FR3142581A1 - Method and device for predicting a level of risk of fog appearing inside a vehicle - Google Patents

Abstract

The present invention relates to a method and a device for predicting a risk of fogging appearing in a passenger compartment of a first vehicle (10). The risk level is determined by classification of first data representative of exterior temperature, second data representative of temperature in the first vehicle (10), third data of a state of cleanliness of a set of cabin filters and/or d 'an air conditioning system of the first vehicle (10) and fourth data representative of the duration spent by the first vehicle (10) in an external environment, on the basis of a model learned during a learning phase from data obtained from a set of second vehicles (11). Figure for abstract: Figure 1

Description

Method and device for predicting a level of risk of fog appearing inside a vehicle

The present invention relates to methods and devices for predicting a level of risk of the appearance of fogging inside a vehicle. The present invention also relates to a method and a device for controlling one or more on-board systems of a vehicle as a function of a level of risk of the appearance of fogging in the vehicle. The present invention also relates to a method and a device for communicating data representative of a level of risk of the appearance of fogging inside a vehicle.

Technology background

The appearance of fogging in the passenger compartment of a vehicle, particularly on the windshield, represents a significant inconvenience for the driver, the presence of fogging reducing the driver's visibility. This therefore creates a potential risk of accident and a risk for vehicle passengers and other road users.

Contemporary vehicles are equipped with systems designed to limit the presence of fogging in the vehicle and to eliminate this fogging when it is present.

Thus, contemporary vehicles most of them have defogging and/or air conditioning systems which control the air in the passenger compartment, for example the air temperature, the ventilation level, the recycling of air. 'air. Such a system corresponds for example to an air recycling system and/or a heating and air conditioning system (called HVAC system, from English “Heating, Ventilation and Air-Conditionning” or in French “Chauffage, ventilation and air conditioning").

These systems require, like any part of a vehicle, regular maintenance operations to ensure proper operation and recycling or correct air conditioning of the air inside the vehicle, which is essential to prevent the appearance of fogging or removing fogging when it is present in the vehicle passenger compartment.

Summary of the present invention

An object of the present invention is to solve at least one of the problems of the technological background described above.

Another object of the present invention is to prevent the appearance of fogging in a vehicle.

Another object of the present invention is to prevent the appearance of a fault in one or more air recycling or air conditioning systems of a vehicle.

According to a first aspect, the present invention relates to a method for predicting a level of risk of the appearance of fogging inside a vehicle, the method being implemented by at least one processor and comprising the following steps:
- reception of first data representative of weather conditions outside the vehicle;
- reception of second data representative of a temperature inside the vehicle, the second data being obtained from a temperature sensor on board the vehicle;
- reception of third data representative of a state of cleanliness of a set of cabin filters and/or of an air conditioning system of the vehicle, the third data being obtained from at least one sensor associated with the set of cabin filters and/or the air conditioning system;
- reception of fourth data representative of duration of presence of the vehicle in an external environment;
- determination of information representative of the level of risk of misting appearing inside the vehicle by classification of the first, second, third and fourth data from a classification model learned in a so-called learning phase prior to receiving the first, second, third and fourth data, the risk level being associated with a class of a set of classes output from the classification model;
- transmission of at least one request to a mobile communication device and/or at least one on-board system of the vehicle based on the information.

The use of data representative of weather conditions around the vehicle, the temperature in the vehicle, the state of cleanliness of the air management systems in the passenger compartment and the time spent outside for the vehicle to cross-reference them with a classification model learned beforehand makes it possible to determine or predict a level of risk of the appearance of fogging in the vehicle, in real time. Depending on the result, one or more actions can be implemented to prevent the appearance of fogging, for example by transmitting a request to the air management system(s) to adapt the parameters of this or these systems and /or by transmitting a request to a mobile communication device to warn the driver of the problem by proposing a solution.

This makes it possible to improve the safety of the vehicle and its passengers and/or to plan maintenance operations for the air management systems if necessary.

According to a variant, the method further comprises a step of receiving fifth data representative of an activation state of a set of functions of the air conditioning system, the information being further determined by classification of the fifth data.

According to another variant, the first data correspond to data representative of exterior temperature.

According to an additional variant, the fourth data is obtained from a navigation system associated with the vehicle.

According to yet another variant, the at least one request belongs to a set of requests comprising:
- a first request for control of at least one parameter of the vehicle's air conditioning system, the first request being transmitted to a device for controlling the air conditioning system;
- a second request to render a message representative of an alert to carry out a maintenance operation on the set of cabin filters and/or the air conditioning system;
- a third request to render a message advising to protect the vehicle with a protective cover when parking the vehicle in the external environment.

According to an additional variant, the second request and/or the third request is transmitted via a wireless connection to a mobile communication device.

According to another variant, the classification is implemented by a neural network.

According to a second aspect, the present invention relates to a device for predicting a level of risk of the appearance of fogging inside a vehicle, the device comprising a memory associated with a processor configured for implementing the steps of the process according to the first aspect of the present invention.

According to a third aspect, the present invention relates to a vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

According to a fourth aspect, the present invention relates to a computer program which comprises instructions adapted for the execution of the steps of the method according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

Such a computer program can use any programming language, and be in the form of a source code, an object code, or an intermediate code between a source code and an object code, such as in partially compiled form, or in any other desirable form.

According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for executing the steps of the method according to the first aspect of the present invention.

On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a ROM memory of the microelectronic circuit type, or even a magnetic recording means or a hard disk.

On the other hand, this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or terrestrial radio or by self-directed laser beam or by other ways. The computer program according to the present invention can in particular be downloaded over an Internet type network.

Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in executing the method in question.

Brief description of the figures

Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting examples of embodiment of the present invention below, with reference to the attached Figures 1 to 3, in which:

schematically illustrates a communication environment of a vehicle, according to a particular embodiment of the present invention;

schematically illustrates a device 2 configured to predict a level of risk of fog appearing inside a vehicle of the , according to a particular and non-limiting embodiment of the present invention; And

illustrates a flowchart of the different stages of a method for predicting a level of risk of the appearance of fogging inside a vehicle of the , according to a particular and non-limiting embodiment of the present invention.

Description of the implementation examples

A method and a device for predicting a level of risk of the appearance of fogging inside a vehicle will now be described in what follows with reference jointly to Figures 1 to 3. The same elements are identified with of the same reference signs throughout the description which follows.

According to a particular and non-limiting example of embodiment of the invention, the prediction of a level of risk of the appearance of fogging or condensation inside a vehicle as a function of parameters such as weather conditions, temperature in the vehicle, the state of cleanliness of the vehicle's cabin filters and/or the state of cleanliness of the air conditioning system and the time spent by the vehicle in an external environment makes it possible to implement the necessary action(s) to reduce the risk, the implementation of this or these actions resulting in the transmission of one or more requests to for example control one or more parameters of the air control systems (air conditioning system, recycling system air for example) and/or to warn the owner/driver of the vehicle of possible problems with clogging of the filters and/or the air conditioning system.

For this purpose, first data representative of weather conditions (for example temperature) outside the vehicle are received or collected from a set of sensors on board the vehicle and/or from a set of sensors arranged in the vehicle. external environment, for example in weather stations. Second data representative of the temperature inside the vehicle are also received or collected, these second data being advantageously obtained from one or more sensors or temperature probes on board the vehicle. Third data representative of a state of cleanliness of a set of cabin filters and/or of an air conditioning system of the vehicle are also received or collected, these third data being obtained from at least one sensor associated with the set cabin filters and/or the air conditioning system. Fourth data representative of the duration of presence of the vehicle in an external environment are also received or collected, these fourth data being obtained from a navigation system embedded in the vehicle or implemented on a mobile communication device (for example a smartphone ( from the English “Smartphone”) belonging to the driver or owner of the vehicle.

Information representative of a level of risk of the appearance of fogging (or condensation) in the vehicle is determined or predicted by classification of the first, second, third and fourth data from a classification model learned in a so-called phase. learning.

The classification is for example implemented in a machine learning method, for example implemented with a neural network. Such a method makes it possible to estimate more reliably and in real time the level of risk of fog appearing over a determined time horizon, for example for the next 30, 60, 120 minutes, on the basis of a model. learned from the same data from a set of other vehicles.

Such an advantage is obtained by determining, during a learning phase, the correlation between the data collected from a set of vehicles and representing the actual use made of these vehicles with the appearance of fogging observed. in this set of other vehicles.

Predicting the level of risk makes it possible to anticipate a problem to potentially correct it before it appears, for example by controlling the air conditioning system or cleaning the cabin filters and/or the air conditioning system, improving the prevention of faults in the air conditioning systems. air management or the risk of fogging.

There schematically illustrates a communication environment 1 of a first vehicle 10, according to a particular and non-limiting embodiment of the present invention.

The first vehicle 10 corresponds for example to a vehicle with a thermal engine, with an electric motor(s) or even a hybrid vehicle with a thermal engine and one or more electric motors. The vehicle 10 thus corresponds for example to a land vehicle, for example an automobile, a truck, a coach, a motorcycle.

The first vehicle 10 also advantageously carries a communication system configured to communicate with one or more remote devices 101 via an infrastructure of a wireless communication network. The remote device 101 advantageously corresponds to a device configured to process data, for example data stored in the memory of the remote device 101 and/or data received from the vehicle 10. The remote device 101 corresponds for example to a “cloud” server 100.

The communication system of the first vehicle 10 comprises for example one or more communication antennas connected to a telematic control unit, called TCU (from the English “Telematic Control Unit”), itself connected to one or more computers of the system on-board the first vehicle 10. The antenna(s), the TCU unit and the computer(s) form for example a multiplexed architecture for the production of different services useful for the proper functioning of the first vehicle 10 and to assist the driver and/or the passengers of the first vehicle 10 in the control of the first vehicle 10 and/or to establish a diagnosis on the operation of one or more components of the first vehicle 10. The computer(s) and the TCU unit communicate and exchange data between them by via one or more computer buses, for example a CAN data bus type communication bus (from the English “Controller Area Network” or in French “Réseau de controllers”), CAN FD (from the English “Controller Area Network Flexible Data-Rate”, FlexRay (according to ISO 17458) or Ethernet (according to ISO/IEC 802-3).

The mobile communication infrastructure allowing wireless data communication between the first vehicle 10 and the remote device 101 comprises for example or several communication equipment 110 of the relay antenna type (cellular network) or roadside unit, called UBR. In a mode of communication using such a network architecture, the data is for example transmitted by the first vehicle 10 to the remote device 101 of the "cloud" 100 via a relay antenna 110 (the antenna 110 being for example connected to the "cloud" 100 via a wired connection).

The wireless communication system allowing the exchange of data between the first vehicle 10 and the remote device 101 corresponds for example to:
- a vehicle-to-infrastructure V2I communication system, for example based on the 3GPP LTE-V or IEEE 802.11p standards of ITS G5; Or
- a cellular network type communication system, for example an LTE type network (from the English “Long-Term Evolution” or in French “Evolution à long term”), LTE-Advanced (or in French LTE-advanced) also called LTE 3G, 4G or 5G; Or
- a WiFi type communication system according to IEEE 802.11, for example according to IEEE 802.11n or IEEE 802.11ac.

• un système de climatisation de l’air, aussi appelé système HVAC ; et/ou
  - un système de pré-chauffage de l’air dans l’habitacle, par exemple intégré au système HVAC ; et/ou
  - un système de désembuage du pare-brise, par exemple intégré au système HVAC.

The first vehicle 10 advantageously incorporates one or more air control systems in the passenger compartment of the vehicle, for example:
- an air recycling system comprising a set of cabin filters, which set includes one or more filters; and or

• an air conditioning system, also called an HVAC system; and or
  - an air pre-heating system in the passenger compartment, for example integrated into the HVAC system; and or
  - a windshield demisting system, for example integrated into the HVAC system.

A process for predicting a level of risk of the appearance of fogging inside the first vehicle 10 is for example implemented by the first vehicle 10, that is to say by a computer or a combination of computers of the on-board system of the first vehicle 10.

According to another example, the prediction and/or classification process is implemented by the remote device 101, for example by one or more processors of this device 101.

According to yet another example, the prediction and/or classification process is implemented by a system comprising the first vehicle 10 (that is to say by one or more devices on board the vehicle 10, for example one or more several computers) and the remote device 101 connected in communication with the first vehicle 10 via a connection or a wireless link.

The process of predicting/determining the level of risk of misting appearing in the passenger compartment of the first vehicle 10 advantageously comprises two phases, each of these phases comprising one or more operations.

The first phase corresponds to a so-called learning or training phase of one or more prediction models (and/or a model or several classification models) and the second phase corresponds to a so-called production or production phase. prediction based on the model(s) learned in the learning phase and data feeding the model(s) learned.

The first phase and the second phase are for example implemented by the remote device 101.

According to a variant embodiment, the first phase is implemented by the remote device 101 (or by a non-cloud server, for example a server hosted in a center (for example a design office) adapted to carry out the learning) and the second phase by the first vehicle 10, for example by a computer of the on-board system of the first vehicle 10.

According to another alternative embodiment, learning is implemented in the first phase and the parameters of the prediction model(s) are refined in real time from the data relating to the first vehicle 10 received or collected during the second phase.

Learning phase

The learning implemented in the first phase advantageously corresponds to supervised learning from a set of data associated with a set or group of second vehicles 11 comprising for example a few dozen, a few hundred, thousands or tens/hundreds thousands of second vehicles. According to a variant embodiment, the learning implemented in the first phase corresponds to unsupervised learning from the set of data associated with the set of second vehicles.

According to a particular example, the second vehicles included in the set of second vehicles 11 all have the same configuration or a similar configuration, that is to say they all correspond to the same type of vehicle (for example the same series of a particular vehicle model) with identical or similar components or organs (same version for example), for example the same shock absorbers or shock absorbers of the same type.

In a first operation of the learning phase, the remote device 101 collects, for each second vehicle 11, a set of first data representative of the weather conditions (for example the temperature) around the second vehicle, a set of second data representative of temperature in the passenger compartment, a set of third data representative of the state of cleanliness of the cabin filter(s) and/or the state of cleanliness of the air conditioning system and a set of fourth data representative of the duration of presence of each second vehicle in an external environment (for example the duration of parking outside a building), these data being for example transmitted by each of the second vehicles of the set 11 as the second vehicles move. The data are for example transmitted by each second vehicle via the wireless network infrastructure described above to the remote device 101. The data correspond for example to part or all of the vehicle signals emitted by sensors on board in each second vehicle and/or by sensors of mobile communication devices (for example a tablet, a smart phone (from the English “Smartphone”) embedded in the second vehicles 11 and for example connected in communication with the second vehicle (for example example via Bluetooth® or Wifi®) which embeds them. These data are for example transmitted continuously at a determined frequency (for example every second, hour, day, etc.) or at each variation of a quantity measured by. a sensor or a computer on board each second vehicle The data reported to the remote device 101 correspond for example to the data of a list determined and stored in the memory of a computer of the on-board system of each second vehicle which makes a selection of the data to be. transmit among all the data or physical quantities that each second vehicle detects or measures.

According to an alternative embodiment, the second vehicles 11 also transmit fifth data representative of an operating or activation state of one or more air control systems in the passenger compartment, these fifth data comprising for example :
- representative air conditioning activation data;
- data representative of activation of air recycling;
- data representative of control parameters of one or more of these systems, for example set temperature, ventilation level, etc.

According to a variant embodiment, the data representative of climatic conditions outside the second vehicles 11 are measured by and received from a set of sensors 102 arranged in the environment, for example near the roads taken by the second vehicles 11 .

Data representative of climatic conditions include, for example:
- temperature data; and or
- rainfall and/or humidity level data; and or
- atmospheric pressure data.

These data are for example timestamped to be correlated temporally with the data received from the second vehicles 11.

In a second operation, the remote device 101 collects or receives a set of sixth data representative of the presence of mist in the passenger compartment of each second vehicle 11, these sixth data being time stamped.

The sixth data are for example transmitted by the drivers of the second vehicles 11, for example through a mobile application installed in each second vehicle 11 or in the drivers' smart phones.

According to a variant embodiment, the sixth data correspond to data representative of the activation of the defogging system, such activation being representative of the presence of fogging in the second vehicle.

In a third operation of the learning phase, the remote device 101 processes the data obtained during the first and second operations, that is to say the first, second, third, fourth data (and the fifth data if applicable) and sixth data.

The processing includes a cross-referencing of this data for the generation of a set of classes, each class of the set being associated with a level of risk of the appearance of fogging, the classes or groups being said to be labeled, i.e. -say that a label is associated with each class or group. For example, if 5 classes or groups are generated when classifying the data, 5 risk levels will also be generated (e.g. from 0 to 4), with for example level 0 corresponding to the lowest or minimal risk level and 4 at the highest or maximum risk level. According to another example, if 3 classes are generated, 3 performance levels will be generated with for example: a minimum level, a standard level and a maximum level.

The risk level prediction model(s) (or in other words the classification model(s)) is/are generated or trained by classification of the data obtained.

A prediction/classification model is for example obtained for each type of vehicle of the set of second vehicles 11. According to a variant, a single prediction/classification model is obtained for the set of second vehicles 11.

The classification implemented corresponds for example to a classification via a k-means partition method (from the English “k-means”). This k-means partition method is for example associated with a method called “Elbow” (or “elbow” in French) in order to group the different profiles obtained into groups or classes of different risk levels. The principles of such a method associated with the “Elbow” method are known to those skilled in the art and for example described in the document entitled “Determining the appropriate cluster number using elbow method for K-means algorithm”, published by H. Humaira and R. Rasyidah in January 2018.

Such a classification/prediction process is advantageously implemented by a neural network implementing an automatic learning method, also called a machine learning method, for example a method of deep learning.

According to a particular and non-limiting example, the neural network corresponds to a recurrent neural network with short and long term memory, called LSTM network (from the English “Long Short Term Memory”), an example of such a network LSTM being described in the document “Long short-term memory” published by Sepp Hochreiter and Jürgen Schmidhuber, in Neural Computation. 9 (8): 1735 1780.doi:10.1162/neco.1997.9.8. 1735. PMID 9377276. An LSTM type neural network is made up of interconnected cells (neurons) interacting non-linearly, an example of which is described next to the . The output of a cell is a nonlinear combination of its inputs. An LSTM neural network is made up of interconnected cells (neurons) interacting non-linearly. The output of a cell is a nonlinear combination of its inputs.

When the prediction or classification phase is implemented by the first vehicle 10, the parameters defining each prediction or classification model obtained in the third operation are transmitted by the remote device 101 to the first vehicle 10 which stores them in associated memory to a computer of the on-board system for the implementation by this computer of the prediction / classification phase.

Such a learning phase thus makes it possible to cross-reference the data from (or associated with) the second vehicles with data representing the detection of the presence of mist in the second vehicles.

Prediction phase

In a first operation of the prediction phase, the device in charge of the prediction phase (that is to say the remote device 101 or one or more computers of the on-board system of the first vehicle 10) collects the following data:
- first data representative of weather conditions outside the first vehicle 10, these first data being for example measured by a sensor or a temperature probe on board the first vehicle or these first data being measured by the sensor(s) 102;
- second data representative of a temperature inside the vehicle, these second data being measured by a sensor or a temperature probe on board the first vehicle 10;
- third data representative of a state of cleanliness of a set of cabin filters and/or of an air conditioning system of the vehicle, these third data being obtained from one or more sensors (for example of the particle counter type) associated with cabin filters and/or the air conditioning system;
- fourth data representative of the duration of presence of the vehicle in an external environment, that is to say outside a covered space, this duration corresponding for example to the duration spent by the vehicle parked outside (for example example at night, during the day when the driver is working, shopping, etc.); And
- optionally, fifth data representative of a state of activation of a set of functions of the air conditioning system, for example activation of the air conditioning system, activation of the passenger compartment preheating function, activation of air recycling, operating parameters of the air conditioning system such as ventilation level, set temperature, activation of automatic regulation, etc.

The first, second, third, fourth and/or fifth data are for example transmitted by the first vehicle 10 to the remote device 101 via the wireless network infrastructure over time, for example at regular intervals or when a new measurement is carried out by one of the sensors acquiring the first data.

When the prediction phase is implemented by the first vehicle 10, the first data are stored in a memory associated with the computer of the on-board system of the first vehicle 10, or in a “cloud” server to which the first vehicle 10 is connected.

According to a variant embodiment, information representative of the time instant at which each piece of data was measured or acquired or transmitted is associated with said first piece of data. In other words, each data is time-stamped.

In a second operation of the prediction phase, the first, second, third, fourth, and optionally the fifth, data are used to feed the prediction/classification model(s) learned or trained during the learning phase. This or these models provides information representative of a prediction of the level of risk of the appearance of fogging or condensation in the passenger compartment of the first vehicle 10.

The determination of the level of risk of the appearance of fogging in the first vehicle 10 is for example implemented using the so-called k-means method, which is for example implemented by a neural network, for example of the LSTM type. . The LSTM type neural network makes it possible to predict the level of risk of fog appearing in the first vehicle in real time from the first, second, third, fourth and optionally fifth data collected.

The first, second, third, fourth and optionally fifth data are thus processed to determine the probability of belonging to each class of the plurality of classes generated during the learning phase. The level of risk determined for the first vehicle 10 corresponds to the level of risk associated with the class to which the probability of belonging is the highest.

In a third operation, one or more requests are generated according to the level of risk obtained in the second operation and transmitted to the mobile communication device 103 and/or to one or more systems embedded in the first vehicle 10.

This or these queries allow for example:
- to activate or control one or more air control systems in the passenger compartment of the first vehicle 10 (for example the air conditioning system) according to one or more determined control parameters, such as temperature, ventilation, air recirculation, etc., to prevent the appearance of fogging or reduce the risk of fogging; and or
- to inform the driver or owner of the vehicle of the risk of fogging; and or
- to provide recommendations to the driver or owner of the vehicle to prevent the risk or to explain the cause(s) leading to the appearance of fogging.

For example, the query(s) generated and transmitted correspond to:
- a first request for control of at least one parameter of the vehicle's air conditioning system (or any other cabin air control system), the first request being transmitted to a control device ( for example a computer) of the air conditioning system (or a control device of any other cabin air control system); and or
- a second request to render a message representative of an alert to carry out a maintenance operation on the set of cabin filters and/or the air conditioning system. The rendering corresponds for example to the display of a determined message on a screen embedded in the first vehicle 10 and/or on a screen of the mobile communication device 103. For example, the level of cleanliness of the filters and/or of the system air conditioning may be at an absolutely acceptable level but insufficient to prevent the appearance of fogging; and or
- a third request to render a message advising to protect the vehicle with a protective cover when parking the vehicle in the external environment. The rendering corresponds for example to the display of a determined message on a screen embedded in the first vehicle 10 and/or on a screen of the mobile communication device 103.

The query(ies) to be generated are, for example, determined based on the class obtained at the end of the classification process. According to this example, one or more queries to be generated are associated with each of the classes identified during the learning phase.

When the prediction phase is implemented by the remote device 101, the request(s) are generated by the remote device 101 and transmitted to the first vehicle 10 and/or to the mobile communication device 103 via the wireless network infrastructure.

When the prediction phase is implemented by the first vehicle 10, the request(s) are generated by a computer and transmitted to other computers of the first vehicle 10 via one or more data buses and/or to the monitoring device. wireless communication 103 via a wireless connection (via the wireless network infrastructure or in a direct wireless communication mode between the first vehicle 10 and the mobile communication device 103).

In a fourth operation, the cabin air control system(s) of the first vehicle 10, for example the air conditioning system, is/are activated or controlled automatically according to the request(s) received.

In addition, one or more messages are rendered, for example displayed, on a screen of the first vehicle 10 and/or the screen of the mobile communication device when one or more content rendering requests are generated and transmitted.

There schematically illustrates a device 2 configured to predict or determine a level of risk of the appearance of fogging inside a vehicle, for example the first vehicle 10, according to a particular and non-limiting embodiment of the present invention.

Such a device 2 is configured for the implementation of the learning phase and the production phase of a machine learning method.

According to yet another example, the device 2 is both configured to control one or more systems of a vehicle, for example the air conditioning system, and to determine or predict a level of risk of the appearance of fogging.

The device 2 is for example configured for the implementation of the operations described with regard to the and/or steps of the process described with regard to the . Examples of such a device 2 include, without being limited to, a computer, a laptop, a server, a mobile phone, a tablet or even on-board electronic equipment such as an on-board computer of a vehicle or a electronic calculator such as an ECU (“Electronic Control Unit. The elements of device 2, individually or in combination, can be integrated in a single integrated circuit, in several integrated circuits, and/or in discrete components. Device 2 can be produced in the form of electronic circuits or software (or computer) modules or even a combination of electronic circuits and software modules.

The device 2 comprises one (or more) processor(s) 20 configured to execute instructions for carrying out the steps of the method and/or for executing the instructions of the software(s) embedded in the device 2. The processor 20 may include integrated memory, an input/output interface, and various circuits known to those skilled in the art. The device 2 further comprises at least one memory 21 corresponding for example to a volatile and/or non-volatile memory and/or comprises a memory storage device which may comprise volatile and/or non-volatile memory, such as EEPROM, ROM , PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored on memory 21.

According to a particular and non-limiting exemplary embodiment, the device 2 comprises a block 22 of interface elements for communicating with external devices, for example a remote server or the "cloud", other nodes of the ad hoc network. The interface elements of block 22 include one or more of the following interfaces:
- RF radio frequency interface, for example of the Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or of the Bluetooth® type (according to IEEE 802.15.1), in the band frequency at 2.4 GHz, or Sigfox type using UBN radio technology (from the English Ultra Narrow Band, in French ultra narrow band), or LoRa in the 868 MHz frequency band, LTE (from the English " Long-Term Evolution” or in French “Long-Term Evolution”), LTE-Advanced (or in French LTE-advanced);
- USB interface (from the English “Universal Serial Bus” or “Bus Universel en Série” in French);
- HDMI interface (from the English “High Definition Multimedia Interface”, or “Interface Multimedia Haute Definition” in French);
- LIN interface (from the English “Local Interconnect Network”, or in French “Local Interconnected Network”).

According to another particular and non-limiting example of embodiment, the device 2 comprises a communication interface 23 which makes it possible to establish communication with other devices (such as other computers of the on-board system) via a communication channel 230. The communication interface 23 corresponds for example to a transmitter configured to transmit and receive information and/or data via the communication channel 230. The communication interface 23 corresponds for example to a CAN type wired network (from the English “Controller Area Network” or in French “Réseau de controlleres”), CAN FD (from the English “Controller Area Network Flexible Data-Rate” or in French “Réseau de controllers à flow flexible data”), FlexRay ( standardized by the ISO 17458 standard) or Ethernet (standardized by the ISO/IEC 802-3 standard).

According to a particular and non-limiting embodiment, the device 2 can provide output signals to one or more external devices, such as a display screen, touch or not, one or more speakers and/or other devices (projection system) via respective output interfaces.

There illustrates a flowchart of the different stages of a method for predicting a level of risk of the appearance of fogging inside a vehicle, for example of the first vehicle 10, according to a particular and non-limiting example of embodiment of the present invention. The method is for example implemented by a device on board the vehicle, for example by device 2 of the , or by a system comprising the on-board device connected in wireless communication to a remote server or computer type device.

In a first step 31, first data representative of weather conditions outside the vehicle are received.

In a second step 32, second data representative of a temperature inside the vehicle are received, the second data being obtained from a temperature sensor on board the vehicle.

In a third step 33, third data representative of a state of cleanliness of a set of cabin filters and/or of an air conditioning system of the vehicle are received, the third data being obtained from at least one sensor associated with the set of cabin filters and/or the air conditioning system.

In a fourth step 34, fourth data representative of the duration of presence of the vehicle in an external environment are received.

In a fifth step 35, information representative of the level of risk of fog appearing inside the vehicle is determined by classification of the first, second, third and fourth data from a classification model learned in a so-called phase. learning prior to receiving the first, second, third and fourth data, the risk level being associated with a class of a set of classes output from the classification model.

In a sixth step 36, at least one request is transmitted to a mobile communication device and/or at least one on-board system of the vehicle depending on the information.

According to a variant, the variants and examples of the operations described in relation to the apply to the stages of the process of .

Of course, the present invention is not limited to the exemplary embodiments described above but extends to a method of controlling one or more air control systems in the passenger compartment of a vehicle, which would include secondary steps, without thereby departing from the scope of the present invention. The same would apply to a device configured to implement such a process.

The present invention also relates to a vehicle, for example an automobile, comprising the device of the .

The present invention also relates to a system comprising a vehicle and one or more remote server-type devices, the vehicle being connected in wireless communication to the remote device(s).

Claims (10)

Method for predicting a level of risk of the appearance of fogging inside a vehicle (10), said method being implemented by at least one processor and comprising the following steps:
- reception (31) of first data representative of weather conditions outside said vehicle (10);
- reception (32) of second data representative of a temperature inside said vehicle, said second data being obtained from a temperature sensor on board said vehicle (10);
- reception (33) of third data representative of a state of cleanliness of a set of cabin filters and/or of an air conditioning system of said vehicle (10), said third data being obtained from at least one sensor associated with said set of cabin filters and/or said air conditioning system;
- reception (34) of fourth data representative of duration of presence of said vehicle (10) in an external environment;
- determination (35) of information representative of the level of risk of fog appearing inside said vehicle (10) by classification of said first, second, third and fourth data from a classification model learned in a phase called learning prior to said reception of the first, second, third and fourth data, said risk level being associated with a class of a set of classes output from said classification model;
- transmission (36) of at least one request to a mobile communication device (103) and/or at least one on-board system of said vehicle based on said information. Method according to claim 1, further comprising a step of receiving fifth data representative of an activation state of a set of functions of said air conditioning system, said information being further determined by classification of said fifth data. Method according to claim 1 or 2, for which said first data correspond to data representative of exterior temperature. Method according to one of claims 1 to 3, for which said fourth data is obtained from a navigation system associated with said vehicle (10). Method according to one of claims 1 to 4, for which said at least one request belongs to a set of requests comprising:
- a first request for controlling at least one parameter of said air conditioning system of said vehicle (10), said first request being transmitted to a device for controlling said air conditioning system;
- a second request to render a message representative of an alert to carry out a maintenance operation on said set of cabin filters and/or said air conditioning system;
- a third request to render a message advising to protect said vehicle (10) with a protective cover when parking said vehicle (10) in said external environment. Method according to claim 5, for which said second request and/or said third request is transmitted via a wireless connection to said mobile communication device (103). Method according to one of claims 1 to 6, for which said classification is implemented by a neural network. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor. Device (2) for predicting a level of risk of the appearance of fogging inside a vehicle, said device (2) comprising a memory (21) associated with at least one processor (20) configured for updating implementing the steps of the method according to any one of claims 1 to 7. Vehicle (10) comprising the device (2) according to claim 9.