CN113183985A - Vehicle control method and device and unmanned vehicle - Google Patents
Vehicle control method and device and unmanned vehicle Download PDFInfo
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- CN113183985A CN113183985A CN202110491914.2A CN202110491914A CN113183985A CN 113183985 A CN113183985 A CN 113183985A CN 202110491914 A CN202110491914 A CN 202110491914A CN 113183985 A CN113183985 A CN 113183985A
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- 238000011217 control strategy Methods 0.000 claims abstract description 63
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- 238000007781 pre-processing Methods 0.000 claims abstract description 26
- 238000010801 machine learning Methods 0.000 claims abstract description 25
- 230000007613 environmental effect Effects 0.000 claims description 43
- 230000008451 emotion Effects 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 9
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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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
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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
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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
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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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
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
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Abstract
The invention discloses a vehicle control method and device and an unmanned vehicle. Wherein, the method comprises the following steps: acquiring a running environment image of a target vehicle; preprocessing the driving environment image to obtain a preprocessed driving environment image; determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and controlling the running behavior of the target vehicle according to the control strategy. The invention solves the technical problems that in the related art, when the road condition analysis is carried out on the road where the vehicle is located, the road condition analysis is easily influenced by the interferent in the analysis object, the accuracy of the road condition analysis is reduced, and potential safety hazards exist.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle control method and device and an unmanned vehicle.
Background
When a vehicle runs on a road, the surrounding road conditions need to be analyzed quickly and timely to ensure that the vehicle can run on the road normally. If it is ensured that the vehicle can normally run on the vehicle, it is very important to analyze the road condition of the vehicle. In the related art, when analyzing the road condition of the vehicle, the information of the road condition of the vehicle on the road is determined by collecting the image of the current road and analyzing the image. However, in the related art, when the collected image is analyzed, the analyzed road condition may be inaccurate due to a large number of interferents on the image, and then a control strategy for controlling the vehicle determined based on the road condition analyzed by the image may also have an error, which may lead to a potential safety hazard for road safety.
Aiming at the problems that in the related art, when the road condition analysis is carried out on the road where the vehicle is located, the influence of an interference object in an analysis object is easily caused, the accuracy of the road condition analysis is reduced, and potential safety hazards exist, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a vehicle control method and device and an unmanned vehicle, and aims to at least solve the technical problems that in the related art, when the road condition of a road where the vehicle is located is analyzed, the road condition is easily influenced by an interference object in an analysis object, the accuracy of road condition analysis is reduced, and potential safety hazards exist.
According to an aspect of an embodiment of the present invention, there is provided a control method of a vehicle, including: acquiring a running environment image of a target vehicle; preprocessing the driving environment image to obtain a preprocessed driving environment image; determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained by using multiple sets of training data through machine learning training, and each set of training data in the multiple sets of training data comprises: the driving environment image is a preprocessed image; and controlling the running behavior of the target vehicle according to the control strategy.
Optionally, acquiring the driving environment image of the current space of the target vehicle includes: determining that the target vehicle is in a running state; and triggering an image acquisition device on the target vehicle to acquire a running environment image in a preset area of the target vehicle.
Optionally, the driving environment image is preprocessed, and the preprocessing includes at least one of the following: denoising the driving environment image by using a thermonuclear diffusion mode to filter out background noise in the driving environment image; and comparing the driving environment image with a driving environment image template to filter out background noise in the driving environment image, wherein the driving environment image template is a preset template for denoising the driving environment image.
Optionally, controlling the driving behavior of the target vehicle according to the control strategy includes at least one of: controlling the running speed of the target vehicle according to the control strategy; and controlling the driving direction of the target vehicle according to the control strategy.
Optionally, the control method of the vehicle further includes: analyzing the running environment image to obtain meteorological information of the environment where the target vehicle is located; determining an environmental level of an environment in which the target vehicle is located based on the weather information; controlling predetermined components of the target vehicle in accordance with the environmental level, wherein the predetermined components include at least one of: vehicle window, wiper, air conditioner.
Optionally, determining an environmental level of an environment in which the target vehicle is located based on the weather information comprises: determining an environmental grade corresponding to the meteorological information through an environmental grade determination model, wherein the environmental grade determination model is obtained by using a plurality of sets of training data through machine learning training, and each set of training data in the plurality of sets of training data comprises: the weather information corresponds to an environmental level of the weather information.
Optionally, before determining the environmental level corresponding to the weather information by the environmental level determination model, the control method of the vehicle further includes: acquiring a plurality of historical meteorological information; and calibrating the plurality of historical meteorological information according to a preset judgment condition, and determining the environmental grade corresponding to each piece of historical meteorological information in the plurality of historical meteorological information to acquire the training data.
Optionally, the control method of the vehicle further includes: capturing images of passengers in the target vehicle; analyzing the image to obtain emotion information of the passenger; determining a multimedia file corresponding to the emotion information; and controlling a media component of the target vehicle to play the multimedia file.
According to another aspect of the embodiments of the present invention, there is also provided a control method of a vehicle, including: displaying a running environment image of a target vehicle in an operation interface of the target vehicle; preprocessing the driving environment image to obtain a preprocessed driving environment image; displaying a driving behavior decision model in the operation interface, and determining a control strategy corresponding to the preprocessed driving environment image, wherein the driving behavior decision model is obtained by using multiple sets of training data through machine learning training, and each set of training data in the multiple sets of training data comprises: the driving environment image is a preprocessed image; and displaying the running route of the target vehicle running according to the control strategy on the operation interface.
According to another aspect of the embodiments of the present invention, there is also provided a control apparatus of a vehicle, including: an acquisition unit configured to acquire a running environment image of a target vehicle; the preprocessing unit is used for preprocessing the driving environment image to obtain a preprocessed driving environment image; a determining unit, configured to determine, through a driving behavior decision model, a control strategy corresponding to the preprocessed driving environment image, where the driving behavior decision model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image; and the control unit is used for controlling the running behavior of the target vehicle according to the control strategy.
Optionally, the obtaining unit includes: the first determination module is used for determining that the target vehicle is in a running state; and the acquisition module is used for triggering the image acquisition equipment on the target vehicle to acquire the running environment image in the preset area of the target vehicle.
Optionally, the pre-processing unit includes at least one of: the denoising processing module is used for denoising the driving environment image by using a thermonuclear diffusion mode so as to filter out background noise in the driving environment image; and the filtering module is used for comparing the driving environment image with a driving environment image template so as to filter out background noise in the driving environment image, wherein the driving environment image template is a preset template used for denoising the driving environment image.
Optionally, the control unit includes at least one of: the first control module is used for controlling the running speed of the target vehicle according to the control strategy; and the second control module is used for controlling the driving direction of the target vehicle according to the control strategy.
Optionally, the control device of the vehicle further includes: the analysis unit is used for analyzing the running environment image to obtain meteorological information of the environment where the target vehicle is located; the determining unit is used for determining the environment level of the environment where the target vehicle is located based on the meteorological information; the control unit is used for controlling preset components of the target vehicle according to the environment level, wherein the preset components comprise at least one of the following components: vehicle window, wiper, air conditioner.
Optionally, the determining unit includes: a second determining module, configured to determine, through an environment level determination model, an environment level corresponding to the weather information, where the environment level determination model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the weather information corresponds to an environmental level of the weather information.
Optionally, the control device of the vehicle further includes: the acquisition module is used for acquiring a plurality of historical meteorological information before determining the environmental level corresponding to the meteorological information through an environmental level judgment model; and the calibration module is used for calibrating the historical meteorological information according to a preset judgment condition and determining the environmental grade corresponding to each historical meteorological information in the historical meteorological information so as to acquire the training data.
Optionally, the control device of the vehicle further includes: a collecting unit for collecting images of passengers in the target vehicle; the acquisition unit is used for analyzing the image to obtain the emotion information of the passenger; the determining unit is used for determining a multimedia file corresponding to the emotion information; the control unit is used for controlling the media component of the target vehicle to play the multimedia file.
According to another aspect of the embodiments of the present invention, there is also provided a control apparatus of a vehicle, including: the first display unit is used for displaying the running environment image of the target vehicle in an operation interface of the target vehicle; the processing unit is used for preprocessing the driving environment image to obtain a preprocessed driving environment image; a second display unit, configured to display, in the operation interface, a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, where the driving behavior decision model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image; and the third display unit is used for displaying the running route of the target vehicle running according to the control strategy on the operation interface.
According to another aspect of the embodiment of the invention, the unmanned vehicle and the control method of the vehicle using any one of the above are further provided.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored computer program, wherein when the computer program is executed by a processor, the apparatus in which the computer-readable storage medium is located is controlled to execute the control method of the vehicle according to any one of the above.
According to another aspect of the embodiment of the present invention, there is also provided a processor for executing a computer program, wherein the computer program executes to execute the control method of the vehicle according to any one of the above.
In the embodiment of the invention, the driving environment image of the target vehicle is acquired; preprocessing the driving environment image to obtain a preprocessed driving environment image; determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and controlling the running behavior of the target vehicle according to the control strategy. By the vehicle control method provided by the embodiment of the invention, the purpose of obtaining the control strategy for controlling the target vehicle through the preprocessed driving environment image is achieved, the technical effect of improving the accuracy of controlling the target vehicle is achieved, the potential safety hazard is reduced, and the technical problems that in the related technology, when the road condition analysis is carried out on the road where the vehicle is located, the road condition analysis is easily influenced by an interferent in an analysis object, the accuracy of the road condition analysis is reduced, and the potential safety hazard exists are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a control method of a vehicle according to an embodiment of the invention;
FIG. 2 is a flow chart of an alternative vehicle control method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a control apparatus of a vehicle according to an embodiment of the invention;
fig. 4 is a schematic diagram of an alternative control arrangement for a vehicle according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of a control method for a vehicle, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flowchart of a control method of a vehicle according to an embodiment of the present invention, as shown in fig. 1, including the steps of:
step S102, acquiring a running environment image of the target vehicle.
Alternatively, the target vehicle here may be an unmanned vehicle.
Alternatively, the driving environment image may be an image of a road where the target vehicle is located, which is acquired during the driving process of the target vehicle.
Optionally, the image capturing device herein may include, but is not limited to: radar, laser sensors.
As an alternative embodiment, acquiring the driving environment image of the space where the target vehicle is currently located includes: determining that the target vehicle is in a running state; and triggering an image acquisition device on the target vehicle to acquire the running environment image in the preset area of the target vehicle.
For example, when the monitoring device on the target vehicle monitors that the target vehicle is in a running state, the image acquisition device on the target vehicle may be triggered to acquire an image within a predetermined range of the target vehicle, that is, a driving environment image, which is used to describe road condition information of a road where the target vehicle is currently located.
And step S104, preprocessing the driving environment image to obtain a preprocessed driving environment image.
Because the driving environment image collected by the image collecting device may be influenced by dust, garbage and the like in the air, more interferents exist in the driving environment image, and the road condition analysis of the road where the target vehicle is located is influenced.
Therefore, in the embodiment of the present invention, the driving environment image may be preprocessed, and then the road condition determination may be performed based on the preprocessed driving environment image, so as to obtain the control strategy corresponding to the driving environment image.
As an alternative embodiment, the running environment image is preprocessed, which includes at least one of the following: denoising the driving environment image by using a thermonuclear diffusion mode to filter out background noise in the driving environment image; and comparing the driving environment image with a driving environment image template to filter out background noise in the driving environment image, wherein the driving environment image template is a preset template for denoising the driving environment image.
That is, the thermonuclear diffusion technology may be used to remove the background noise in the image, or the driving environment image may be compared with the image template in the image library to obtain a clean environment image with the background noise removed, so as to facilitate subsequent analysis of the driving environment image.
Step S106, determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained through machine learning training by using multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the driving environment image is a preprocessed image, and the driving environment image and the control strategy correspond to the driving environment image.
And step S108, controlling the running behavior of the target vehicle according to the control strategy.
As can be seen from the above, in the embodiment of the present invention, after the driving environment image of the target vehicle is acquired, the driving environment image is preprocessed, so as to obtain a preprocessed driving environment image; and determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and controlling the driving behavior of the target vehicle according to the control strategy, so that the purpose of obtaining the control strategy for controlling the target vehicle through the preprocessed driving environment image is achieved, the technical effect of improving the accuracy of controlling the target vehicle is achieved, and the potential safety hazard is reduced.
Therefore, the control method of the vehicle provided by the embodiment of the invention solves the technical problems that the road condition analysis of the road where the vehicle is located is easily influenced by the interferent in the analysis object, the accuracy of the road condition analysis is reduced, and potential safety hazards exist in the related art.
In the step S108, the driving behavior of the target vehicle is controlled according to the control strategy, which includes at least one of: controlling the running speed of the target vehicle according to the control strategy; and controlling the driving direction of the target vehicle according to the control strategy.
That is, in this embodiment, the traveling speed of the target vehicle may be controlled according to the control strategy, and the traveling direction of the target vehicle may also be controlled according to the control strategy.
As an alternative embodiment, the control method of the vehicle may further include: analyzing the running environment image to obtain meteorological information of the environment where the target vehicle is located; determining the environmental grade of the environment where the target vehicle is located based on the meteorological information; controlling predetermined components of the target vehicle according to the environmental level, wherein the predetermined components include at least one of: vehicle window, wiper, air conditioner.
That is, in the embodiment of the present invention, the contents of dust, haze, and the like in the environment where the target vehicle is located may also be obtained by analyzing the driving environment image. In addition, the environmental condition may also be determined according to the determined contents of dust, haze, and the like, so that windows, wipers, air conditioners, and the like of the target vehicle may be controlled.
For example, when it is determined that it is currently in the rainfall state, the wiper of the target vehicle may be controlled to be turned on; when the current state of high dust and haze content is determined, the window of the target vehicle can be controlled to be closed; when the current temperature in the vehicle is determined to be higher, the air conditioner in the vehicle can be controlled to be turned on. Further, the operation mode of the in-vehicle air conditioner may be determined based on the difference in temperature between the inside and the outside of the target vehicle. Specifically, a target operation mode corresponding to the temperature difference may be determined through the operation mode determination model, thereby controlling the air conditioner to operate in the target operation mode.
It should be noted that the operation mode determination model is obtained by machine learning training using a plurality of sets of training data, and each set of training data in the plurality of sets of training data includes: a temperature difference and an operation mode corresponding to the temperature difference.
As an alternative embodiment, determining the environmental level of the environment in which the target vehicle is located based on the weather information includes: through environmental grade decision model, confirm the environmental grade that corresponds with meteorological information, wherein, environmental grade decision model obtains for using multiunit training data through machine learning training, and every group training data all includes in the multiunit training data: the weather information corresponds to an environmental level of the weather information.
Wherein, before the environmental level corresponding to the weather information is determined by the environmental level determination model, the method for controlling a vehicle further includes: acquiring a plurality of historical meteorological information; the method comprises the steps of calibrating a plurality of pieces of historical meteorological information through a preset judgment condition, and determining an environment grade corresponding to each piece of historical meteorological information in the plurality of pieces of historical meteorological information to obtain training data.
The environment grade determination model can be obtained through machine learning training, so that the environment grade can be determined according to the environment grade determination model, the environment state determination efficiency is improved, and corresponding reactions can be made in time.
As an alternative embodiment, the control method of the vehicle further includes: acquiring images of passengers in a target vehicle; analyzing the image to obtain emotion information of the passenger; determining a multimedia file corresponding to the emotion information; and controlling the media part of the target vehicle to play the multimedia file.
In this embodiment, images of passengers in the vehicle may be acquired by using an image acquisition device disposed in the target vehicle, so that emotion information of the passengers corresponding to the images may be determined by using an emotion analysis mode, a multimedia file corresponding to the emotion information may be determined, and a media component in the target vehicle may be controlled to play the multimedia information, thereby providing a comfortable riding environment for the passengers.
By the vehicle control method provided by the embodiment of the invention, the acquired images in the environment where the target vehicle is located can be preprocessed, and the control strategy for controlling the target vehicle is determined according to the preprocessed images, so that the accuracy of controlling the target vehicle is improved, and the potential safety hazard of a road is reduced.
Example 2
In accordance with an embodiment of the present invention, there is provided a method embodiment of a control method for a vehicle, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 2 is a flowchart of an alternative control method of a vehicle according to an embodiment of the present invention, as shown in fig. 2, including the steps of:
step S202, displaying the running environment image of the target vehicle in the operation interface of the target vehicle.
And step S204, preprocessing the driving environment image to obtain a preprocessed driving environment image.
Step S206, a control strategy corresponding to the preprocessed driving environment image is determined through a driving behavior decision model displayed in an operation interface, wherein the driving behavior decision model is obtained through machine learning training by using multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the driving environment image is a preprocessed image, and the driving environment image and the control strategy correspond to the driving environment image.
And step S208, displaying the running route of the target vehicle running according to the control strategy on the operation interface.
As can be seen from the above, in the embodiment of the present invention, after the driving environment image of the target vehicle is displayed in the operation interface of the target vehicle; preprocessing the driving environment image to obtain a preprocessed driving environment image; and displaying a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model in an operation interface, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and displaying the running route of the target vehicle running according to the control strategy on the operation interface, so that the aim of obtaining the control strategy for controlling the target vehicle through the preprocessed running environment image is fulfilled, the technical effect of improving the precision of controlling the target vehicle is achieved, and the potential safety hazard is reduced.
Therefore, the control method of the vehicle provided by the embodiment of the invention solves the technical problems that the road condition analysis of the road where the vehicle is located is easily influenced by the interferent in the analysis object, the accuracy of the road condition analysis is reduced, and potential safety hazards exist in the related art.
Example 3
According to another aspect of the embodiment of the present invention, there is also provided a control apparatus of a vehicle, fig. 3 is a schematic view of the control apparatus of the vehicle according to the embodiment of the present invention, and as shown in fig. 3, the control apparatus of the vehicle may include: an acquisition unit 31, a preprocessing unit 33, a determination unit 35, and a control unit 37. The following describes a control device for the vehicle.
An acquisition unit 31 for acquiring a running environment image of the target vehicle.
And the preprocessing unit 33 is configured to preprocess the driving environment image to obtain a preprocessed driving environment image.
The determining unit 35 is configured to determine, through a driving behavior decision model, a control strategy corresponding to the preprocessed driving environment image, where the driving behavior decision model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image, and the driving environment image and the control strategy correspond to the driving environment image.
A control unit 37 for controlling the driving behavior of the target vehicle according to the control strategy.
It should be noted here that the acquiring unit 31, the preprocessing unit 33, the determining unit 35, and the controlling unit 37 correspond to steps S102 to S108 in embodiment 1, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
As can be seen from the above, in the embodiment of the present invention, the driving environment image of the target vehicle may be acquired by the acquisition unit; then, preprocessing the driving environment image by using a preprocessing unit to obtain a preprocessed driving environment image; and then determining a control strategy corresponding to the preprocessed driving environment image by using a determination unit through a driving behavior decision model, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and controlling the driving behavior of the target vehicle according to the control strategy by using the control unit. The control device of the vehicle provided by the embodiment of the invention achieves the purpose of obtaining the control strategy for controlling the target vehicle through the preprocessed driving environment image, achieves the technical effect of improving the control precision of the target vehicle, reduces the potential safety hazard, and solves the technical problems that in the related technology, when the road condition analysis is carried out on the road where the vehicle is located, the influence of interferents in an analysis object is easily caused, the road condition analysis precision is reduced, and the potential safety hazard exists.
Optionally, the obtaining unit includes: the first determination module is used for determining that the target vehicle is in a running state; and the acquisition module is used for triggering the image acquisition equipment on the target vehicle to acquire the running environment image in the preset area of the target vehicle.
Optionally, the pre-processing unit comprises at least one of: the denoising processing module is used for denoising the driving environment image by using a thermonuclear diffusion mode so as to filter out background noise in the driving environment image; and the filtering module is used for comparing the driving environment image with the driving environment image template so as to filter out background noise in the driving environment image, wherein the driving environment image template is a preset template used for denoising the driving environment image.
Optionally, the control unit comprises at least one of: the first control module is used for controlling the running speed of the target vehicle according to the control strategy; and the second control module is used for controlling the running direction of the target vehicle according to the control strategy.
Optionally, the control device of the vehicle further includes: the analysis unit is used for analyzing the running environment image to obtain meteorological information of the environment where the target vehicle is located; a determination unit for determining an environmental level of an environment in which the target vehicle is located based on the weather information; a control unit for controlling predetermined components of the target vehicle according to the environmental level, wherein the predetermined components include at least one of: vehicle window, wiper, air conditioner.
Optionally, the determining unit includes: the second determination module is used for determining the environmental grade corresponding to the meteorological information through an environmental grade determination model, wherein the environmental grade determination model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the weather information corresponds to an environmental level of the weather information.
Optionally, the control device of the vehicle further includes: the acquisition module is used for acquiring a plurality of historical meteorological information before determining the environmental level corresponding to the meteorological information through the environmental level judgment model; the calibration module is used for calibrating the historical meteorological information according to a preset judgment condition, and determining the environmental grade corresponding to each historical meteorological information in the historical meteorological information so as to acquire training data.
Optionally, the control device of the vehicle further includes: a collecting unit for collecting images of passengers in a target vehicle; the acquisition unit is used for analyzing the image to obtain emotion information of the passenger; a determining unit for determining a multimedia file corresponding to the emotion information; and the control unit is used for controlling the media component of the target vehicle to play the multimedia file.
Example 4
According to another aspect of the embodiment of the present invention, there is also provided a control apparatus of a vehicle, fig. 4 is a schematic view of an alternative control apparatus of a vehicle according to the embodiment of the present invention, and as shown in fig. 4, the control apparatus of a vehicle may include: a first presentation unit 41, a processing unit 43, a second presentation unit 45 and a third presentation unit 47. The following describes a control device for the vehicle.
The first display unit 41 is configured to display the driving environment image of the target vehicle in the operation interface of the target vehicle.
And the processing unit 43 is configured to perform preprocessing on the driving environment image to obtain a preprocessed driving environment image.
A second display unit 45, configured to display, in the operation interface, a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, where the driving behavior decision model is obtained through machine learning training by using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image, and the driving environment image and the control strategy correspond to the driving environment image.
And a third display unit 47 for displaying the driving route of the target vehicle according to the control strategy on the operation interface.
It should be noted that the first display unit 41, the processing unit 43, the second display unit 45, and the third display unit 47 correspond to steps S202 to S208 in embodiment 2, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in embodiment 2. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
As can be seen from the above, in the embodiment of the present invention, the driving environment image of the target vehicle may be displayed in the operation interface of the target vehicle by the first display unit; then, preprocessing the driving environment image by using a processing unit to obtain a preprocessed driving environment image; and then displaying a driving behavior decision model in an operation interface by using a second display unit, and determining a control strategy corresponding to the preprocessed driving environment image, wherein the driving behavior decision model is obtained by using a plurality of groups of training data through machine learning training, and each group of training data in the plurality of groups of training data comprises: the driving environment image is a preprocessed image; and displaying the driving route of the target vehicle according to the control strategy on the operation interface by using the third display unit. The control device of the vehicle provided by the embodiment of the invention achieves the purpose of obtaining the control strategy for controlling the target vehicle through the preprocessed driving environment image, achieves the technical effect of improving the control precision of the target vehicle, reduces the potential safety hazard, and solves the technical problems that in the related technology, when the road condition analysis is carried out on the road where the vehicle is located, the influence of interferents in an analysis object is easily caused, the road condition analysis precision is reduced, and the potential safety hazard exists.
Example 5
According to another aspect of the embodiment of the invention, there is also provided an unmanned vehicle, a control method of a vehicle using any one of the above.
Example 6
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored computer program, wherein when the computer program is executed by a processor, an apparatus in which the computer-readable storage medium is controlled performs the control method of the vehicle of any one of the above.
Example 7
According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a computer program, wherein the computer program executes to execute the control method of the vehicle of any one of the above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple 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 through some interfaces, units or modules, and may be in an electrical or other form.
The 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 units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes 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 invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (14)
1. A control method of a vehicle, characterized by comprising:
acquiring a running environment image of a target vehicle;
preprocessing the driving environment image to obtain a preprocessed driving environment image;
determining a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, wherein the driving behavior decision model is obtained by using multiple sets of training data through machine learning training, and each set of training data in the multiple sets of training data comprises: the driving environment image is a preprocessed image;
and controlling the running behavior of the target vehicle according to the control strategy.
2. The method of claim 1, wherein obtaining the image of the driving environment of the space in which the target vehicle is currently located comprises:
determining that the target vehicle is in a running state;
and triggering an image acquisition device on the target vehicle to acquire a running environment image in a preset area of the target vehicle.
3. The method of claim 1, wherein preprocessing the driving environment image comprises at least one of:
denoising the driving environment image by using a thermonuclear diffusion mode to filter out background noise in the driving environment image;
and comparing the driving environment image with a driving environment image template to filter out background noise in the driving environment image, wherein the driving environment image template is a preset template for denoising the driving environment image.
4. The method of claim 1, wherein controlling the driving behavior of the target vehicle in accordance with the control strategy comprises at least one of:
controlling the running speed of the target vehicle according to the control strategy;
and controlling the driving direction of the target vehicle according to the control strategy.
5. The method of claim 1, further comprising:
analyzing the running environment image to obtain meteorological information of the environment where the target vehicle is located;
determining an environmental level of an environment in which the target vehicle is located based on the weather information;
controlling predetermined components of the target vehicle in accordance with the environmental level, wherein the predetermined components include at least one of: vehicle window, wiper, air conditioner.
6. The method of claim 5, wherein determining an environmental level of an environment in which the target vehicle is located based on the weather information comprises:
determining an environmental grade corresponding to the meteorological information through an environmental grade determination model, wherein the environmental grade determination model is obtained by using a plurality of sets of training data through machine learning training, and each set of training data in the plurality of sets of training data comprises: the weather information corresponds to an environmental level of the weather information.
7. The method of claim 6, wherein prior to determining the environmental level corresponding to the weather information via an environmental level determination model, the method further comprises:
acquiring a plurality of historical meteorological information;
and calibrating the plurality of historical meteorological information according to a preset judgment condition, and determining the environmental grade corresponding to each piece of historical meteorological information in the plurality of historical meteorological information to acquire the training data.
8. The method according to any one of claims 5 to 7, further comprising:
capturing images of passengers in the target vehicle;
analyzing the image to obtain emotion information of the passenger;
determining a multimedia file corresponding to the emotion information;
and controlling a media component of the target vehicle to play the multimedia file.
9. A control method of a vehicle, characterized by comprising:
displaying a running environment image of a target vehicle in an operation interface of the target vehicle;
preprocessing the driving environment image to obtain a preprocessed driving environment image;
displaying a driving behavior decision model in the operation interface, and determining a control strategy corresponding to the preprocessed driving environment image, wherein the driving behavior decision model is obtained by using multiple sets of training data through machine learning training, and each set of training data in the multiple sets of training data comprises: the driving environment image is a preprocessed image;
and displaying the running route of the target vehicle running according to the control strategy on the operation interface.
10. A control apparatus of a vehicle, characterized by comprising:
an acquisition unit configured to acquire a running environment image of a target vehicle;
the preprocessing unit is used for preprocessing the driving environment image to obtain a preprocessed driving environment image;
a determining unit, configured to determine, through a driving behavior decision model, a control strategy corresponding to the preprocessed driving environment image, where the driving behavior decision model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image;
and the control unit is used for controlling the running behavior of the target vehicle according to the control strategy.
11. A control apparatus of a vehicle, characterized by comprising:
the first display unit is used for displaying the running environment image of the target vehicle in an operation interface of the target vehicle;
the processing unit is used for preprocessing the driving environment image to obtain a preprocessed driving environment image;
a second display unit, configured to display, in the operation interface, a control strategy corresponding to the preprocessed driving environment image through a driving behavior decision model, where the driving behavior decision model is obtained through machine learning training using multiple sets of training data, and each set of training data in the multiple sets of training data includes: the driving environment image is a preprocessed image;
and the third display unit is used for displaying the running route of the target vehicle running according to the control strategy on the operation interface.
12. An unmanned vehicle characterized by using the control method of a vehicle according to any one of claims 1 to 8 or the control method of a vehicle according to claim 9.
13. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program is executed by a processor, the apparatus on which the computer-readable storage medium is stored is controlled to execute the method for controlling a vehicle according to any one of claims 1 to 8 or the method for controlling a vehicle according to claim 9.
14. A processor for running a computer program, wherein the computer program is run to perform the control method of the vehicle of any one of the preceding claims 1 to 8 or the control method of the vehicle of claim 9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| GB2609542A (en) * | 2021-06-02 | 2023-02-08 | Nvidia Corp | Techniques for classification with neural networks |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2609542A (en) * | 2021-06-02 | 2023-02-08 | Nvidia Corp | Techniques for classification with neural networks |
| GB2609542B (en) * | 2021-06-02 | 2023-12-13 | Nvidia Corp | Techniques for classification with neural networks |
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