CN116834752A - Power-assisted control method and device for self-adaptive driving - Google Patents
Power-assisted control method and device for self-adaptive driving Download PDFInfo
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- 230000003044 adaptive effect Effects 0.000 claims description 29
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0809—Driver authorisation; Driver identity check
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Abstract
The application relates to the technical field of vehicle control, in particular to a self-adaptive driving power-assisted control method and device, wherein the method comprises the following steps: acquiring driver identity information, and acquiring driving environment information and driving parameter information in the running process of a vehicle; identifying each driving scene in the driving environment information, and obtaining driving parameters corresponding to each driving scene according to the driving parameter information; inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve; and when a confirmation instruction for the assistance curve is received, performing assistance control on the vehicle according to the assistance curve. According to the application, the power-assisted curve is updated by collecting the driving environment information and the driving parameter information in the running process of the vehicle and utilizing the pre-trained deep learning model, namely, the power-assisted curve is updated according to the running data of the driver, different drivers can obtain different power-assisted curves, and the convenience of the driver on the driving of the vehicle is improved by targeted power-assisted control.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a self-adaptive driving power-assisted control method and device.
Background
Along with the continuous upgrading and iteration of the electric, intelligent and networking of the new energy vehicles, the intelligent demands of users are also continuously improved. For the new energy automobile, the action of a driver for operating the automobile can be divided into the steps of stepping on an accelerator, stepping on a brake and rotating a steering wheel, so that the normal driving of the automobile is completed. In general, automobile manufacturers only provide a uniform fixed mode such as a common mode, a sport mode, an energy-saving mode and the like for drivers, each mode corresponds to a fixed power-assisted curve for power-assisted control of stepping on an accelerator, stepping on a brake and rotating a steering wheel, and for anyone, the same power-assisted curve is used when the power-assisted control is performed on a vehicle, but driving habits and operating preferences of each driver are different, and the uniform power-assisted control causes that the driver cannot conveniently drive the vehicle.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The application provides a self-adaptive driving power-assisted control method and device, which are used for solving the technical problem that a driver cannot conveniently drive a vehicle due to uniform power-assisted control because the same power-assisted curve is used when the vehicle is subjected to power-assisted control in the related art.
In order to achieve the above purpose, the present application adopts the following technical scheme:
an embodiment of a first aspect of the present application provides a power-assisted control method for adaptive driving, including the steps of:
acquiring driver identity information, and acquiring driving environment information and driving parameter information in the running process of a vehicle;
identifying each driving scene in the driving environment information, and obtaining driving parameters corresponding to each driving scene according to the driving parameter information;
inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve;
and when a confirmation instruction for the assistance curve is received, performing assistance control on the vehicle according to the assistance curve.
According to the technical means, the power-assisted curve is updated by acquiring the driving environment information and the driving parameter information in the running process of the vehicle and using the pre-trained deep learning model, namely, the power-assisted curve is updated according to the running data of the driver, different power-assisted curves can be obtained by different drivers, and the convenience of the driver on the driving of the vehicle is improved by targeted power-assisted control.
Optionally, in one embodiment of the present application, the driver identity information includes driver face information and/or driver fingerprint information, the driving environment information includes vehicle road condition information, traffic flow information and obstacle information, the driving scene includes one or more of a start scene, an acceleration scene, a cruise scene, a brake scene and a steering scene, and the driving parameter information includes accelerator pedal acceleration, brake pedal acceleration and steering wheel rotation angle.
According to the technical means, in the embodiment of the application, the personal driving habit and the manipulation preference of the driver are switched by identifying the identity of the driver, storing the facial information of the driver and/or the fingerprint information of the driver and binding the personal driving habit and the manipulation preference of the driver with the driver.
Optionally, in one embodiment of the present application, before the step of acquiring the driver identity information and collecting the driving environment information and the driving parameter information during the driving of the vehicle, the method further includes:
receiving a login instruction of the self-adaptive driving assistance system;
if the driver logs in for the first time, the identity information of the driver is collected and stored;
when a vehicle part position adjustment instruction is received, vehicle part position information is obtained according to the current vehicle part position, and a first corresponding relation between the driver identity information and the vehicle part position information is established.
According to the technical means, the embodiment of the application can directly call the corresponding vehicle part position information, automatically adjust the positions of the vehicle parts such as the rearview mirror, the seat and the like, automatically adjust the positions of the vehicle parts according to personal preference and habit, and realize personalized customization.
Optionally, in one embodiment of the present application, inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve, including:
acquiring all driving scenes and corresponding driving parameters in a preset time period;
and inputting all driving scenes and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve, and establishing a second corresponding relation between the power-assisted curve and the driver identity information.
According to the technical means, the embodiment of the application acquires the historical data according to the identity information of the driver, and updates the power-assisted curve by using all driving scenes and corresponding driving parameters in the preset time period, so that the accuracy of updating the power-assisted curve is improved.
Optionally, in one embodiment of the present application, after the receiving the adaptive driving assistance system login instruction, the method further includes:
if the first login is not performed, acquiring driver identity information, and searching the first corresponding relation and the second corresponding relation according to the driver identity information;
obtaining vehicle part position information corresponding to the driver identity information according to the first corresponding relation, and obtaining a power assisting curve corresponding to the driver identity information according to the second corresponding relation;
and adjusting the position of the vehicle part according to the position information of the vehicle part.
According to the technical means, when the driver uses the vehicle again, the embodiment of the application automatically identifies the identity information of the driver, adjusts the relevant setting to the memory position, realizes self-adaptive interaction with the user, and brings convenient and quick control experience to the user.
Optionally, in one embodiment of the present application, when a confirmation instruction for the assistance curve is received, before performing assistance control on the vehicle according to the assistance curve, the method further includes:
and if the power-assisted curve adjustment instruction is received, adjusting the power-assisted curve according to the power-assisted curve adjustment instruction.
According to the technical means, the embodiment of the application can manually confirm and adjust details on the new power-assisted curve, and further improves individuation and convenience of power-assisted control.
Optionally, in an embodiment of the present application, the deep learning model is a convolutional neural network or a recurrent neural network.
According to the technical means, the deep learning model used in the embodiment of the application can continuously optimize and adjust the fitting parameters of the throttle pedal, the brake pedal and the steering wheel rotation power-assisted curve under different scenes, so as to generate the power-assisted curve capable of self-adaptive driving.
An embodiment of a second aspect of the present application provides an assistance control device for adaptive driving, including:
the acquisition module is used for acquiring the identity information of the driver and acquiring the driving environment information and the driving parameter information in the running process of the vehicle;
the identification module is used for identifying each driving scene in the driving environment information and obtaining driving parameters corresponding to each driving scene according to the driving parameter information;
the input module is used for inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve;
and the control module is used for carrying out power-assisted control on the vehicle according to the power-assisted curve when receiving a confirmation instruction of the power-assisted curve.
An embodiment of the third aspect of the present application provides a vehicle including a memory, a processor, and an adaptive-driving assistance control program stored in the memory and executable on the processor, the steps of the adaptive-driving assistance control method being implemented when the processor executes the adaptive-driving assistance control program.
An embodiment of the fourth aspect of the present application provides a computer-readable storage medium having stored thereon an adaptive-driving assistance control program that, when executed by a processor, implements the steps of the adaptive-driving assistance control method described above.
The application has the beneficial effects that:
(1) According to the application, the power-assisted curve is updated by collecting the driving environment information and the driving parameter information in the running process of the vehicle and utilizing the pre-trained deep learning model, namely, the power-assisted curve is updated according to the running data of the driver, different drivers can obtain different power-assisted curves, and the convenience of the driver on the driving of the vehicle is improved by targeted power-assisted control.
(2) When the driver uses the vehicle again, the application automatically identifies the identity information of the driver, adjusts the relevant setting to the memory position, realizes the self-adaptive interaction with the user, and brings convenient and quick control experience for the user.
(3) According to the method and the device for updating the power-assisted curve, the historical data are acquired according to the identity information of the driver, and the power-assisted curve is updated by using all driving scenes and corresponding driving parameters in the preset time period, so that the accuracy of updating the power-assisted curve is improved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of a power-assisted control method for adaptive driving according to an embodiment of the present application;
FIG. 2 is a schematic diagram of processing logic of a power assist control method for adaptive driving according to an embodiment of the present application;
fig. 3 is a flowchart of a specific embodiment of a power-assisted control method for adaptive driving according to an embodiment of the present application.
FIG. 4 is a schematic structural diagram of an adaptive driving assistance control device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of an internal structure of a vehicle according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a power-assisted control method and apparatus for adaptive driving according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the problem that the same assistance curves are used when the vehicle is subjected to assistance control in the background art, and the uniform assistance control leads to the fact that a driver cannot conveniently drive the vehicle, the application provides an adaptive driving assistance control method, in which the identity information of the driver is acquired, and driving environment information and driving parameter information in the running process of the vehicle are acquired; identifying each driving scene in the driving environment information, and obtaining driving parameters corresponding to each driving scene according to the driving parameter information; inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve; and when a confirmation instruction for the assistance curve is received, performing assistance control on the vehicle according to the assistance curve. According to the embodiment of the application, the power-assisted curve is updated by collecting the driving environment information and the driving parameter information in the driving process of the vehicle and utilizing the pre-trained deep learning model, namely, the power-assisted curve is updated according to the driving data of the driver, different drivers can obtain different power-assisted curves, the convenience of the driver on driving the vehicle is improved by targeted power-assisted control, and the problem that the same power-assisted curve is used when the vehicle is subjected to power-assisted control, and the driver cannot conveniently drive the vehicle due to unified power-assisted control is solved.
Specifically, fig. 1 is a schematic flow chart of a power-assisted control method for adaptive driving according to an embodiment of the present application.
As shown in fig. 1, the power-assisted control method for adaptive driving includes the steps of:
in step S101, driver identity information is acquired, and driving environment information and driving parameter information during running of the vehicle are acquired.
In the embodiment of the application, the vehicle is provided with the vehicle-mounted sensor and the data acquisition module, the identity information of the driver is obtained by using the vehicle-mounted sensor, and the driving environment information and the driving parameter information in the running process of the vehicle are acquired in real time by using the data acquisition module, so that the personalized training of the power-assisted curve is further carried out.
In step S102, each driving scene in the driving environment information is identified, and driving parameters corresponding to each driving scene are obtained according to the driving parameter information.
The embodiment of the application carries out cleaning processing on the driving environment information acquired in real time to obtain the corresponding driving scene, wherein the driving environment information and the driving parameter information are both time sequence data, and the current driving parameter can be corresponding to a certain driving scene.
In one embodiment of the present application, the driver identity information includes driver face information and/or driver fingerprint information, the driving environment information includes vehicle road condition information, traffic flow information and obstacle information, the driving scene includes one or more of a start scene, an acceleration scene, a cruise scene, a brake scene and a steering scene, and the driving parameter information includes accelerator pedal acceleration, brake pedal acceleration and a steering wheel rotation angle.
The embodiment can collect the face information or the fingerprint information of the driver through the camera and the fingerprint sensor, and can collect the face information and the fingerprint information of the driver, so that the identification accuracy is improved. And the vehicle-mounted sensors such as a laser radar, a millimeter wave radar, a camera and a GPS are used for collecting and processing surrounding environment information such as vehicle road conditions, vehicle flow, obstacles and the like in real time, and simultaneously, the information of stepping on an accelerator, stepping on a brake and rotating a steering wheel is collected in real time. The data obtained by processing the driving environment information is a driving scene, which is divided into a starting scene, an accelerating scene, a cruising scene, a braking scene, a steering scene and other common scenes, and the driving parameters are accelerator pedal acceleration, brake pedal acceleration and steering wheel rotation angle so as to be conveniently input into a deep learning model. In daily driving behaviors, the data acquisition module acquires and processes information of the vehicle stepping on the accelerator, stepping on the brake, rotating the steering wheel and surrounding environment information in real time, and provides data for updating a power-assisted curve.
According to the embodiment of the application, the identity of the driver is identified, the face information and/or the fingerprint information of the driver are stored, and the face information and/or the fingerprint information are bound with the driving habit and the operating preference of the driver and the driver, so that the personalized switching of the driving habit and the operating preference of the driver is finished.
In one embodiment of the present application, step S101 further includes: receiving a login instruction of the self-adaptive driving assistance system; if the driver logs in for the first time, the identity information of the driver is collected and stored; when a vehicle part position adjustment instruction is received, vehicle part position information is obtained according to the current vehicle part position, and a first corresponding relation between driver identity information and the vehicle part position information is established.
In the embodiment of the application, when a driver uses the self-adaptive driving assistance system for the first time, biological information such as the face, fingerprint and the like of the individual is required to be input through the camera sensor and the fingerprint sensor. The positions of the vehicle parts refer to the positions of the parts such as the rearview mirror and the seat, and the positions of the vehicle parts such as the outer rearview mirror and the seat are adjusted according to personal preference and are bound with the identity information of the driver, so that when the driver drives next time, the corresponding position information of the vehicle parts can be directly called, the positions of the vehicle parts such as the rearview mirror and the seat are automatically adjusted, the positions of the vehicle parts are automatically adjusted according to personal preference and habit, and personalized customization is realized.
In step S103, each driving scene and the corresponding driving parameter are input into a deep learning model trained in advance, and a power assisting curve is obtained.
According to the embodiment of the application, each driving scene and corresponding driving parameters are input into a pre-trained deep learning model, calculation is performed according to the data, and the power curves of stepping on the accelerator, stepping on the brake and rotating the steering wheel in different scenes are adjusted.
In one embodiment of the present application, step S103 specifically includes: acquiring all driving scenes and corresponding driving parameters in a preset time period; and inputting all driving scenes and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve, and establishing a second corresponding relation between the power-assisted curve and the identity information of the driver.
According to the embodiment of the application, learning can be performed according to the historical data and the current data, and the power-assisted curves of stepping on the accelerator, stepping on the brake and rotating the steering wheel at different positions can be continuously calculated, so that the accuracy of the power-assisted curves is improved. The history data of the driver is not available when the driver logs in for the first time, the driver can select the data of each mode provided by the system, namely, the driving parameters such as stepping on the accelerator, stepping on the brake, rotating the steering wheel and the like corresponding to different scenes such as starting, accelerating, cruising, braking, steering and the like, and the selected driving scene and the corresponding driving parameters are bound with the identity information of the driver.
According to the embodiment of the application, the historical data is acquired according to the identity information of the driver, and the power-assisted curve is updated by using all driving scenes and corresponding driving parameters in the preset time period, so that the accuracy of updating the power-assisted curve is improved.
In one embodiment of the present application, after receiving the adaptive driving assistance system login instruction, the method further includes: if the first login is not performed, acquiring driver identity information, and searching a first corresponding relation and a second corresponding relation according to the driver identity information; obtaining vehicle part position information corresponding to the driver identity information according to the first corresponding relation, and obtaining a power assisting curve corresponding to the driver identity information according to the second corresponding relation; the vehicle component position is adjusted according to the vehicle component position information.
When the driver uses the vehicle again, the embodiment of the application automatically identifies the identity information of the driver, adjusts the relevant setting to the memory position, realizes self-adaptive interaction with the user, and brings convenient and quick control experience for the user.
In step S104, when a confirmation instruction for the assist curve is received, the assist control is performed on the vehicle in accordance with the assist curve.
According to the embodiment of the application, the power-assisted curve change is fed back to the driver in a certain time period, and whether the modification is confirmed or not is selected, so that the personalized driving requirement of the driver is met.
In one embodiment of the present application, when a confirmation instruction for the assist curve is received, before performing the assist control on the vehicle according to the assist curve, the method further includes: and if the power-assisted curve adjustment instruction is received, adjusting the power-assisted curve according to the power-assisted curve adjustment instruction.
The embodiment of the application can prompt a driver to update the self-adaptive driving power assisting system after the power assisting curve is obtained, and manually confirm and adjust details on the new power assisting curve, thereby further improving individuation and convenience of power assisting control.
In one embodiment of the application, the deep learning model is a convolutional neural network or a recurrent neural network. The deep learning model adopts a Convolutional Neural Network (CNN) or a cyclic neural network (RNN), and for training of the deep learning model, the historical data and the current period input data of the last year can be used as training data sets of the deep learning model, so that the deep learning model can calculate and continuously optimize and adjust fitting parameters of accelerator stepping, brake stepping and steering wheel rotation boosting curves under different vehicle speeds, turning and braking scenes, and a boosting curve capable of self-adapting driving is generated.
In the embodiment of the present application, as shown in fig. 2, step A1, collecting driving environment information and driving parameter information during driving of a vehicle by using a data collecting module; step A2, identifying driver identity information in a driver identification system; a3, switching the preference positions of the vehicles and the preference of driving; step A4, performing power-assisted control by using a power-assisted curve in a vehicle execution control module; and step A5, inputting the data into a pre-trained deep learning model for training a vehicle power-assisted curve.
In one embodiment, as shown in fig. 3, step B1, identification;
step B2, judging whether the driver is a new driver; if yes, executing the steps C1 and C2; if not, executing the step B3;
step B3, adjusting the rearview mirror and the seat to a memory position;
step B4, adjusting the sensitivity of the boosting of the steering wheel when the accelerator is stepped on and the brake is stepped on under different scenes; the power-assisted sensitivity refers to accelerator pedal acceleration, brake pedal acceleration and steering wheel rotation angle;
step B5, acquiring road condition and scene information in the driving process;
step B6, collecting data of the pedal amplitude of the accelerator, the pedal amplitude of the brake and the rotation angle of the steering wheel in the driving process; and executing the step B7;
step C1, setting the position of a rearview mirror and a seat;
step C2, stepping on the accelerator, stepping on the brake and rotating the power sensitivity of the steering wheel under different scenes; and executing the step B7;
and B7, calculating a power assisting curve by using a deep learning algorithm such as a Convolutional Neural Network (CNN).
As shown in fig. 4, the assistance control device 10 for adaptive driving includes: the device comprises an acquisition module 100, an identification module 200, an input module 300 and a control module 400.
Specifically, the acquisition module 100 is configured to acquire driver identity information, and acquire driving environment information and driving parameter information during driving of the vehicle;
the identification module 200 is configured to identify each driving scenario in the driving environment information, and obtain driving parameters corresponding to each driving scenario according to the driving parameter information;
the input module 300 is configured to input each driving scene and corresponding driving parameters into a pre-trained deep learning model, so as to obtain a power-assisted curve;
the control module 400 is configured to perform power-assisted control on the vehicle according to the power-assisted curve when receiving a confirmation command for the power-assisted curve.
Optionally, the driver identity information includes driver face information and/or driver fingerprint information, the driving environment information includes vehicle road condition information, traffic flow information and obstacle information, the driving scene includes one or more of a start scene, an acceleration scene, a cruising scene, a braking scene and a steering scene, and the driving parameter information includes accelerator pedal acceleration, brake pedal acceleration and steering wheel rotation angle.
Optionally, the power-assisted control device 10 for adaptive driving further includes: the adjusting module is used for receiving a login instruction of the self-adaptive driving assistance system; if the driver logs in for the first time, the identity information of the driver is collected and stored; when a vehicle part position adjustment instruction is received, vehicle part position information is obtained according to the current vehicle part position, and a first corresponding relation between driver identity information and the vehicle part position information is established.
Optionally, the input module 300 includes:
the acquisition unit is used for acquiring all driving scenes and corresponding driving parameters in a preset time period;
and the input unit is used for inputting all driving scenes and corresponding driving parameters into a pre-trained deep learning model, obtaining a power-assisted curve, and establishing a second corresponding relation between the power-assisted curve and the identity information of the driver.
Optionally, the input module 300 further includes:
the searching unit is used for acquiring the identity information of the driver if the first login is not performed, and searching the first corresponding relation and the second corresponding relation according to the identity information of the driver;
the information acquisition unit is used for obtaining vehicle part position information corresponding to the driver identity information according to the first corresponding relation and obtaining a power assisting curve corresponding to the driver identity information according to the second corresponding relation;
and the position adjusting unit is used for adjusting the position of the vehicle part according to the position information of the vehicle part.
Optionally, the power-assisted control device 10 for adaptive driving further includes:
and the curve adjustment module is used for adjusting the power-assisted curve according to the power-assisted curve adjustment instruction if the power-assisted curve adjustment instruction is received.
Optionally, the deep learning model is a convolutional neural network or a recurrent neural network.
It should be noted that the foregoing explanation of the embodiment of the power-assisted control method for adaptive driving is also applicable to the power-assisted control device for adaptive driving of this embodiment, and will not be repeated here.
According to the self-adaptive driving power-assisted control device provided by the embodiment of the application, the identity information of a driver is acquired, and the driving environment information and the driving parameter information in the running process of a vehicle are acquired; identifying each driving scene in the driving environment information, and obtaining driving parameters corresponding to each driving scene according to the driving parameter information; inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve; and when a confirmation instruction for the assistance curve is received, performing assistance control on the vehicle according to the assistance curve. According to the embodiment of the application, the power-assisted curve is updated by collecting the driving environment information and the driving parameter information in the driving process of the vehicle and utilizing the pre-trained deep learning model, namely, the power-assisted curve is updated according to the driving data of the driver, different drivers can obtain different power-assisted curves, the convenience of the driver on driving the vehicle is improved by targeted power-assisted control, and the problem that the same power-assisted curve is used when the vehicle is subjected to power-assisted control, and the driver cannot conveniently drive the vehicle due to unified power-assisted control is solved.
Fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502.
The processor 502 implements the assist control method of adaptive driving provided in the above embodiment when executing a program.
Further, the vehicle further includes:
a communication interface 503 for communication in the memory 501 and the processor 502.
Memory 501 for storing a computer program executable on processor 502.
The memory 501 may include high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 may be connected to each other via a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Periphera l Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one line is shown in fig. 5, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a chip, the memory 501, the processor 502, and the communication interface 503 may perform communication with each other through internal interfaces.
The processor 502 may be a central processing unit (Central Processing Unit, abbreviated as CPU) or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC) or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the assistance control method of adaptive driving as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can read instructions from and execute instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or part of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. The power-assisted control method for the self-adaptive driving is characterized by comprising the following steps of:
acquiring driver identity information, and acquiring driving environment information and driving parameter information in the running process of a vehicle;
identifying each driving scene in the driving environment information, and obtaining driving parameters corresponding to each driving scene according to the driving parameter information;
inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve;
and when a confirmation instruction for the assistance curve is received, performing assistance control on the vehicle according to the assistance curve.
2. The assistance control method for adaptive driving as claimed in claim 1, wherein the driver's identity information includes driver's face information and/or driver's fingerprint information, the driving environment information includes vehicle road condition information, traffic flow information, and obstacle information, the driving scene includes one or more of a start scene, an acceleration scene, a cruise scene, a brake scene, and a steering scene, and the driving parameter information includes accelerator pedal acceleration, brake pedal acceleration, and a steering wheel rotation angle.
3. The power assist control method for adaptive driving as set forth in claim 1, wherein before said acquiring driver's identity information and collecting driving environment information and driving parameter information during running of the vehicle, further comprising:
receiving a login instruction of the self-adaptive driving assistance system;
if the driver logs in for the first time, the identity information of the driver is collected and stored;
when a vehicle part position adjustment instruction is received, vehicle part position information is obtained according to the current vehicle part position, and a first corresponding relation between the driver identity information and the vehicle part position information is established.
4. The assistance control method for adaptive driving as claimed in claim 3, wherein inputting each of said driving scenes and corresponding driving parameters into a deep learning model trained in advance, obtaining an assistance curve, comprises:
acquiring all driving scenes and corresponding driving parameters in a preset time period;
and inputting all driving scenes and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve, and establishing a second corresponding relation between the power-assisted curve and the driver identity information.
5. The adaptive driving assistance control method according to claim 4, wherein after receiving the adaptive driving assistance system login instruction, further comprising:
if the first login is not performed, acquiring driver identity information, and searching the first corresponding relation and the second corresponding relation according to the driver identity information;
obtaining vehicle part position information corresponding to the driver identity information according to the first corresponding relation, and obtaining a power assisting curve corresponding to the driver identity information according to the second corresponding relation;
and adjusting the position of the vehicle part according to the position information of the vehicle part.
6. The assistance control method for adaptive driving according to claim 1, wherein when a confirmation instruction for the assistance curve is received, before performing assistance control on the vehicle in accordance with the assistance curve, further comprising:
and if the power-assisted curve adjustment instruction is received, adjusting the power-assisted curve according to the power-assisted curve adjustment instruction.
7. The assistance control method for adaptive driving as claimed in claim 1, wherein the deep learning model is a convolutional neural network or a cyclic neural network.
8. An assist control device for adaptive driving, comprising:
the acquisition module is used for acquiring the identity information of the driver and acquiring the driving environment information and the driving parameter information in the running process of the vehicle;
the identification module is used for identifying each driving scene in the driving environment information and obtaining driving parameters corresponding to each driving scene according to the driving parameter information;
the input module is used for inputting each driving scene and corresponding driving parameters into a pre-trained deep learning model to obtain a power-assisted curve;
and the control module is used for carrying out power-assisted control on the vehicle according to the power-assisted curve when receiving a confirmation instruction of the power-assisted curve.
9. A vehicle comprising a memory, a processor and an adaptive-driving assistance control program stored in the memory and operable on the processor, the processor implementing the steps of the adaptive-driving assistance control method according to any one of claims 1-7 when executing the adaptive-driving assistance control program.
10. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon an adaptive driving assistance control program, which when executed by a processor, implements the steps of the adaptive driving assistance control method according to any one of claims 1 to 7.
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| CN202310951441.9A CN116834752A (en) | 2023-07-31 | 2023-07-31 | Power-assisted control method and device for self-adaptive driving |
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| CN202310951441.9A CN116834752A (en) | 2023-07-31 | 2023-07-31 | Power-assisted control method and device for self-adaptive driving |
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| CN202310951441.9A Pending CN116834752A (en) | 2023-07-31 | 2023-07-31 | Power-assisted control method and device for self-adaptive driving |
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