving behavior data of the driver under different scenes based on an intelligent driving controller.
4. The intelligent driving system optimization method according to claim 3, wherein the step of self-learning the first driving behavior data to obtain the second driving behavior data specifically comprises: and uploading the collected first driving behavior data to the intelligent driving controller for self-learning to obtain second driving behavior data.
5. The intelligent driving system optimization method according to any one of claims 1 to 4, further comprising, after the step of optimizing the intelligent driving system according to the vehicle control parameters:
and generating a real-time vehicle control parameter curve based on the first driving behavior data of the driver acquired in real time, and correcting the vehicle control parameter according to the real-time vehicle control parameter curve.
6. The intelligent driving system optimization method of claim 5, wherein the first driving behavior data comprises: longitudinal acceleration and deceleration, steering wheel rotation angle and transverse rotation angle rate under different scenes.
7. The intelligent driving system optimization method of claim 5, wherein the vehicle control parameter profile comprises: a longitudinal acceleration and deceleration curve, a steering wheel corner curve and a transverse corner rate curve under different scenes.
8. An intelligent driving system optimization apparatus, the apparatus comprising:
the intelligent driving system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring first driving behavior data of a driver in different scenes when the intelligent driving system is not started;
the processing module is used for self-learning the first driving behavior data to obtain second driving behavior data;
and the operation module is used for generating vehicle control parameters according to the second driving behavior data and optimizing the intelligent driving system according to the vehicle control parameters.
9. The intelligent driving system optimization device of claim 8, wherein prior to the acquisition module, the device further comprises:
and the pre-configuration module is used for presetting intelligent driving parameters.
10. A computer-readable storage medium, wherein the storage medium stores at least one executable instruction, and when the executable instruction is executed on an intelligent driving system optimization device, the executable instruction causes a tunnel flooding early warning device to perform the operations of the intelligent driving system optimization method according to any one of claims 1 to 7.
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| CN112677982A (en) * | 2020-12-25 | 2021-04-20 | 重庆大学 | Vehicle longitudinal speed planning method based on driver characteristics |
| CN112829758A (en) * | 2021-01-08 | 2021-05-25 | 广西宁达汽车科技有限公司 | Automobile driving style self-learning method, device, equipment and storage medium |
| CN112918481A (en) * | 2021-03-25 | 2021-06-08 | 蔚来汽车科技(安徽)有限公司 | Method for determining driving parameters and vehicle control device |
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