CN113581199A - Vehicle control method and device - Google Patents
Vehicle control method and device Download PDFInfo
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- CN113581199A CN113581199A CN202110737604.4A CN202110737604A CN113581199A CN 113581199 A CN113581199 A CN 113581199A CN 202110737604 A CN202110737604 A CN 202110737604A CN 113581199 A CN113581199 A CN 113581199A
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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
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
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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
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Abstract
The invention discloses a vehicle control method and device. Wherein, the method comprises the following steps: acquiring an area image in a preset area where a target vehicle is located in the running process of the target vehicle, wherein the area image is acquired by an information sensing module of the target vehicle; determining a target object needing to evaluate the risk level in the area image through an image recognition model; through the risk judgment model, confirm the risk grade that the target object corresponds, wherein, the risk judgment model is obtained for using multiunit training data to pass through machine learning training, and every group training data in the multiunit training data all includes: the target object and the risk level corresponding to the target object; and controlling the running behavior of the target vehicle according to the risk level. The invention solves the technical problems that the vehicle can not make a driving decision in an intelligent mode and the reliability is low in the related technology.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a vehicle control method and device.
Background
The current vehicle has long been the tool of choice when people go out. When people choose to take a bus for travel, the vehicles need to be driven. For the journey with a short distance, the driver does not feel tired; however, for a journey with a long distance, a driver can be easily tired, the travel experience of the driver is reduced, and potential safety hazards exist.
In addition, the driver also needs to concentrate on the driving process of the vehicle to determine the surrounding road conditions to make driving decisions, so that the burden of the driver is greatly increased, and the travel experience is reduced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a vehicle control method and device, which at least solve the technical problems that in the related art, a vehicle cannot make a driving decision in an intelligent mode and the reliability is low.
According to an aspect of an embodiment of the present invention, there is provided a control method of a vehicle, including: acquiring an area image in a preset area where a target vehicle is located in the running process of the target vehicle, wherein the area image is acquired by an information perception module of the target vehicle; determining a target object needing risk level evaluation in the region image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; determining a risk grade corresponding to the target object through a risk judgment model, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: a target object and a risk level corresponding to the target object; and controlling the running behavior of the target vehicle according to the risk level.
Optionally, before determining, by the image recognition model, a target object in the region image for which the risk level needs to be evaluated, the control method of the vehicle further includes: acquiring a filtering condition for filtering the area image; and filtering invalid information in the regional image by using the filtering condition to obtain a filtered regional image.
Optionally, before determining, by the image recognition model, a target object in the region image for which the risk level needs to be evaluated, the control method of the vehicle further includes: acquiring a plurality of historical region images of a historical time period and a plurality of historical target objects needing to evaluate risk levels in the plurality of historical region images; converting the plurality of historical region images and the plurality of historical target objects into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and training the multiple groups of training data to obtain the image recognition model.
Optionally, before determining the risk level corresponding to the target object through the risk judgment model, the vehicle control method further includes: acquiring a plurality of historical target objects of a historical time period and a plurality of historical risk levels corresponding to the plurality of historical target objects; converting the plurality of historical target objects and the plurality of historical risk levels into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and training the multiple groups of training data to obtain the risk judgment model.
Optionally, controlling the driving behavior of the target vehicle according to the risk level comprises: judging whether the target object forms danger for the running of the target vehicle according to the risk level to obtain a judgment result; when the judgment result shows that the risk level judges that the target object is dangerous to the target vehicle in driving, sending a request message to a cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new driving path based on the current road condition information, and controlling the target vehicle to drive based on the new driving path; and when the judgment result shows that the risk level judges that the target object does not form danger for the running of the target vehicle, controlling the target vehicle to run according to the original running path.
Optionally, the control method of the vehicle further includes: when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, judging that the target vehicle runs along the original running path, and when a driving strategy is changed, judging whether the target vehicle can not collide with the target object or not to obtain a judgment result; if so, adjusting a driving strategy based on the characteristic information of the target object, and controlling the target vehicle to run along the original running path based on the adjusted driving strategy; if not, sending a request message to a cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new driving path based on the current road condition information, and controlling the target vehicle to drive based on the new driving path.
According to another aspect of the embodiments of the present invention, there is also provided a control method of a vehicle, including: displaying an area image in a preset area where a target vehicle is located in an operation panel of the target vehicle in the running process of the target vehicle, wherein the area image is acquired by an information perception module of the target vehicle; identifying a target object needing risk level evaluation in the region image determined based on an image recognition model in the operation panel, wherein the image recognition model is obtained by machine learning training by using multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; displaying a risk grade corresponding to the target object determined based on a risk judgment model in the operation panel, wherein the risk judgment 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: a target object and a risk level corresponding to the target object; displaying, in the operation panel, a travel behavior performed by the target vehicle based on the risk level.
According to another aspect of the embodiments of the present invention, there is also provided a control apparatus of a vehicle, including: the system comprises a collecting unit, a processing unit and a display unit, wherein the collecting unit is used for collecting an area image in a preset area where a target vehicle is located in the running process of the target vehicle, and the area image is collected by an information sensing module of the target vehicle; a first determining unit, configured to determine, through an image recognition model, a target object in the region image for which a risk level needs to be evaluated, where the image recognition 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 method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; a second determining unit, configured to determine a risk level corresponding to the target object through a risk judgment model, where the risk judgment 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: a target object and a risk level corresponding to the target object; a control unit for controlling the driving behavior of the target vehicle in accordance with the risk level.
Optionally, the control device of the vehicle further includes: the first acquisition unit is used for acquiring a filtering condition for filtering the area image before determining a target object needing to evaluate the risk level in the area image through an image recognition model; and the filtering unit is used for filtering invalid information in the regional image by using the filtering condition to obtain the filtered regional image.
Optionally, the control device of the vehicle further includes: a second obtaining unit, configured to obtain a plurality of history region images of a history time period and a plurality of history target objects of which risk levels need to be evaluated in the plurality of history region images before determining the target object of which risk levels need to be evaluated in the region images through an image recognition model; the first conversion unit is used for converting the historical region images and the historical target objects into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and the first training unit is used for training the multiple groups of training data to obtain the image recognition model.
Optionally, the control device of the vehicle further includes: a second obtaining unit, configured to obtain a plurality of historical target objects in a historical time period and a plurality of historical risk levels corresponding to the plurality of historical target objects before determining the risk levels corresponding to the target objects through a risk judgment model; the second conversion unit is used for converting the historical target objects and the historical risk levels into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and the second training unit is used for training the multiple groups of training data to obtain the risk judgment model.
Optionally, the control unit includes: the first judgment module is used for judging whether the target object forms danger for the running of the target vehicle according to the risk level to obtain a judgment result; the first control module is used for sending a request message to a cloud end when the judgment result shows that the risk level judges that the target object is dangerous to drive the target vehicle, so as to obtain the current road condition information of the preset range of the target vehicle, determine a new driving path based on the current road condition information, and control the target vehicle to drive based on the new driving path; and the second control module is used for controlling the target vehicle to run according to the original running path when the judgment result shows that the risk level judges that the target object does not form danger on the running of the target vehicle.
Optionally, the control device of the vehicle further includes: the second judgment module is used for judging that the target vehicle runs along the original running path when the judgment result shows that the risk level judges that the target object runs to the target vehicle to form danger, and judging whether the target vehicle can not collide with the target object when the driving strategy is changed to obtain a judgment result; the third control module is used for driving the target vehicle according to the original driving path, when the driving strategy is changed, the target vehicle can not collide with the target object, the driving strategy is adjusted based on the characteristic information of the target object, and the target vehicle is controlled to drive along the original driving path based on the adjusted driving strategy; and the fourth control module is used for sending a request message to a cloud end to acquire current road condition information of the target vehicle in a preset range when the target vehicle runs according to the original running path and still collides with the target object when the driving strategy is changed, determining a new running path based on the current road condition information, and controlling the target vehicle to run based on the new running path.
According to another aspect of the embodiments of the present invention, there is also provided a control apparatus of a vehicle, including: the display device comprises a first display unit, a second display unit and a display unit, wherein the first display unit is used for displaying an area image in a preset area where a target vehicle is located in an operation panel of the target vehicle in the running process of the target vehicle, and the area image is acquired by an information sensing module of the target vehicle; an identification unit, configured to identify, in the operation panel, a target object whose risk level needs to be evaluated in the region image determined based on an image recognition model, where the image recognition 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 method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; a second display unit, configured to display, in the operation panel, a risk level corresponding to the target object determined based on a risk judgment model, where the risk judgment 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: a target object and a risk level corresponding to the target object; a third display unit configured to display, in the operation panel, a driving behavior of the target vehicle performed based on the risk level.
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 on 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, in the running process of a target vehicle, acquiring a region image in a preset region where the target vehicle is located, wherein the region image is acquired by an information sensing module of the target vehicle; determining a target object needing risk level assessment in the regional image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; through the risk judgment model, confirm the risk grade that the target object corresponds, wherein, the risk judgment model is obtained for using multiunit training data to pass through machine learning training, and every group training data in the multiunit training data all includes: the target object and the risk level corresponding to the target object; and controlling the running behavior of the target vehicle according to the risk level. By the vehicle control method provided by the embodiment of the invention, the purpose of assisting the vehicle to run by using the regional image acquired by the information sensing module arranged on the target vehicle is realized, the technical effect of improving the running safety of the target vehicle is achieved, and the technical problem that the vehicle cannot make a driving decision in an intelligent mode and has lower reliability in the related technology is 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 device of the vehicle in an embodiment in accordance with 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 regional image in a preset region where the target vehicle is located in the running process of the target vehicle, wherein the regional image is acquired by an information perception module of the target vehicle.
In this embodiment, the target vehicle may be an unmanned vehicle, or may be an intelligent vehicle having an intelligent driving assistance module. The information perception module of the target vehicle is triggered to collect the area image in the preset area on the current road during the running process of the vehicle.
Step S104, determining a target object needing to evaluate the risk level in the region image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: and the area image and the target object in the area image needing to evaluate the risk level.
Target objects in the region image, such as pedestrians, other vehicles, etc., for which the risk level needs to be evaluated, can be determined in an intelligent manner.
Step S106, determining a risk grade corresponding to the target object through a risk judgment model, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: a target object and a corresponding risk level for the target object.
In the embodiment, the risk level of the target object can be determined through the risk judgment module, so that the purpose of efficiently and accurately determining the risk level of the target object is achieved, and the running safety of the target vehicle is improved.
And step S108, controlling the running behavior of the target vehicle according to the risk level.
Therefore, in the embodiment of the invention, the regional image in the preset region where the target vehicle is located can be acquired in the running process of the target vehicle, wherein the regional image is acquired by the information sensing module of the target vehicle; determining a target object needing risk level assessment in the regional image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; through the risk judgment model, confirm the risk grade that the target object corresponds, wherein, the risk judgment model is obtained for using multiunit training data to pass through machine learning training, and every group training data in the multiunit training data all includes: the target object and the risk level corresponding to the target object; the driving behavior of the target vehicle is controlled according to the risk level, the purpose of assisting the vehicle in driving by using the regional image acquired by the information sensing module arranged on the target vehicle is achieved, and the technical effect of improving the driving safety of the target vehicle is achieved.
Therefore, the control method of the vehicle provided by the embodiment of the invention solves the technical problems that the vehicle cannot make a driving decision in an intelligent mode and the reliability is low in the related art.
In an optional embodiment, before determining, by the image recognition model, a target object in the region image for which the risk level needs to be evaluated, the control method of the vehicle further includes: acquiring a filtering condition for filtering the area image; and filtering invalid information in the regional image by using a filtering condition to obtain a filtered regional image.
In this embodiment, before determining the target object in the region image, which needs to be subjected to risk level assessment, through the image recognition model, the region image may be preprocessed, for example, invalid information in the region image, such as a fuzzy region in the region image, is filtered out by using a predetermined rule, so as to improve reliability and efficiency of target object recognition.
In an optional embodiment, before determining, by the image recognition model, a target object in the region image for which the risk level needs to be evaluated, the control method of the vehicle further includes: acquiring a plurality of historical region images of a historical time period and a plurality of historical target objects needing to evaluate risk levels in the plurality of historical region images; converting the plurality of historical region images and the plurality of historical target objects into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and training the multiple groups of training data to obtain an image recognition model.
In this embodiment, a neural network model may be trained using a plurality of historical region images and a plurality of historical target objects over a historical time period to derive an image recognition model. It should be noted that, in the embodiment of the present invention, specific types of the neural network model are not specifically limited.
In an optional embodiment, before determining the risk level corresponding to the target object through the risk judgment model, the control method of the vehicle further includes: acquiring a plurality of historical target objects of a historical time period and a plurality of historical risk levels corresponding to the plurality of historical target objects; converting a plurality of historical target objects and a plurality of historical risk levels into a format which can be identified by a neural network model to obtain a plurality of groups of training data; and training the multiple groups of training data to obtain a risk judgment model.
In this embodiment, the neural network model may be trained using a plurality of historical target objects and a plurality of historical risk levels over a historical time period to obtain a risk judgment model. It should be noted that, in the embodiment of the present invention, specific types of the neural network model are not specifically limited.
In the above step S108, controlling the traveling behavior of the target vehicle according to the risk level includes: judging whether the target object forms danger for the running of the target vehicle according to the risk level to obtain a judgment result; when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, sending a request message to the cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new running path based on the current road condition information, and controlling the target vehicle to run based on the new running path; and controlling the target vehicle to travel according to the original travel path when the judgment result shows that the risk level judgment target object does not form danger for the travel of the target vehicle.
In this embodiment, it may be determined whether the target object may pose a risk to the traveling of the target vehicle according to the acquired risk level; if so, a request message can be sent to the cloud end to acquire the current road condition information of the target vehicle within the preset range, then a new driving path can be determined according to the current road condition information, and the target vehicle is controlled to drive according to the new driving path. Otherwise, the target vehicle is controlled to run according to the original running path.
In an optional embodiment, the control method of the vehicle further includes: when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, judging that the target vehicle runs along the original running path, and when the driving strategy is changed, judging whether the target vehicle can not collide with the target object or not to obtain a judgment result; if so, adjusting the driving strategy based on the characteristic information of the target object, and controlling the target vehicle to run along the original running path based on the adjusted driving strategy; if not, sending a request message to the cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new driving path based on the current road condition information, and controlling the target vehicle to drive based on the new driving path.
In this embodiment, when the risk level indicates that the target object poses a risk to the traveling of the target vehicle, it may be determined that the target vehicle travels along the original traveling path, and when the target object is not collided while the vehicle is being driven, the driving policy may be adjusted according to the characteristic information of the target object, for example, the target object may be bypassed by acceleration or deceleration, and the vehicle may travel along the original traveling path without changing the traveling path. On the contrary, even if the driving strategy is changed, if the target vehicle runs according to the original running path, the target vehicle still collides with the target object, for example, tools and personnel for road construction, the request message is sent to the cloud end, so as to obtain the current road condition information in the preset range of the target vehicle, and the target vehicle is controlled to run based on the new running path based on the planned running path of the current road condition information.
In summary, according to the control method of the vehicle provided by the embodiment of the present invention, the information sensing module disposed on the target vehicle can be used to obtain the area image of the road where the vehicle is located, and the obtained area image can be analyzed to determine the target object for which the risk level evaluation is required; after the risk level of the target object is determined, the driving behavior of the vehicle can be determined according to the risk level, so that a driving behavior determination mode based on visual perception is realized, the driving reliability of the vehicle is improved, and potential safety hazards are reduced.
Example 2
There is also provided, in accordance with an embodiment of the present invention, a method embodiment of a control method for a vehicle, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be implemented 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 that described 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, in the running process of the target vehicle, displaying an area image in a preset area where the target vehicle is located in an operation panel of the target vehicle, wherein the area image is acquired by an information perception module of the target vehicle.
Step S204, identifying a target object needing to evaluate the risk level in the region image determined based on the image recognition model in the operation panel, wherein the image recognition 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: and the area image and the target object in the area image needing to evaluate the risk level.
Step S206, displaying the risk grade corresponding to the target object determined based on the risk judgment model in the operation panel, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: a target object and a corresponding risk level for the target object.
In step S208, the running behavior of the target vehicle executed based on the risk level is displayed in the operation panel.
Therefore, by the vehicle control method provided by the embodiment of the invention, in the running process of the target vehicle, the area image in the preset area where the target vehicle is located is displayed in the operation panel of the target vehicle, wherein the area image is acquired by the information perception module of the target vehicle; identifying a target object needing risk level evaluation in a region image determined based on an image recognition model in an operation panel, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; the risk grade corresponding to the target object determined based on the risk judgment model is displayed in the operation panel, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the target object and the risk level corresponding to the target object; the driving behavior of the target vehicle executed based on the risk level is displayed in the operation panel, the purpose of assisting the vehicle in driving by using the regional image acquired by the information sensing module arranged on the target vehicle is achieved, the technical effect of improving the driving safety of the target vehicle is achieved, the driving behavior determining mode of the vehicle is more visual and clear, and data support is provided for maintenance, overhaul and the like of subsequent vehicles.
Therefore, the control method of the vehicle provided by the embodiment of the invention solves the technical problems that the vehicle cannot make a driving decision in an intelligent mode and the reliability is low 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 diagram 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 first determination unit 33, a second determination unit 35, and a control unit 37. The following describes a control device for the vehicle.
The acquisition unit 31 is configured to acquire an area image in a predetermined area where the target vehicle is located during the driving process of the target vehicle, where the area image is acquired by an information sensing module of the target vehicle.
The first determining unit 33 is configured to determine, through an image recognition model, a target object in the region image for which the risk level needs to be evaluated, where the image recognition 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: and the area image and the target object in the area image needing to evaluate the risk level.
A second determining unit 35, configured to determine a risk level corresponding to the target object through a risk judgment model, where the risk judgment 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 includes: a target object and a corresponding risk level for the target object.
A control unit 37 for controlling the driving behavior of the target vehicle in dependence of the risk level.
It should be noted here that the above-mentioned acquisition unit 31, the first determination unit 33, the second determination unit 35 and the control unit 37 correspond to steps S102 to S108 in embodiment 1, and the above-mentioned modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to what is disclosed 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 acquisition unit may be used to acquire the area image in the predetermined area where the target vehicle is located during the driving process of the target vehicle, where the area image is acquired by the information sensing module of the target vehicle; then, a first determination unit is used for determining a target object needing risk level evaluation in the region image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; and then determining the risk grade corresponding to the target object by using a second determination unit through a risk judgment model, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the target object and the risk level corresponding to the target object; and controlling the driving behavior of the target vehicle according to the risk level using the control unit. The vehicle control device provided by the embodiment of the invention achieves the purpose of assisting the vehicle to run by using the regional image acquired by the information sensing module arranged on the target vehicle, achieves the technical effect of improving the running safety of the target vehicle, and solves the technical problems that the vehicle cannot make a driving decision in an intelligent mode and the reliability is low in the related art.
In an alternative embodiment, the control apparatus of the vehicle further includes: the first acquisition unit is used for acquiring a filtering condition for filtering the area image before determining a target object needing to evaluate the risk level in the area image through the image recognition model; and the filtering unit is used for filtering invalid information in the regional image by using the filtering condition to obtain the filtered regional image.
In an alternative embodiment, the control apparatus of the vehicle further includes: a second acquisition unit, configured to acquire a plurality of historical region images of a historical time period and a plurality of historical target objects of which risk levels need to be evaluated in the plurality of historical region images before determining the target object of which risk levels need to be evaluated in the region images through the image recognition model; the first conversion unit is used for converting the plurality of historical region images and the plurality of historical target objects into a format which can be identified by the neural network model to obtain a plurality of groups of training data; and the first training unit is used for training the multiple groups of training data to obtain the image recognition model.
In an alternative embodiment, the control apparatus of the vehicle further includes: a second obtaining unit, configured to obtain a plurality of historical target objects of a historical time period and a plurality of historical risk levels corresponding to the plurality of historical target objects before determining the risk levels corresponding to the target objects through the risk judgment model; the second conversion unit is used for converting the plurality of historical target objects and the plurality of historical risk levels into a format which can be identified by the neural network model to obtain a plurality of groups of training data; and the second training unit is used for training the multiple groups of training data to obtain a risk judgment model.
In an alternative embodiment, the control unit comprises: the first judgment module is used for judging whether the target object forms danger for the running of the target vehicle according to the risk level to obtain a judgment result; the first control module is used for sending a request message to the cloud end when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, so as to obtain the current road condition information of the target vehicle in a preset range, determine a new running path based on the current road condition information, and control the target vehicle to run based on the new running path; and the second control module is used for controlling the target vehicle to run according to the original running path when the judgment result shows that the risk level judgment target object does not form danger on the running of the target vehicle.
In an alternative embodiment, the control apparatus of the vehicle further includes: the second judgment module is used for judging whether the target vehicle runs along the original running path when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, and whether the target vehicle can not collide with the target object when the driving strategy is changed to obtain a judgment result; the third control module is used for driving the target vehicle according to the original driving path, adjusting the driving strategy based on the characteristic information of the target object when the driving strategy is changed and preventing the target vehicle from colliding with the target object, and controlling the target vehicle to drive along the original driving path based on the adjusted driving strategy; and the fourth control module is used for sending a request message to the cloud end to acquire the current road condition information of the preset range of the target vehicle when the target vehicle runs according to the original running path and still collides with the target object when the driving strategy is changed, determining a new running path based on the current road condition information and controlling the target vehicle to run based on the new running path.
Example 4
According to another aspect of the embodiment of the present invention, there is also provided a control apparatus of a vehicle, and fig. 4 is a schematic diagram 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, an identification 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, in the driving process of the target vehicle, an area image in a predetermined area where the target vehicle is located in an operation panel of the target vehicle, where the area image is acquired by an information sensing module of the target vehicle.
An identifying unit 43, configured to identify, in the operation panel, a target object whose risk level needs to be evaluated in the region image determined based on an image recognition model, where the image recognition 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: and the area image and the target object in the area image needing to evaluate the risk level.
A second display unit 45, configured to display, in the operation panel, a risk level corresponding to the target object determined based on the risk judgment model, where the risk judgment 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: a target object and a corresponding risk level for the target object.
A third presentation unit 47 for presenting the running behavior of the subject vehicle executed based on the risk level in the operation panel.
It should be noted here that the first display unit 41, the identification 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 first display unit may be used to display, in the operation panel of the target vehicle, the area image in the predetermined area where the target vehicle is located, where the area image is acquired by the information sensing module of the target vehicle; then, identifying a target object needing risk level evaluation in the region image determined based on the image recognition model in the operation panel by using an identification unit, wherein the image recognition 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 method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained; and then displaying the risk grade corresponding to the target object determined based on the risk judgment model in the operation panel by using a second display unit, wherein the risk judgment 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 target object and the risk level corresponding to the target object; and displaying, with the third display unit, a driving behavior of the target vehicle performed based on the risk level in the operation panel. The vehicle control device provided by the embodiment of the invention achieves the purpose of assisting the vehicle to run by using the regional image acquired by the information sensing module arranged on the target vehicle, achieves the technical effect of improving the running safety of the target vehicle, and solves the technical problems that the vehicle cannot make a driving decision in an intelligent mode and the reliability is low in the related art.
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 according to 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 (12)
1. A control method of a vehicle, characterized by comprising:
acquiring an area image in a preset area where a target vehicle is located in the running process of the target vehicle, wherein the area image is acquired by an information perception module of the target vehicle;
determining a target object needing risk level evaluation in the region image through an image recognition model, wherein the image recognition model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained;
determining a risk grade corresponding to the target object through a risk judgment model, wherein the risk judgment model is obtained by using multiple groups of training data through machine learning training, and each group of training data in the multiple groups of training data comprises: a target object and a risk level corresponding to the target object;
and controlling the running behavior of the target vehicle according to the risk level.
2. The method of claim 1, wherein prior to determining, by an image recognition model, a target object in the region image for which a risk level is to be assessed, the method further comprises:
acquiring a filtering condition for filtering the area image;
and filtering invalid information in the regional image by using the filtering condition to obtain a filtered regional image.
3. The method of claim 1, wherein prior to determining, by an image recognition model, a target object in the region image for which a risk level is to be assessed, the method further comprises:
acquiring a plurality of historical region images of a historical time period and a plurality of historical target objects needing to evaluate risk levels in the plurality of historical region images;
converting the plurality of historical region images and the plurality of historical target objects into a format which can be identified by a neural network model to obtain a plurality of groups of training data;
and training the multiple groups of training data to obtain the image recognition model.
4. The method of claim 1, wherein prior to determining the risk level corresponding to the target object via a risk assessment model, the method further comprises:
acquiring a plurality of historical target objects of a historical time period and a plurality of historical risk levels corresponding to the plurality of historical target objects;
converting the plurality of historical target objects and the plurality of historical risk levels into a format which can be identified by a neural network model to obtain a plurality of groups of training data;
and training the multiple groups of training data to obtain the risk judgment model.
5. The method of claim 1, wherein controlling the driving behavior of the target vehicle in accordance with the risk level comprises:
judging whether the target object forms danger for the running of the target vehicle according to the risk level to obtain a judgment result;
when the judgment result shows that the risk level judges that the target object is dangerous to the target vehicle in driving, sending a request message to a cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new driving path based on the current road condition information, and controlling the target vehicle to drive based on the new driving path;
and when the judgment result shows that the risk level judges that the target object does not form danger for the running of the target vehicle, controlling the target vehicle to run according to the original running path.
6. The method of claim 5, further comprising:
when the judgment result shows that the risk level judges that the target object is dangerous to the running of the target vehicle, judging that the target vehicle runs along the original running path, and when a driving strategy is changed, judging whether the target vehicle can not collide with the target object or not to obtain a judgment result;
if so, adjusting a driving strategy based on the characteristic information of the target object, and controlling the target vehicle to run along the original running path based on the adjusted driving strategy;
if not, sending a request message to a cloud end to acquire current road condition information of the target vehicle in a preset range, determining a new driving path based on the current road condition information, and controlling the target vehicle to drive based on the new driving path.
7. A control method of a vehicle, characterized by comprising:
displaying an area image in a preset area where a target vehicle is located in an operation panel of the target vehicle in the running process of the target vehicle, wherein the area image is acquired by an information perception module of the target vehicle;
identifying a target object needing risk level evaluation in the region image determined based on an image recognition model in the operation panel, wherein the image recognition model is obtained by machine learning training by using multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained;
displaying a risk grade corresponding to the target object determined based on a risk judgment model in the operation panel, wherein the risk judgment 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: a target object and a risk level corresponding to the target object;
displaying, in the operation panel, a travel behavior performed by the target vehicle based on the risk level.
8. A control apparatus of a vehicle, characterized by comprising:
the system comprises a collecting unit, a processing unit and a display unit, wherein the collecting unit is used for collecting an area image in a preset area where a target vehicle is located in the running process of the target vehicle, and the area image is collected by an information sensing module of the target vehicle;
a first determining unit, configured to determine, through an image recognition model, a target object in the region image for which a risk level needs to be evaluated, where the image recognition 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 method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained;
a second determining unit, configured to determine a risk level corresponding to the target object through a risk judgment model, where the risk judgment 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: a target object and a risk level corresponding to the target object;
a control unit for controlling the driving behavior of the target vehicle in accordance with the risk level.
9. A control apparatus of a vehicle, characterized by comprising:
the display device comprises a first display unit, a second display unit and a display unit, wherein the first display unit is used for displaying an area image in a preset area where a target vehicle is located in an operation panel of the target vehicle in the running process of the target vehicle, and the area image is acquired by an information sensing module of the target vehicle;
an identification unit, configured to identify, in the operation panel, a target object whose risk level needs to be evaluated in the region image determined based on an image recognition model, where the image recognition 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 method comprises the steps that a region image and a target object needing to be evaluated in the region image are obtained;
a second display unit, configured to display, in the operation panel, a risk level corresponding to the target object determined based on a risk judgment model, where the risk judgment 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: a target object and a risk level corresponding to the target object;
a third display unit configured to display, in the operation panel, a driving behavior of the target vehicle performed based on the risk level.
10. An unmanned vehicle characterized by using the control method of a vehicle according to any one of claims 1 to 6 or the control method of a vehicle according to claim 7.
11. 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 control method of the vehicle according to any one of claims 1 to 6 or the control method of the vehicle according to claim 7.
12. 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 6 or the control method of the vehicle of claim 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114312828A (en) * | 2021-11-30 | 2022-04-12 | 深圳元戎启行科技有限公司 | Risk management method, risk management platform and computer readable storage medium |
| CN114604199A (en) * | 2022-04-08 | 2022-06-10 | 中国第一汽车股份有限公司 | Vehicle protection system and method |
| CN115042823A (en) * | 2022-07-29 | 2022-09-13 | 浙江吉利控股集团有限公司 | Passenger-riding parking method and device, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106347359A (en) * | 2016-09-14 | 2017-01-25 | 北京百度网讯科技有限公司 | Method and device for operating autonomous vehicle |
| CN111301404A (en) * | 2020-02-06 | 2020-06-19 | 北京小马慧行科技有限公司 | Vehicle control method and device, storage medium and processor |
| CN111354182A (en) * | 2018-12-20 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Driving assisting method and system |
| US20200247404A1 (en) * | 2019-02-01 | 2020-08-06 | Toyota Jidosha Kabushiki Kaisha | Information processing device, information processing system, information processing method, and program |
| CN112232314A (en) * | 2020-12-11 | 2021-01-15 | 智道网联科技(北京)有限公司 | Vehicle control method and device for target detection based on deep learning |
| CN112693454A (en) * | 2019-10-23 | 2021-04-23 | 财团法人车辆研究测试中心 | Self-adaptive track generation method and system |
| CN112946628A (en) * | 2021-02-08 | 2021-06-11 | 江苏中路工程技术研究院有限公司 | Road running state detection method and system based on radar and video fusion |
-
2021
- 2021-06-30 CN CN202110737604.4A patent/CN113581199A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106347359A (en) * | 2016-09-14 | 2017-01-25 | 北京百度网讯科技有限公司 | Method and device for operating autonomous vehicle |
| CN111354182A (en) * | 2018-12-20 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Driving assisting method and system |
| US20200247404A1 (en) * | 2019-02-01 | 2020-08-06 | Toyota Jidosha Kabushiki Kaisha | Information processing device, information processing system, information processing method, and program |
| CN112693454A (en) * | 2019-10-23 | 2021-04-23 | 财团法人车辆研究测试中心 | Self-adaptive track generation method and system |
| CN111301404A (en) * | 2020-02-06 | 2020-06-19 | 北京小马慧行科技有限公司 | Vehicle control method and device, storage medium and processor |
| CN112232314A (en) * | 2020-12-11 | 2021-01-15 | 智道网联科技(北京)有限公司 | Vehicle control method and device for target detection based on deep learning |
| CN112946628A (en) * | 2021-02-08 | 2021-06-11 | 江苏中路工程技术研究院有限公司 | Road running state detection method and system based on radar and video fusion |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114312828A (en) * | 2021-11-30 | 2022-04-12 | 深圳元戎启行科技有限公司 | Risk management method, risk management platform and computer readable storage medium |
| CN114604199A (en) * | 2022-04-08 | 2022-06-10 | 中国第一汽车股份有限公司 | Vehicle protection system and method |
| CN115042823A (en) * | 2022-07-29 | 2022-09-13 | 浙江吉利控股集团有限公司 | Passenger-riding parking method and device, electronic equipment and storage medium |
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