CN114407926A - Vehicle control method based on artificial intelligence dangerous scene of automatic driving and vehicle - Google Patents
Vehicle control method based on artificial intelligence dangerous scene of automatic driving and vehicle Download PDFInfo
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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
- B60W60/0016—Planning or execution of driving tasks specially adapted for safety of the vehicle or its occupants
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/06—Automatic manoeuvring for parking
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo or light sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
Abstract
The invention discloses a vehicle control method based on an artificial intelligence dangerous scene of automatic driving, which is used for a vehicle with a camera and comprises the following steps: acquiring a target image of a target area through the camera; inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade; and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park. The invention also discloses a vehicle control device based on the artificial intelligence dangerous scene of automatic driving, a vehicle and a storage medium. By using the method, the dangerous scene analysis level output by the model does not depend on the preset threshold, and the accuracy of the dangerous scene analysis level is higher, so that the automatic driving vehicle can perform parking operation in time when a dangerous scene appears, and the safety of the automatic driving vehicle is improved.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a vehicle control method and device based on an artificial intelligence dangerous scene of automatic driving, a vehicle and a storage medium.
Background
The automatic driving system is a hot research problem at present, and a general research object is an environmental condition around a driving process, such as finding out and avoiding an obstacle. The video monitoring, radar monitoring or other monitoring modes are used according to the road safety regulations.
At present, in order to enhance the use safety of the automatic driving vehicle, the vehicle dangerous situation of the existing automatic driving vehicle needs to be monitored in the driving process of the automatic driving vehicle. In the prior art, a physical detecting head is used for detecting, such as electronic elements such as a temperature detector and a smoke detector, so as to detect dangerous scenes of a vehicle.
However, the safety of the autonomous vehicle is poor by using the conventional detection method.
Disclosure of Invention
The invention mainly aims to provide a vehicle control method, a vehicle control device, a vehicle and a storage medium based on an artificial intelligence dangerous scene of automatic driving, and aims to solve the technical problem that the safety of an automatic driving vehicle is poor due to the adoption of the existing detection mode in the prior art.
In order to achieve the above object, the present invention provides a vehicle control method based on an artificial intelligence dangerous scene of automatic driving, which is used for a vehicle with a camera, and comprises the following steps:
acquiring a target image of a target area through the camera;
inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade;
and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park.
Optionally, before the step of inputting the target image into a dangerous scene analysis model and obtaining a dangerous scene analysis grade, the method further includes:
extracting the region of interest of the target image to obtain an initial image corresponding to the region of interest;
carrying out binarization processing on the initial image to obtain an image to be detected;
the step of inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps:
and inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade.
Optionally, the cameras include a plurality of cameras, and the image to be detected includes a plurality of images to be detected corresponding to the plurality of cameras; before the step of inputting the image to be detected into the dangerous scene analysis model and obtaining the dangerous scene analysis grade, the method further comprises:
performing characteristic marking on each image to be detected to obtain a plurality of dangerous characteristic areas corresponding to each image to be detected;
screening a plurality of dangerous characteristic areas of each image to be detected to obtain a dangerous characteristic set corresponding to each image to be detected;
the step of inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps:
and inputting a plurality of dangerous feature sets corresponding to a plurality of images to be detected into a dangerous scene analysis model to obtain dangerous scene analysis grades.
Optionally, before the step of inputting a plurality of sets of risk features corresponding to a plurality of images to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade, the method further includes:
acquiring a plurality of preset danger feature sets and preset danger scene analysis levels corresponding to the preset danger feature sets;
inputting a plurality of preset danger feature sets and the preset danger scene analysis levels into an initial analysis model for training to obtain the danger scene analysis model.
Optionally, the step of controlling the vehicle to park when the dangerous scene analysis level reaches a first preset dangerous level includes:
when the dangerous scene analysis level reaches a first preset dangerous level, outputting the dangerous scene analysis level;
when a determination operation sent aiming at the dangerous scene analysis level is received, determining a voting rate corresponding to the determination operation;
when the voting rate is higher than a preset rate, obtaining a parking motion track based on the current position information of the vehicle;
and carrying out parking operation by using the parking motion trail.
Optionally, the step of performing a parking operation by using the parking motion trajectory includes:
when the vehicle moves according to the parking motion trail, acquiring a parking image of a parking detection area of the vehicle;
correcting the parking motion trail by using the parking image to obtain a corrected parking motion trail;
and carrying out parking operation by using the corrected parking motion trail.
Optionally, after the step of controlling the vehicle to perform the parking operation when the dangerous scene analysis level reaches a first preset dangerous level, the method further includes:
and when the dangerous scene analysis level reaches a second preset dangerous level, controlling the vehicle to execute parking operation and controlling the vehicle to open a door and a window.
In addition, in order to achieve the above object, the present invention further provides an automatic driving-based artificial intelligence dangerous scene vehicle control device, for a vehicle with a camera, the device comprising:
the acquisition module is used for acquiring a target image of a target area through the camera;
the analysis module is used for inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade;
and the control module is used for controlling the vehicle to perform parking operation when the dangerous scene analysis level reaches a first preset dangerous level.
Further, to achieve the above object, the present invention also proposes a vehicle including: the vehicle control method comprises a camera, a memory, a processor and a vehicle control program stored on the memory and running on the processor based on the artificial intelligence danger scene of automatic driving, wherein the vehicle control program based on the artificial intelligence danger scene of automatic driving realizes the steps of the vehicle control method based on the artificial intelligence danger scene of automatic driving according to any one of the above items when being executed by the processor.
In addition, in order to achieve the above object, the present invention further provides a storage medium, wherein a vehicle control program based on an artificial intelligence hazard scenario of automatic driving is stored on the storage medium, and the vehicle control program based on the artificial intelligence hazard scenario of automatic driving realizes the steps of the vehicle control method based on the artificial intelligence hazard scenario of automatic driving according to any one of the above items when being executed by a processor.
The technical scheme of the invention provides a vehicle control method based on an artificial intelligence dangerous scene of automatic driving, which is used for a vehicle with a camera, and comprises the following steps: acquiring a target image of a target area through the camera; inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade; and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park.
In the existing method, a dangerous scene is detected through various electronic detection elements, various electronic elements judge the dangerous scene by using a preset threshold, but in an actual scene, the effectiveness of the preset threshold is poor, so that the judgment accuracy of the electronic elements is low, the accuracy of a danger level analysis result is low, and further, when an automatic driving vehicle encounters the dangerous scene, the parking operation cannot be performed in time, so that the safety of the automatic driving vehicle is poor. The method provided by the invention is used for analyzing by using the dangerous scene analysis model, the dangerous scene analysis level output by the model does not depend on the preset threshold value, and the accuracy of the dangerous scene analysis level is higher, so that the automatic driving vehicle can perform parking operation in time when a dangerous scene appears, and the safety of the automatic driving vehicle is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic vehicle configuration diagram of a hardware operating environment in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a vehicle control method based on an automated driving artificial intelligence dangerous scene according to the present invention;
FIG. 3 is a schematic structural view of a further embodiment of the vehicle of the present invention;
fig. 4 is a block diagram illustrating a first embodiment of a vehicle control apparatus for an artificial intelligence hazard scenario based on autonomous driving according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
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.
Referring to fig. 1, fig. 1 is a vehicle configuration diagram of a hardware operating environment according to an embodiment of the present invention.
Generally, a vehicle includes: a camera 307, at least one processor 301, a memory 302, and an automated driving artificial intelligence based hazard scenario vehicle control program stored on and executable on the memory, the automated driving artificial intelligence based hazard scenario vehicle control program configured to implement the steps of the automated driving artificial intelligence based hazard scenario vehicle control method as previously described.
The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 301 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. The processor 301 may further include an AI (Artificial Intelligence) processor for processing vehicle control method operations related to the automated driving based Artificial Intelligence risk scenario such that the vehicle control method model based on the automated driving based Artificial Intelligence risk scenario may be trained and learned autonomously, improving efficiency and accuracy.
In some embodiments, the terminal may further include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. Various peripheral devices may be connected to communication interface 303 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power source 306.
The communication interface 303 may be used to connect at least one peripheral device related to I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302 and the communication interface 303 may be implemented on a single chip or circuit board, which is not limited in this embodiment.
The Radio Frequency circuit 304 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 304 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 304 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 304 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 304 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 305 is a touch display screen, the display screen 305 also has the ability to capture touch signals on or over the surface of the display screen 305. The touch signal may be input to the processor 301 as a control signal for processing. At this point, the display screen 305 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 305 may be one, the front panel of the electronic device; in other embodiments, the display screens 305 may be at least two, respectively disposed on different surfaces of the electronic device or in a folded design; in still other embodiments, the display screen 305 may be a flexible display screen disposed on a curved surface or a folded surface of the electronic device. Even further, the display screen 305 may be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 305 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), and the like.
The power supply 306 is used to power various components in the electronic device. The power source 306 may be alternating current, direct current, disposable or rechargeable. When the power source 306 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 is not intended to be limiting of the vehicle and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
In addition, an embodiment of the present invention further provides a storage medium, where a vehicle control program based on an artificial intelligence hazard scenario for automatic driving is stored, and the vehicle control program based on the artificial intelligence hazard scenario for automatic driving is executed by a processor to implement the steps of the vehicle control method based on the artificial intelligence hazard scenario for automatic driving as described above. Therefore, a detailed description thereof will be omitted. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in the embodiments of the storage medium referred to in the present application, reference is made to the description of the embodiments of the method of the present application. Determining by way of example, the program instructions may be deployed for execution on one vehicle or on multiple vehicles at one site or distributed across multiple sites and interconnected by a communication network.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a storage medium and can include the processes of the embodiments of the methods described above when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Based on the hardware structure, the embodiment of the vehicle control method based on the artificial intelligence dangerous scene of automatic driving is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a vehicle control method based on an artificial intelligence dangerous scene of automatic driving, the method is used for a vehicle, and the method comprises the following steps:
step S11: and acquiring a target image of a target area through the camera.
The execution subject of the present invention is a vehicle, the vehicle is installed with a vehicle control program based on an artificial intelligence risk scenario of automatic driving, and when the vehicle executes the vehicle control program based on the artificial intelligence risk scenario of automatic driving, the steps of the vehicle control method based on the artificial intelligence risk scenario of automatic driving of the present invention are implemented. Meanwhile, the vehicle is provided with a camera, a shooting area of the camera is a target area, when the camera shoots the target area, a target image is obtained, and generally, the vehicle control camera shoots the image in real time so as to carry out the steps of the vehicle control method based on the artificial intelligence dangerous scene of automatic driving in real time.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a further embodiment of the vehicle of the present invention, in fig. 3, A, B and C are three cameras, each corresponding to a target area, for respectively acquiring three target images at a time, where Y0 is an algorithm center of the vehicle, and the algorithm center is used for executing steps S12 and S13 of the present invention.
It can be understood that, in the invention, the vehicle is an automatic driving vehicle, and the vehicle is also provided with an external vehicle obstacle monitoring module, a central electronic processing module, a driving information module, a sending and controlling module, an automatic driving force calculating module, a differential electronic module, an electronic control coupling differential, a safety door and window controller and an internal danger monitoring module, which are used for respectively realizing each function in the automatic driving process.
Step S12: and inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade.
After different target images are input into the dangerous scene analysis model, different dangerous scene analysis levels are output, and the higher the dangerous scene analysis level is, the more dangerous is indicated.
Specifically, the target image is an entity image of the target area, and further processing is required to input the dangerous scene analysis model, as follows:
before the step of inputting the target image into a dangerous scene analysis model and obtaining a dangerous scene analysis grade, the method further includes: extracting the region of interest of the target image to obtain an initial image corresponding to the region of interest; carrying out binarization processing on the initial image to obtain an image to be detected; the step of inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps: and inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade.
The camera comprises a plurality of cameras, and the image to be detected comprises a plurality of images to be detected corresponding to the cameras; before the step of inputting the image to be detected into the dangerous scene analysis model and obtaining the dangerous scene analysis grade, the method further comprises: performing characteristic marking on each image to be detected to obtain a plurality of dangerous characteristic areas corresponding to each image to be detected; screening a plurality of dangerous characteristic areas of each image to be detected to obtain a dangerous characteristic set corresponding to each image to be detected; the step of inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps: and inputting a plurality of dangerous feature sets corresponding to a plurality of images to be detected into a dangerous scene analysis model to obtain dangerous scene analysis grades.
Firstly, region-of-interest extraction needs to be performed on a plurality of target images (one camera corresponds to one target image) respectively corresponding to a plurality of cameras, and a partial image corresponding to a region of interest of one target image is an initial image. And then, carrying out binarization processing on each initial image, wherein the obtained binarized image is the image to be detected. When each image to be detected is subjected to feature marking, each image to be detected can mark a plurality of dangerous feature areas, the plurality of dangerous feature areas of one image to be detected can be divided into at least one dangerous feature set, wherein screening means that areas with obvious dangerous features (large values) in the plurality of dangerous feature areas are left, and other areas are deleted. And for a plurality of images to be detected, a plurality of danger characteristic sets can be corresponding, and then the plurality of danger characteristic sets corresponding to the plurality of images to be detected are input into a danger scene analysis model to obtain the danger scene analysis grade.
Further, before the step of inputting a plurality of sets of risk features corresponding to a plurality of images to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade, the method further includes: acquiring a plurality of preset danger feature sets and preset danger scene analysis levels corresponding to the preset danger feature sets; inputting a plurality of preset danger feature sets and the preset danger scene analysis levels into an initial analysis model for training to obtain the danger scene analysis model.
In specific application, a plurality of preset dangerous images corresponding to a dangerous scene are acquired, and then the plurality of preset dangerous images are subjected to region-of-interest extraction, binarization processing, feature labeling and feature region screening in sequence according to the method to obtain a plurality of corresponding preset feature sets. Then, a training process is performed, wherein the preset danger scene analysis levels corresponding to the preset danger feature sets can include multiple levels, that is: the preset dangerous scene analysis level may refer to all levels, so that the analysis accuracy of the obtained dangerous scene analysis model is high, for example, the dangerous scene analysis model has 10 corresponding dangerous levels, and the preset dangerous scene analysis level for training and the corresponding plurality of preset dangerous feature sets also refer to the 10 dangerous levels.
The initial analysis model may be various types of neural network models or decision tree models, and the invention is not limited thereto. E.g., CNN neural networks, etc.
Step S13: and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park.
It should be noted that the first preset danger level may be set by the user based on the requirement, the present invention is not limited thereto, when the danger scene analysis level reaches the first preset danger level, it indicates that the vehicle is in the danger scene, i.e., parking is performed, and when parking is completed, the engine is controlled to stop, so that the passenger can get off in time to escape from the danger.
Further, the step of controlling the vehicle to perform a parking operation when the dangerous scene analysis level reaches a first preset dangerous level includes: when the dangerous scene analysis level reaches a first preset dangerous level, outputting the dangerous scene analysis level; when a determination operation sent aiming at the dangerous scene analysis level is received, determining a voting rate corresponding to the determination operation; when the voting rate is higher than a preset rate, obtaining a parking motion track based on the current position information of the vehicle; and carrying out parking operation by using the parking motion trail.
In this embodiment, each seat may be provided with one voter, when the dangerous scene analysis level reaches a first preset dangerous level, the dangerous scene analysis level is output, so that a passenger can know the dangerous scene analysis level in time, and then the passenger presses the voter based on the dangerous scene analysis level, a ratio of the number of the voters pressed to the total number of the voters is a voting rate, and when the voting rate is higher than the preset ratio, it indicates that the passenger feels that the dangerous scene is serious and needs to perform a parking operation. The preset ratio can be set by a user based on requirements, and can also be determined based on the actual passenger carrying capacity of the vehicle, and the invention is not limited.
When the voting rate is not higher than the preset rate, the passenger feels that the dangerous scene is not serious, and manual parking operation or other continuous driving operation can be performed.
The current position information of the vehicle may include the current position of the vehicle and road condition information of the position where the vehicle is located, so as to obtain a parking motion trajectory, which may be obtained based on an automatic driving algorithm.
Further, the step of performing a parking operation using the parking motion trajectory includes: when the vehicle moves according to the parking motion trail, acquiring a parking image of a parking detection area of the vehicle; correcting the parking motion trail by using the parking image to obtain a corrected parking motion trail; and carrying out parking operation by using the corrected parking motion trail.
During parking, the parking motion trajectory can be adjusted in real time, wherein the parking detection areas generally refer to the areas of the side direction and the rear direction of the vehicle when the vehicle is parked.
Further, after the step of controlling the vehicle to perform the parking operation when the dangerous scene analysis level reaches a first preset dangerous level, the method further includes: and when the dangerous scene analysis level reaches a second preset dangerous level, controlling the vehicle to execute parking operation and controlling the vehicle to open a door and a window.
It should be noted that the second preset risk level may be set by the user based on the requirement, and usually, the second preset risk level is greater than the first preset risk level; when the dangerous scene analysis level reaches a second preset dangerous level, the vehicle is in a very dangerous scene and needs to be stopped in time, and the door is opened to open the window, so that passengers can get off the vehicle in time.
The technical scheme of the invention provides a vehicle control method based on an artificial intelligence dangerous scene of automatic driving, which is used for a vehicle with a camera, and comprises the following steps: acquiring a target image of a target area through the camera; inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade; and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park.
In the existing method, a dangerous scene is detected through various electronic detection elements, various electronic elements judge the dangerous scene by using a preset threshold, but in an actual scene, the effectiveness of the preset threshold is poor, so that the judgment accuracy of the electronic elements is low, the accuracy of a danger level analysis result is low, and further, when an automatic driving vehicle encounters the dangerous scene, the parking operation cannot be performed in time, so that the safety of the automatic driving vehicle is poor. The method provided by the invention is used for analyzing by using the dangerous scene analysis model, the dangerous scene analysis level output by the model does not depend on the preset threshold value, and the accuracy of the dangerous scene analysis level is higher, so that the automatic driving vehicle can perform parking operation in time when a dangerous scene appears, and the safety of the automatic driving vehicle is improved.
Referring to fig. 4, fig. 4 is a block diagram illustrating a first embodiment of a vehicle control apparatus based on an artificial intelligence hazard scenario of automatic driving, which is applied to a vehicle, and includes:
an obtaining module 10, configured to obtain a target image of a target area through the camera;
the analysis module 20 is configured to input the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade;
and the control module 30 is configured to control the vehicle to perform a parking operation when the dangerous scene analysis level reaches a first preset dangerous level.
It should be noted that, since the steps executed by the apparatus of this embodiment are the same as the steps of the foregoing method embodiment, the specific implementation and the achievable technical effects thereof can refer to the foregoing embodiment, and are not described herein again.
The above description is only an alternative embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A vehicle control method based on an artificial intelligence dangerous scene of automatic driving is characterized in that the method is used for a vehicle with a camera, and comprises the following steps:
acquiring a target image of a target area through the camera;
inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade;
and when the dangerous scene analysis level reaches a first preset dangerous level, controlling the vehicle to park.
2. The method of claim 1, wherein prior to the step of inputting the target image into a hazardous scene analysis model to obtain a hazardous scene analysis rating, the method further comprises:
extracting the region of interest of the target image to obtain an initial image corresponding to the region of interest;
carrying out binarization processing on the initial image to obtain an image to be detected;
the step of inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps:
and inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade.
3. The method according to claim 2, wherein the cameras comprise a plurality of cameras, and the image to be detected comprises a plurality of images to be detected corresponding to the plurality of cameras; before the step of inputting the image to be detected into the dangerous scene analysis model and obtaining the dangerous scene analysis grade, the method further comprises:
performing characteristic marking on each image to be detected to obtain a plurality of dangerous characteristic areas corresponding to each image to be detected;
screening a plurality of dangerous characteristic areas of each image to be detected to obtain a dangerous characteristic set corresponding to each image to be detected;
the step of inputting the image to be detected into a dangerous scene analysis model to obtain a dangerous scene analysis grade comprises the following steps:
and inputting a plurality of dangerous feature sets corresponding to a plurality of images to be detected into a dangerous scene analysis model to obtain dangerous scene analysis grades.
4. The method of claim 3, wherein before the step of inputting a plurality of sets of risk features corresponding to a plurality of images to be detected into a risk scene analysis model to obtain a risk scene analysis rating, the method further comprises:
acquiring a plurality of preset danger feature sets and preset danger scene analysis levels corresponding to the preset danger feature sets;
inputting a plurality of preset danger feature sets and the preset danger scene analysis levels into an initial analysis model for training to obtain the danger scene analysis model.
5. The method of claim 1, wherein the step of controlling the vehicle to park when the risk scenario analysis level reaches a first preset risk level comprises:
when the dangerous scene analysis level reaches a first preset dangerous level, outputting the dangerous scene analysis level;
when a determination operation sent aiming at the dangerous scene analysis level is received, determining a voting rate corresponding to the determination operation;
when the voting rate is higher than a preset rate, obtaining a parking motion track based on the current position information of the vehicle;
and carrying out parking operation by using the parking motion trail.
6. The method of claim 5, wherein said step of using said parking motion profile to perform a parking maneuver comprises:
when the vehicle moves according to the parking motion trail, acquiring a parking image of a parking detection area of the vehicle;
correcting the parking motion trail by using the parking image to obtain a corrected parking motion trail;
and carrying out parking operation by using the corrected parking motion trail.
7. The method of claim 1, wherein after the step of controlling the vehicle to perform a parking operation when the risk scenario analysis level reaches a first preset risk level, the method further comprises:
and when the dangerous scene analysis level reaches a second preset dangerous level, controlling the vehicle to execute parking operation and controlling the vehicle to open a door and a window.
8. A vehicle control device based on an artificial intelligence dangerous scene of automatic driving is characterized in that, the device is used for a vehicle with a camera, and the device comprises:
the acquisition module is used for acquiring a target image of a target area through the camera;
the analysis module is used for inputting the target image into a dangerous scene analysis model to obtain a dangerous scene analysis grade;
and the control module is used for controlling the vehicle to perform parking operation when the dangerous scene analysis level reaches a first preset dangerous level.
9. A vehicle, characterized in that the vehicle comprises: a camera, a memory, a processor and a vehicle control program stored on the memory and running on the processor the autonomous driving artificial intelligence based hazard scenario, the vehicle control program being executed by the processor to implement the steps of the method of any one of claims 1 to 7.
10. A storage medium having stored thereon a vehicle control program for an automated driving-based artificial intelligence hazard scenario, the vehicle control program when executed by a processor implementing the steps of the method for vehicle control for an automated driving-based artificial intelligence hazard scenario of any one of claims 1 to 7.
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