CN115687761A - Vehicle driving recommendation method, device and equipment and vehicle - Google Patents
Vehicle driving recommendation method, device and equipment and vehicle Download PDFInfo
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- CN115687761A CN115687761A CN202211344611.9A CN202211344611A CN115687761A CN 115687761 A CN115687761 A CN 115687761A CN 202211344611 A CN202211344611 A CN 202211344611A CN 115687761 A CN115687761 A CN 115687761A
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Abstract
The invention discloses a recommendation method, a recommendation device and a recommendation device for vehicle driving and a vehicle, which are characterized by firstly responding to a personalized configuration recommendation instruction and acquiring first data of a driver, wherein the first data comprises characteristic information and environmental data of the driver; then, the first data is input into a driving configuration parameter recommendation model which is acquired in advance, and the driving configuration parameters are output, so that recommendation information corresponding to the driving configuration parameters is sent, and the recommendation information is displayed for the driver. According to the embodiment of the invention, the driving configuration parameters which best accord with the habit of the driver can be matched and recommended according to the characteristic information and the current environment information of the driver, so that the driving experience of a user can be improved.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a recommendation method and device for vehicle driving, electronic equipment and a vehicle.
Background
With the advancement of technology, intelligent driving is becoming more and more popular. At present, the intelligent driving technology only meets the requirement of safety of intelligent driving through the transverse and longitudinal control of the machinery of the vehicle, but no analysis and corresponding solution are provided for the comfort and individuation requirements of vehicle driving, and further improvement is needed.
Disclosure of Invention
The embodiment of the invention provides a vehicle driving recommendation method and device, electronic equipment and a vehicle, and aims to solve the problem that the user experience is poor due to the fact that the existing vehicle driving cannot recommend driving parameters according to the personalized requirements of users.
The embodiment of the invention provides a recommendation method for vehicle driving, which comprises the following steps:
responding to a personalized configuration recommendation instruction, and acquiring first data of a driver, wherein the first data comprises characteristic information and environment data of the driver;
inputting the first data into a pre-acquired driving configuration parameter recommendation model, and outputting driving configuration parameters;
and sending recommendation information corresponding to the driving configuration parameters to show the recommendation information to the driver.
Correspondingly, the embodiment of the invention also provides a recommendation device for vehicle driving, which comprises:
the system comprises a first data acquisition module, a second data acquisition module and a recommendation module, wherein the first data acquisition module is used for responding to a personalized configuration recommendation instruction and acquiring first data of a driver, and the first data comprises characteristic information and environmental data of the driver;
the output module is used for inputting the first data into a pre-acquired driving configuration parameter recommendation model and outputting driving configuration parameters;
and the recommending module is used for sending recommending information corresponding to the driving configuration parameters so as to show the recommending information to the driver.
Accordingly, an embodiment of the present invention further provides an electronic device, including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the recommendation method for vehicle driving provided by the above embodiment via executing the executable instructions.
Correspondingly, the embodiment of the invention also provides a vehicle, and the vehicle comprises the vehicle driving recommendation device.
Compared with the prior art, the recommendation method, the recommendation device, the recommendation equipment and the vehicle for vehicle driving provided by the embodiment of the invention have the advantages that firstly, the first data of the driver are obtained in response to the personalized configuration recommendation instruction, wherein the first data comprise the characteristic information and the environmental data of the driver; then, the first data is input into a driving configuration parameter recommendation model which is acquired in advance, and the driving configuration parameters are output, so that recommendation information corresponding to the driving configuration parameters is sent, and the recommendation information is displayed for the driver. According to the embodiment of the invention, the driving configuration parameters which best meet the habits of the driver can be matched and recommended according to the characteristic information and the current environment information of the driver, so that the driving experience of the user can be improved.
Drawings
FIG. 1 is a flow chart of a method for recommending vehicle driving according to an embodiment of the present invention;
FIG. 2 is a block diagram of a convolutional neural network provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a residual block structure in a deep residual network model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an improved residual error network provided by an embodiment of the present invention;
FIG. 5 is a block diagram of a process for obtaining a recommended driving configuration parameter model by using a pre-training model according to the present invention and using transfer learning.
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 schematic flow chart of a recommendation method for vehicle driving according to an embodiment of the present invention, where the recommendation method for vehicle driving includes S11 to S13:
s11, responding to the personalized configuration recommendation instruction, and acquiring first data of the driver, wherein the first data comprises characteristic information and environment data of the driver.
For example, when a personalized configuration recommendation instruction of a user is received, characteristic information of a driver can be acquired through a camera and a sensor.
Illustratively, the characteristic information of the driver includes, but is not limited to, height, weight, sex, age of the driver; the height, age and gender of the driver can be collected through the camera and are processed and identified, and the weight of the driver can be identified through the weight sensor. The camera can be fixed in other suitable positions on door or the automobile body, and the camera can also discern road surface and slope. The environmental data includes, but is not limited to, air temperature, ambient humidity inside the vehicle, visibility outside the vehicle, decibels inside the vehicle. The system comprises a weather forecast system, a humidity sensor, a decibel tester and a controller, wherein the air temperature and the visibility outside the vehicle can be acquired through the weather forecast system, the ambient humidity inside the vehicle can be acquired through the humidity sensor arranged inside the vehicle, and the decibel inside the vehicle can be measured through the decibel tester.
In the embodiment of the present invention, the first data may be obtained in real time, or may be obtained at a preset frequency.
And S12, inputting the first data into a driving configuration parameter recommendation model which is acquired in advance, and outputting driving configuration parameters.
Illustratively, the driving configuration parameters include, but are not limited to, a fore-aft position of a driver seat, a height of the driver seat, a tilt of the driver seat, a position of a rear view mirror, a temperature of an air conditioner, a volume of a vehicle machine, a lateral and longitudinal distance of a steering wheel, and a car light. It can be understood that drivers with different weights set different preferences of the height of the driving seat and the inclination of the driving seat when driving are identified and recommended by the driving configuration parameter recommendation model.
Illustratively, when the first data is obtained in real time, on the basis of obtaining the characteristic information of the driver, the attributes of the driver such as weight, sex and the like do not change, so that only the environmental data needs to be obtained in real time, when the environmental data collected by the sensor exceeds a certain range of change each time, the changed environmental data is obtained and the real-time characteristic extraction is carried out, so as to be input into the driving configuration parameter recommendation model to obtain the best matched driving configuration parameter, for example, when the temperature change of the environment outside the vehicle exceeds 3 ℃, the vehicle can upload the current temperature to the cloud end, and after the calculation of the driving configuration parameter recommendation model, the cloud end pushes the driving configuration parameter to configure the air conditioner temperature; for another example, when the humidity change outside the vehicle exceeds 10%, the vehicle may upload the current humidity data to the cloud, and after calculation of the driving configuration parameter recommendation model, the cloud pushes the driving configuration parameters to configure the air conditioning mode (refrigeration, air supply and the like); if the visibility outside the vehicle is less than 200 meters, the vehicle can upload the current visibility data to the cloud every 50 meters, and after the calculation of the driving configuration parameter recommendation model, the cloud pushes the driving configuration parameters to configure the vehicle lamp switches, such as turning on fog lamps, passing lamps, clearance lamps, front and rear position lamps and the like; when the decibel number in the car changes by more than 10%, the car can upload the current decibel data to the cloud, and after deep learning calculation, the cloud pushes personalized recommended configuration to configure the volume of the car machine.
And S13, sending recommendation information corresponding to the driving configuration parameters to show the recommendation information to the driver.
The vehicle driving recommendation method provided by the embodiment of the invention firstly responds to an individualized configuration recommendation instruction to acquire first data of a driver, wherein the first data comprises characteristic information and environmental data of the driver; then, the first data is input into a driving configuration parameter recommendation model which is acquired in advance, and the driving configuration parameters are output, so that recommendation information corresponding to the driving configuration parameters is sent, and the recommendation information is displayed for the driver. According to the embodiment of the invention, the driving configuration parameters which best meet the habits of the driver can be matched and recommended according to the characteristic information and the current environment information of the driver, so that the driving experience of the user can be improved.
In one embodiment, the driving configuration parameter recommendation model is obtained by:
acquiring a pre-training model, and obtaining a residual convolutional neural network through transfer learning of the pre-training model;
acquiring first data samples of all drivers as training samples;
and inputting the training sample into a residual convolution neural network for training to obtain the driving configuration parameter recommendation model.
Specifically, in the vehicle driving process, feature data of different drivers on the same vehicle are acquired, and driving configuration parameters selected by the drivers in different environments are collected for deep learning so as to generate the driving configuration parameter recommendation model.
Specifically, the obtaining of the convolutional neural network based on the residual error through the pre-training model transfer learning specifically includes:
and transferring the network parameter values extracted from the residual convolutional neural network of the pre-training model to the residual convolutional neural network of the driving configuration parameter recommendation model.
Specifically, the driving configuration parameter recommendation model can be trained locally on a vehicle machine and also can be trained in a cloud. In order to reduce the computational power of the machine, it is preferable to choose to train on the cloud. During specific implementation, the vehicle can request recommended configuration from the cloud for the environmental data such as height, weight, temperature and humidity acquired by the camera and the sensor, the cloud can match with the driving configuration parameter recommendation model through the environmental data such as height, weight, temperature and humidity, the obtained driving configuration parameters are pushed to the vehicle machine, then list recommendation is carried out on the vehicle machine, and whether the driver uses the driving personalized configuration in the recommendation list is inquired.
Specifically, the migration learning is a machine learning method, which is a new task for improving learning by transferring knowledge from learned related tasks, namely, taking a model developed for task a as an initial point and reusing the model developed for task B in the process of developing the model. Here, the model corresponding to the task a is a pre-training model, and various feature data and weight information exist in the pre-training model, including: the characteristic data and the weight information which are closely related to the objects identified by classification and the characteristic data and the weight information which are common to each other are compared, the characteristic data and the information which are common to each other can be shared by different tasks or objects, and the common characteristic data and the weight information need to be migrated in the migration learning, so that the knowledge is prevented from being learned again, and the rapid learning is realized. Therefore, a brand-new network does not need to be designed and trained again, parameter and knowledge migration can be carried out on the basis of the trained network model, and support for a new task can be realized only by a small amount of computing resource overhead and training time.
Illustratively, referring to fig. 2, a classical convolutional neural network consists of convolutional layers, pooling layers, fully-connected layers, and classifiers. The convolution layer and the pooling layer are used for extracting and screening image features, the full-connection layer is used for further extracting output features of the pooling layer, and the classifier is used for normalizing the output of the full-connection layer to enable the output to be in accordance with probability distribution.
In the embodiment of the invention, optimization is performed on a traditional convolutional neural network model, and a deep residual error network and a transfer learning method are added. The deep residual error network uses residual error connection by constructing a residual error block, namely, the input X of the neural network is mapped by an identity I: x → X is directly connected to the output Y of the parameter layer, so that the parameter layer learns a residual map: x → F (X) -X. The residual block structure in the depth residual network model is shown in fig. 3.
It will be appreciated that if the newly added layers are temperature-characterized and are very poor learning for the out-of-vehicle visibility features, we can "skip" this part directly by a residual edge (an edge added directly from the input to the output). This is simply done by setting the weighting parameters of the layers to 0. Thus, regardless of how many layers are in the neural network, the layers that are good are retained and the layers that are not good are skipped. In a word, the effect of the model can be stably improved by deepening the layer number at least without making the effect worse than the original effect by adding a new neural network layer.
This model is further improved as shown in fig. 4. One layer of the residual network can be generally regarded as H (x), and one residual block of the residual network can be represented as H (x) = F (x) + x, that is, F (x) = H (x) -x, in the unit mapping, y = x is an observed value, and H (x) is a predicted value, so that F (x) corresponds to the residual, and when F (x) approaches 0, it means that the observed value and the predicted value are closer, that is, the learning effect of the residual layer is better; conversely, the larger the absolute value of F (x), the worse the learning effect of the residual layer. Therefore, some neural networks with poor learning effects on certain features can be skipped through the arrangement of the residual error module, and specific neural network processing can be added while certain features are skipped so as to perform learning optimization specially for certain features.
Specifically, the definition of the migration learning is as follows: given a source domain: d s ＝{X s ,P(X s ) And learning tasks: t is a unit of s ＝{Y s ,f s (·), given a target domain: d t ＝{X t ,Q(X t ) And learning task T t ＝{Y t ,f t (. The) the purpose of transfer learning is to obtain the source domain D s And learning task T s To help boost the prediction function f in the target domain t (ii) learning of wherein D s ≠D t Or T s ≠T t 。
Illustratively, referring to fig. 4, the pre-trained model trains a neural network model incorporating a deep residual network on a configuration data set. The migration model reserves most of the framework of the pre-training model, but removes the final output full-link layer, freezes the parameters of the convolution layer of the first layer and the migrated residual layer to be used as a shallow layer feature extractor, then randomly initializes the parameters of the 4 th residual layer to learn the deep layer features of the configuration data, and finally adds an output full-link layer to obtain the feature extraction result.
In specific implementation, model training is actively triggered at intervals, latest collected configuration data are actively trained, a relatively stable model can be obtained by iterative traversal of a training set with a certain number of rounds in each training, parameters are frozen, the parameters are durably used for specific application of the model, and driving configuration parameters which are most suitable for a current driver in different environments are predicted.
In one embodiment, the method further comprises:
and responding to a feedback result of the driver to the recommendation information, and updating the driving configuration parameter recommendation model according to the feedback result.
Illustratively, the feedback results include positive feedback, such as operations like time approval, and negative feedback, such as operations like bombs that are not liked by various types of expressions. If the driving configuration parameters are approved, performing negative feedback operation on the driving configuration parameter recommendation model according to the characteristic information, the current environment data and the driving configuration parameters of the current driver, increasing the weight of the driving configuration parameters in the original driving configuration parameter recommendation model under the characteristic information and the current environment data of the current driver, and adjusting the rest driving configuration parameters in proportion. And finally, normalizing the whole updated driving configuration parameter recommendation model. The more driving configuration parameters are fed back in the forward direction, the higher proportion is occupied when the driving configuration parameter recommendation model is updated, so that the subjective hobbies of drivers with different characteristics in different environments can be reflected effectively, the influence degree of the subjective feelings of the drivers on the recommendation result is improved by adding the subjective feelings of the drivers to the characteristic variables, and the accuracy of the driving configuration parameter recommendation model is improved.
In other embodiments, the method further comprises:
responding to the personalized configuration search instruction, and displaying a driving configuration parameter data list of other vehicle owners acquired from the cloud end to a driver;
and responding to a selection instruction of the driver to the driving configuration parameter data list, acquiring the selected driving configuration parameter data, and controlling the control equipment corresponding to the vehicle according to the selected driving configuration parameter data.
In the embodiment of the invention, the driver of the vehicle can also share the driving configuration parameters of other vehicle owners, when the user selects the command of searching the personalized driving configuration parameters by himself, the driving configuration parameters of other vehicle owners are obtained from the cloud, and the driving configuration parameter data list is displayed on the display screen, so that the driver can set the personalized configuration more quickly and better, and the driving experience is provided.
In other embodiments, the method further comprises:
responding to a filtering instruction of a driver to the driving configuration parameter data list, screening out the driving configuration parameter data meeting the filtering condition according to the filtering condition carried by the filtering instruction, and displaying the driving configuration parameter data;
and responding to a sequencing instruction of the driver to the driving configuration parameter data list, and performing corresponding sequencing display on the driving configuration parameter data list according to a sequencing condition carried by the filtering instruction.
In a specific implementation, the driving configuration parameter data list may be sorted according to a praise rate, and a filtering search function is supported, and a dimension of the filtering search may be at least one of the following: height, weight, sex, age, temperature or region.
Correspondingly, the embodiment of the invention also provides a recommendation device for vehicle driving, which comprises:
the system comprises a first data acquisition module, a second data acquisition module and a recommendation module, wherein the first data acquisition module is used for responding to a personalized configuration recommendation instruction and acquiring first data of a driver, and the first data comprises characteristic information and environmental data of the driver;
the output module is used for inputting the first data into a pre-acquired driving configuration parameter recommendation model and outputting driving configuration parameters;
and the recommending module is used for sending recommending information corresponding to the driving configuration parameters so as to show the recommending information to the driver.
It should be noted that the recommendation apparatus for vehicle driving according to the embodiment of the present invention is configured to execute all the steps and processes of the recommendation method for vehicle driving according to the above embodiment, and the working principles and beneficial effects of the two are in one-to-one correspondence, which is not described herein again.
Correspondingly, an embodiment of the present invention further provides an electronic device, including: a processor and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the recommendation method of vehicle driving of the above embodiment via execution of the executable instructions.
Correspondingly, the embodiment of the invention also provides a vehicle, and the vehicle comprises the vehicle driving recommendation device in the embodiment.
According to the recommendation device, the recommendation equipment and the vehicle for vehicle driving provided by the embodiment of the invention, firstly, in response to a personalized configuration recommendation instruction, first data of a driver is obtained, wherein the first data comprises characteristic information and environmental data of the driver; then, the first data is input into a driving configuration parameter recommendation model which is acquired in advance, and the driving configuration parameters are output, so that recommendation information corresponding to the driving configuration parameters is sent, and the recommendation information is displayed for the driver. According to the embodiment of the invention, the driving configuration parameters which best accord with the habit of the driver can be matched and recommended according to the characteristic information and the current environment information of the driver, so that the driving experience of a user can be improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (9)
1. A recommendation method for vehicle driving, characterized by comprising:
responding to a personalized configuration recommendation instruction, and acquiring first data of a driver, wherein the first data comprises characteristic information and environment data of the driver;
inputting the first data into a pre-acquired driving configuration parameter recommendation model, and outputting driving configuration parameters;
and sending recommendation information corresponding to the driving configuration parameters to show the recommendation information to the driver.
2. The recommendation method for vehicle driving according to claim 1, wherein the driving configuration parameter recommendation model is obtained by:
acquiring a pre-training model, and obtaining a residual convolutional neural network through transfer learning of the pre-training model;
acquiring first data samples of all drivers as training samples;
and inputting the training sample into a residual convolution neural network for training to obtain the driving configuration parameter recommendation model.
3. The method for recommending vehicle driving according to claim 2, wherein said obtaining a convolutional neural network based on residual errors through the pre-training model transfer learning specifically comprises:
and transferring the network parameter values extracted from the residual convolutional neural network of the pre-training model to the residual convolutional neural network of the driving configuration parameter recommendation model.
4. The method for recommending vehicle driving according to claim 1, characterized in that said method further comprises:
and responding to a feedback result of the driver to the recommendation information, and updating the driving configuration parameter recommendation model according to the feedback result.
5. The method for recommending vehicle driving according to claim 1, characterized in that said method further comprises:
responding to the personalized configuration searching instruction, and displaying a driving configuration parameter data list of other vehicle owners acquired from a cloud end to a driver;
and responding to a selection instruction of the driver to the driving configuration parameter data list, acquiring the selected driving configuration parameter data, and controlling the control equipment corresponding to the vehicle according to the selected driving configuration parameter data.
6. The method for recommending vehicle driving according to claim 5, characterized in that said method further comprises:
responding to a filtering instruction of a driver to the driving configuration parameter data list, screening out the driving configuration parameter data meeting the filtering condition according to the filtering condition carried by the filtering instruction, and displaying the driving configuration parameter data;
and responding to a sequencing instruction of the driver to the driving configuration parameter data list, and performing corresponding sequencing display on the driving configuration parameter data list according to a sequencing condition carried by the filtering instruction.
7. A recommendation device for vehicle driving, characterized by comprising:
the system comprises a first data acquisition module, a second data acquisition module and a recommendation module, wherein the first data acquisition module is used for responding to a personalized configuration recommendation instruction and acquiring first data of a driver, and the first data comprises characteristic information and environmental data of the driver;
the output module is used for inputting the first data into a pre-acquired driving configuration parameter recommendation model and outputting driving configuration parameters;
and the recommending module is used for sending recommending information corresponding to the driving configuration parameters so as to show the recommending information to the driver.
8. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of recommending vehicle driving of any of claims 1-7 via execution of the executable instructions.
9. A vehicle characterized by comprising the vehicle driving recommendation device according to claim 7.
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