Identification of an off-road vehicle driving situation

Abstract

The invention relates to a method for identifying an off-road driving situation of a vehicle comprising collecting (200) data descriptive of a driving situation of the vehicle and detecting (202), based on a first set of data from among the collected data, an off-road driving situation of the vehicle. Following the detection of the off-road driving situation, the method comprises identifying (204; 205; 206) a type of off-road driving situation based on at least a second set of data from among the collected data, and transmitting (207; 208) the identified type of off-road driving situation to an entity remote from the vehicle and / or to a module of the vehicle capable of implementing at least one vehicle protection function based on the identified type of off-road driving situation. FIG. 2

Description

Title of the invention: Identification of an off-road driving situation of a vehicle

[0001] The present invention belongs to the field of identifying a driving situation of a vehicle, in particular an off-road driving situation, in order to improve the dimensioning of vehicle components or in order to activate a driving assistance function intended for such an off-road driving situation.

[0002] The term “vehicle” means any type of vehicle such as a private, utility or heavy goods vehicle.

[0003] A mission profile is a representation of the static and dynamic load conditions to which a component or components of the vehicle are subjected during its life cycle. The life cycle includes production, testing, transportation, and operational uses of the component when mounted in the vehicle.

[0004] Mission profiles are data that are thus essential for the dimensioning of vehicle components. They allow, via statistical analyses, to predict component dimensions adapted to the different driving situations in which the vehicles that integrate the components may find themselves.

[0005] The load conditions to which a component of a vehicle is subjected at a given moment depend in fact strongly on the driving situation of the vehicle at the given moment, and depend in particular on the type of road on which the vehicle is traveling.

[0006] It is therefore important to have data relating to the components of a vehicle, in all driving situations in which the vehicle may find itself during its life cycle.

[0007] However, to date, only mission profiles on so-called classic roads, such as motorways, city roads, etc., are taken into account.

[0008] Off-road driving situations, also called "off-road" in English, are not taken into account in the mission profiles. However, although less frequent, these driving situations impose significant dynamic and static load conditions on the vehicles and their components, which can cause damage if they have not been dimensioned to take these situations into account, and / or if a function making it possible to limit this damage has not been provided for this type of driving situation.

[0009] There is therefore a need to take into account off-road driving situations in order to improve the lifespan of vehicle components.

[0010] The present invention improves the situation.

[0011] To this end, a first aspect of the invention relates to a method for identifying an off-road driving situation of a vehicle, implemented in a device of the vehicle and comprising the following steps: - collection of descriptive data of a vehicle driving situation; - detection, based on a first set of data from among the collected data, of an off-road driving situation of the vehicle; - following detection of the off-road driving situation, identification of a type of off-road driving situation based on at least a second set of data from among the collected data; - transmission of the identified off-road driving situation type to a remote entity of the vehicle and / or to a vehicle module capable of implementing at least one vehicle protection function based on the identified off-road driving situation type.

[0012] Thus, the invention allows not only the detection but also the identification of a type of off-road driving situation. Such information allows a remote entity, such as a remote server, to collect such information for a set of vehicles, which allows the development of mission profiles for off-road driving situations, and therefore better dimensioning of vehicle components for such driving situations. Alternatively or in addition, a function implemented in the vehicle can be dedicated to the protection of components in such driving situations: the detection and identification of the off-road driving situation can allow the activation of such a function, and the taking into account of the type of driving situation in the parameterization of the function. This results in a better service life of the vehicle components.

[0013] According to embodiments, the detection of the off-road driving situation of the vehicle may comprise the application of a first classification algorithm or the application of a first set of classification rules, the first classification algorithm or the first set of classification rules being capable of receiving the first set of data as inputs, and of classifying the first set of data into one of at least two categories comprising an off-road driving situation and at least one other driving situation.

[0014] The first classification algorithm can be obtained by machine learning on a first training data set. Thus, it is made possible to implement reliable detection of the off-road driving situation, potentially with several input parameters.

[0015] According to embodiments, the first data set may comprise one or more of the following data: - vehicle driving data, including driving time; - at least one image from a vehicle camera; - a user input indicating off-road traffic; - vehicle position data.

[0016] Such first data enable precise detection of the off-road driving situation. Taking into account the driving duration makes it possible to avoid detection of a driving situation in a parking lot.

[0017] According to embodiments, the identification of the type of off-road driving situation may comprise an identification of a type of ground on which the vehicle is traveling based on the second set of data and / or the identification of a geographical area in which the vehicle is traveling based on a third set of data among the collected data, and the type of off-road driving situation may be identified based on the identified type of ground and / or the identified geographical area.

[0018] Thus, the type of off-road driving situation precisely identifies the driving situation, which allows the establishment of a better mission profile for off-road driving situations and / or which allows the precise implementation of the function of the other module of the vehicle, which may be a function aimed at protecting one or more components of the vehicle.

[0019] Additionally, identifying the soil type may include applying a second classification algorithm to the second data set, the second classification algorithm being configured to classify the second data set into at least one soil class from a predefined set of multiple soil classes.

[0020] Thus, it is possible to accurately identify the type of ground on which the vehicle is traveling, which improves the accuracy of identifying the type of off-road driving situation.

[0021] According to embodiments, the second data set may comprise one or more of the following data: - an exterior temperature of the vehicle; - data captured by a frost detector; - data captured by a sensor of the wiping system; - a vehicle speed; - a pair of vehicle wheels; and - data from a sensor of an anti-lock braking system.

[0022] Such a second set of data allows precise identification of the type of ground on which the vehicle is traveling.

[0023] According to embodiments, identifying the geographic area may comprise applying a third classification algorithm to the third dataset, the third classification algorithm being configured to classify the third dataset into at least one geographic class from a predefined set of geographic classes.

[0024] Thus, it is possible to precisely identify the geographical area in which the vehicle is traveling, which improves the accuracy of identifying the type of off-road driving situation.

[0025] According to embodiments, the third data set may comprise one or more of the following data: - at least one image captured by a vehicle camera; - vehicle position data; and - vehicle altitude data.

[0026] Such a third set of data allows precise identification of the geographical area in which the vehicle is traveling.

[0027] A second aspect of the invention relates to a computer program comprising instructions for implementing the method according to one of the preceding claims, when these instructions are executed by a processor.

[0028] A third aspect of the invention relates to a device for identifying an off-road driving situation of a motor vehicle, the device comprising: - at least one interface arranged to collect descriptive data of a vehicle driving situation; - a processor configured to detect, based on a first set of data from among the collected data, an off-road driving situation of the vehicle, and, following the detection of the off-road driving situation, to identify a type of off-road driving situation based on at least a second set of data from among the collected data. The processor is further configured to transmit the identified type of off-road driving situation to an entity remote from the vehicle and / or to a module of the vehicle capable of implementing at least one vehicle protection function based on the identified type of off-road driving situation.

[0029] Other characteristics and advantages of the invention will appear on examining the detailed description below, and the appended drawings in which:

[0030] [Fig-1] illustrates a motor vehicle according to embodiments of the invention;

[0031] [Fig.2] is a diagram illustrating the steps of a process according to rea modes lization of the invention;

[0032] [Fig.3] illustrates an off-road driving situation identification device for motor vehicle, according to embodiments of the invention.

[0033] [Fig.l] illustrates a motor vehicle 100, according to embodiments of the invention.

[0034] The vehicle 100 comprises in particular a device 101, which may be a centralized control device in charge of a plurality of functions of the motor vehicle, or which may alternatively be dedicated to a single function. The device 101 may be of the ECU type in particular, for “Electronic Control Unit” in English.

[0035] According to the invention, the device 101 is a device for identifying an off-road driving situation of the vehicle 100, as detailed below.

[0036] The device 101 is thus at least configured for the implementation of functions for detecting and identifying off-road driving situations, functions described below.

[0037] The vehicle 100 further comprises a driving assistance module 102 of the vehicle, also called AD AS, for “Advanced Driver Assistance Systems” in English. The AD AS module 102 is capable of implementing at least one driving assistance function based in particular on data from at least one sensor.

[0038] The term "driving assistance" for a vehicle means any method capable of assisting in the driving of the vehicle. The method may thus consist of partially or totally steering the vehicle, providing any type of assistance to a natural person driving the vehicle, as well as performing other functions such as functions enabling energy savings and / or the preservation or protection of vehicle components.

[0039] The module 102 makes it possible to improve driving comfort and safety and can make it possible to protect certain components of the vehicle in certain driving situations, by taking advantage of data from vehicle sensors, such as radar or lidar data, cameras and / or geolocation devices, but also based on information provided by other modules or devices of the vehicle, such as the device 101. For example, the AD AS module 102 can be configured to implement a protection function for one or more components of the vehicle, the protection function being activated upon detection of an off-road driving situation, and taking into account an identification of a type of off-road driving situation.

[0040] The vehicle 100 comprises a set 110 comprising N sensors, N being an integer greater than or equal to 1. No restriction is attached to the number of sensors or to the types of sensors with which the vehicle 100 is equipped. By way of example, the vehicle 100 may comprise one or more of the following sensors: - a radar; - a lidar; - an outside temperature sensor and / or a temperature sensor inside the vehicle passenger compartment; - a frost detector; - a sensor for the windshield wiping system; - a sensor capable of measuring the torque of the wheels of the vehicle 100; - a sensor capable of measuring the activation of an anti-lock braking system, or ABS; and / or - any other sensor of the vehicle 100.

[0041] The vehicle 100 may further comprise a memory 103 capable of storing data used by the device 101, by the AD AS module 102 and / or by any other module of the vehicle 100.

[0042] The vehicle further comprises a communication interface 104, which may be a cellular interface allowing the vehicle 100 to access a cellular network, such as a 3G, 4G, 5G or any other generation network, for example, or which may be a V2X interface allowing exchange with other equipment, such as a road infrastructure or other vehicles traveling near the vehicle 100.

[0043] The vehicle 100 may in particular be able to communicate, via the interface 106, with a remote entity, such as a remote server accessible via an internet network, accessible via the cellular network. Such a vehicle 100 is said to be “connected”. A connected vehicle 100 may thus interact with a remote terminal or a remote server, in particular, according to certain embodiments, with a server responsible for collecting data from several connected vehicles, for the establishment of mission profiles. The data thus sent to the server may comprise driving data and / or descriptive data of components of the vehicle, captured by one or more sensors of the vehicle and / or by one or more modules of the vehicle. According to these embodiments of the invention, the vehicle 100 may further send to the server descriptive information of an off-road driving situation, such as a type of off-road driving situation.The data stored in the server thus allows the establishment of mission profiles for off-road driving situations. An operator accessing these mission profiles can thus adjust the dimensioning of vehicle components to adapt it to these off-road driving situations, and / or can provide functions to be integrated into the vehicle, such as AD AS functions, to protect one or more of these components during off-road driving situations.

[0044] The vehicle 100 may further comprise a satellite positioning module 105, of the GPS type for example, for “Global Positioning System”, capable of obtaining and maintaining a current position of the vehicle.

[0045] Such a current position of the vehicle 100 can be superimposed by the control device 101 and / or by the AD AS module 102, with cartographic data in order to locate the vehicle 100 in the road infrastructure which surrounds it, in particular in order to determine the altitude or the geographical zone in which the vehicle is traveling.

[0046] The vehicle 100 may further comprise at least one camera 106 capable of obtaining images representative of a scene outside the vehicle 100, and / or of a scene inside the vehicle 100. For example, the camera 106 may be a camera facing the front of the vehicle, and capable of acquiring images of the scene facing the vehicle. The vehicle may comprise an exterior camera and an interior camera.

[0047] The vehicle 100 may further comprise at least one user interface, which is capable of receiving user inputs, for adjusting vehicle parameters or for interacting with an infotainment system of the vehicle. According to embodiments of the invention, the vehicle comprises at least one user interface 107 by which the user can indicate to the device 101 that the vehicle 100 is traveling off-road, or is about to travel off-road. The user interface 107 may be: - a microphone capable of receiving a voice command from the user, indicating off-road driving; - a button dedicated to indicating an off-road driving situation; - a touch screen, shared with other functions of the vehicle, by which the user can indicate, by a touch input on a graphic element associated with off-road driving, that the vehicle 100 is in an off-road driving situation.

[0048] Alternatively, the device 101 may receive the indication from the user that the vehicle is traveling off-road via an interface 108 capable of communicating with a user terminal located in the vehicle, such as a Smartphone. The interface 108 may be a wired interface, of the USB type for example, or may be a short or medium range interface of the Bluetooth or Wifi type for example.

[0049] [Fig.2] is a diagram illustrating the steps of a method for identifying an off-road driving situation, according to embodiments of the invention.

[0050] The method can be implemented in the device 101 described with reference to [Fig.l].

[0051] In a step 200, the off-road driving situation detection and identification function is activated by the device 101. Such activation may follow the reception of an activation message on the communication interface 104 of the connected vehicle 100, for example when a car manufacturer has a need to establish a mission profile for off-road driving situations, as part of a study. Such a study may indeed require an evaluation of the utilization rate of off-road driving situations, and / or may require data from sensors or modules of the vehicle 100 during off-road driving situations. On the basis of this data, the study may allow the improvement of the dimensioning of components of the vehicle and / or the improvement of protection functions of the vehicle activated in off-road driving situations.

[0052] In a step 201, the device 101 obtains or collects data from at least another module of the vehicle and / or at least one sensor of the vehicle. The data collected may include any combination of the following data: - vehicle driving data, for example indicating a current driving time; - one or more images from camera 106; - one or more user inputs on one or more user interfaces of the vehicle 100, for example a user input indicating off-road driving via the user interface 107 or received via the interface 108 from the user's mobile terminal; - data captured by the set 110 of at least one sensor of the vehicle, such as an outside temperature, data captured by the frost detector, data captured by the sensor of the wiping system, a speed of the vehicle, a torque of the wheels of the vehicle, data from the sensor of the ABS system; - position data of the vehicle 100 from the GPS module 105, and possibly altitude data obtained from the position data and cartographic data stored in the memory 103 of the vehicle; - any other descriptive data of the driving situation, obtained by a sensor or module of the vehicle.

[0053] Step 201 may be implemented over a collection period. No restriction is attached to the duration of the collection period.

[0054] In a step 202, the device 101 determines, based on a first set of data from among the data collected in step 201, whether the vehicle 100 is in an off-road driving situation or not.

[0055] For this purpose, the device 101 can apply a first set of classification rules to the first set of data, the set of classification rules being capable of classifying the first set of data into at least two categories: an off-road driving situation or another driving situation, for example an on-road situation, regardless of the type of road.

[0056] Alternatively, the device 101 may implement a first classification algorithm derived from artificial intelligence. For this purpose, the first classification algorithm may be trained on a first training database comprising first training data sets, comprising the same type of data as the first data set.

[0057] In the case of supervised learning training, each first training data set is associated with, or labeled by, a category, among the at least two categories mentioned above.

[0058] The first classification algorithm can be configured in the form of an artificial neural network for example, trained by supervised learning or not. supervised. However, there are no restrictions on the configuration of the first classification algorithm, which can be a clustering algorithm, a decision tree, or any other structure capable of classifying data into at least two categories.

[0059] No restriction is attached to the first data set, which may include any combination of the following data collected in step 201: - vehicle driving data, for example indicating the current driving time; - the image(s) from camera 106; - the user input indicating off-road traffic on the user interface 107 of the vehicle 100 or received via the communication interface 108 with the user's mobile terminal; - the position data of the vehicle 100 from the GPS module 105.

[0060] Taking into account the current driving time makes it possible to avoid taking into account fleeting driving situations in parking lots, for example. Geolocation makes it possible to determine, via GPS coordinates, whether the vehicle is traveling on a road or not. The images from the camera 106 can, for their part, make it possible to deduce from the external environment whether the vehicle is traveling on the road or not.

[0061] Step 202 can be iterated regularly, as and when the data collected in step 201 is obtained, and is thus a step of detecting an off-road driving situation.

[0062] Following step 202, if an off-road driving situation is detected, the method proceeds to steps 204 and 205. Otherwise, the method proceeds to an optional step 203, in which the device 101 can send the indication to the remote server that the vehicle is traveling on the road and / or the device 101 can indicate the on-road driving situation to another module of the vehicle, for example the AD AS module 102, for taking into account the on-road driving situation in at least one AD AS function. The method then returns to step 203, with driving continuing and new data being collected in step 201.

[0063] In step 204, the device 101 identifies a ground type of the off-road driving situation, from a second set of data among the data collected in step 201. The second set of data is different from the first set of data: the second set of data may be completely distinct from the first set of data, or may comprise at least some data in common. The second set of data may for example comprise any combination among the following data: - the outside temperature; - the data captured by the frost detector; - the data captured by the wiping system sensor; - the speed of the vehicle; - the torque of the vehicle's wheels; and / or - the data from the ABS system sensor.

[0064] In order to identify the soil type, the device 101 may implement a second classification algorithm capable of classifying the second data set into one soil class or into several soil classes, from a predefined set of several soil classes, from the second data set. For example, the soil classes may correspond to any combination of the following soil classes: - mud; - water ; - gravel; - sand ; - snow ; - any other type of soil.

[0065] The second classification algorithm can thus identify a type of ground, which can correspond to a single ground class such as “snow”, or to several ground classes, such as “snow and sand”, when the vehicle is traveling on both snow and sand.

[0066] The second classification algorithm may be a multi-class classification algorithm derived from artificial intelligence. For this purpose, the second classification algorithm may be trained on a second training database comprising second training data sets, comprising the same type of data as the second data set.

[0067] In the case of supervised learning training, each second training data set is associated with, or labeled by, one or more soil classes, among the soil classes mentioned above.

[0068] The second classification algorithm may be configured as an artificial neural network, for example, trained by supervised or unsupervised learning. However, no restrictions are attached to the configuration of the second classification algorithm, which may be a clustering algorithm, a decision tree, or any other structure capable of classifying data into at least two types or categories.

[0069] In step 205, the device 101 identifies a geographic area of ​​the vehicle 100 based on a third set of data from among the data collected in step 201. The third set of data may be different from the first set of data and the second set of data: the third set of data may be entirely distinct from the first data set and the second data set, or may include at least some data in common with the first data set and / or with the second data set. The third data set may, for example, include any combination of the following data: - the image or images captured by the camera 106; - position data from the GPS module 105, and / or - altitude data.

[0070] In order to identify the geographic area, the device 101 may implement a third classification algorithm capable of classifying the second data set into one or more geographic classes, from a predefined set of more geographic classes, from the third data set. For example, the geographic classes may correspond to any combination of the following geographic classes: - mountain ; - beach ; - desert ; - river ; - forest ; - any other class describing a geographic area.

[0071] Thus, the geographic class identifies the type of environment or ecosystem in which the vehicle circulates.

[0072] The third classification algorithm can thus identify a geographical area, which can correspond to a single geographical class, such as “mountain”, or to several geographical classes, such as “mountain and forest” for example.

[0073] The third classification algorithm may be a multi-class classification algorithm derived from artificial intelligence. For this purpose, the third classification algorithm may be trained on a third training database comprising third training data sets, comprising the same type of data as the third data set.

[0074] In the case of supervised learning training, every third training data set is associated with, or labeled by, one or more geographic classes, among the geographic classes mentioned above.

[0075] The third classification algorithm may be configured as an artificial neural network, for example, trained by supervised or unsupervised learning. However, no restrictions are attached to the configuration of the third classification algorithm, which may be a clustering algorithm, a tree of decision-making or any other structure capable of classifying data into at least two types or categories.

[0076] In a step 206, the device 101 determines a type of off-road driving situation, from the type of ground identified in step 204 and / or from the geographical area identified in step 205. Thus, step 204 or step 205 is optional, and the device 101 may implement only one of them. However, higher precision is allowed in identifying a type of off-road driving situation, when both steps 204 and 205 are implemented.

[0077] The type of off-road driving situation can be obtained by concatenating the identified soil type and the identified geographical area. For example, the following types of driving situations can be obtained, as examples: - snowy mountain; - snow-covered mountains and forests; - beach with sand; - beach with sand and mud.

[0078] Steps 204 and 205 may be optional. Indeed, as a variant, during step 206, the device 101 may directly identify a type of off-road driving situation from a single multi-class classification algorithm, capable of providing as output the concatenation of a geographical area and a type of soil.

[0079] Thus, more generally, the device 101 identifies a type of off-road driving situation on the basis of at least one second classification algorithm, different from the first classification algorithm used for the detection of the off-road driving situation of step 202, the at least one second classification algorithm preferably being multi-class.

[0080] In a step 207, the device 101 transmits to the remote server, via the communication interface 104 described previously, the type of off-road driving situation identified in step 206. The type of off-road driving situation can be transmitted with other complementary information such as sensor data captured during the off-road driving situation, or a duration of the off-road driving situation.

[0081] It is thus made possible to establish a mission profile by collecting the types of driving situations, and associated complementary data, in the centralized remote server, for a plurality of vehicles such as the vehicle 100. Such mission profiles allow better dimensioning of vehicle components and / or allow the development of vehicle component protection functions to be activated during off-road driving situations.

[0082] In addition to step 207, or as a variant of step 207, the device 101 can implement a step 208 during which the device 101 indicates to another vehicle module 100 that the vehicle is traveling off-road and further transmits the type of off-road driving situation identified to the other module. The other module may be the AD AS module 102 described previously, which may be capable of activating a function dedicated to off-road driving, the function being capable of taking into account the type of off-road driving situation identified. The function dedicated to off-road driving may in particular have the purpose of preserving one or more components during the off-road driving situation. The parameters of the function may advantageously depend on the type of off-road driving situation identified.

[0083] [Fig.3] shows the structure of a device 101 according to embodiments of the invention.

[0084] The device 101 comprises a processor 301 configured to communicate unidirectionally or bidirectionally, via one or more buses or via a direct wired connection, with a memory 302 such as a memory of the “Random Access Memory” type, RAM, or a memory of the “Read Only Memory” type, ROM, or any other type of memory (Flash, EEPROM, etc.). Alternatively, the memory 302 comprises several memories of the aforementioned types.

[0085] The memory 302 is capable of storing, permanently or temporarily, at least some of the data used and / or resulting from the implementation of the method described with reference to [Fig.2].

[0086] In particular, the memory 302 may store the aforementioned classification algorithm(s), in particular the first classification algorithm and the second and third multi-class classification algorithms. As described previously, a first set of at least one rule may be stored, in place of the first classification algorithm, for detecting the off-road driving situation. Alternatively, the classification algorithms and / or the first set of at least one rule are stored in the memory 103 of the vehicle, or in a remote server accessible from the communication interface 104.

[0087] The processor 301 is capable of executing instructions, stored in the memory 302, for implementing the steps of the method according to the invention, described with reference to [Fig. 2]. Alternatively, the processor 301 can be replaced by a microcontroller designed and configured to carry out the steps of the method according to the invention, described with reference to [Fig. 2].

[0088] The device 101 may comprise a first interface 303 capable of communicating with the set 110 of at least one sensor.

[0089] The device 101 may comprise a second interface 304 for communicating with the at least one camera 106.

[0090] The device 101 may comprise a third interface 305 for communicating with the memory 103 of the vehicle 100.

[0091] The device 101 may comprise a fourth interface 306 for communicating with the communication interface 104.

[0092] The device 101 may comprise a fifth interface 307 for communicating with the GPS module 105.

[0093] The device 101 may comprise a sixth interface 308 for communicating with the AD AS module 102.

[0094] The device 101 may comprise a seventh interface 309 for communicating with the user interface 107.

[0095] The device 101 may comprise an eighth interface 310 for communicating with the interface 108 described previously.

[0096] The device 101 may comprise other interfaces for communicating with other equipment of the vehicle 100.

[0097] The present invention is not limited to the embodiments described above as examples; it extends to other variants.

Claims

Claims

1. Method for identifying an off-road driving situation of a vehicle (100), implemented in a device (101) of the vehicle and comprising the following steps: - collection (200) of descriptive data of a driving situation of the vehicle; - detection (202), as a function of a first set of data among the collected data, of an off-road driving situation of the vehicle; - following the detection of the off-road driving situation, identification (204; 205; 206) of a type of off-road driving situation as a function of at least a second set of data among the collected data; - transmission (207; 208) of the type of off-road driving situation identified to an entity remote from the vehicle and / or to a module of the vehicle (102) capable of implementing at least one vehicle protection function on the basis of the type of off-road driving situation identified.

2. The method of claim 1, wherein detecting the off-road driving situation of the vehicle (100) comprises executing a first classification algorithm or applying a first set of classification rules, the first classification algorithm or the first set of classification rules being adapted to receive the first set of data as inputs, and to classify the first set of data into one of at least two categories comprising an off-road driving situation and at least one other driving situation.

3. Method according to one of claims 1 and 2, wherein the first data set comprises one or more of the following data: - driving data of the vehicle (100), including a driving time; - at least one image from a camera (106) of the vehicle; - a user input indicating off-road driving; - position data of the vehicle.

4. Method according to one of the preceding claims, wherein the identification (204; 205; 206) of the type of off-road driving situation comprises an identification (204) of a type of ground on which drives the vehicle according to the second set of data and / or the identification (205) of a geographical area in which the vehicle drives according to a third set of data from among the collected data, and in which the type of off-road driving situation is identified (206) according to the type of soil identified and / or the geographical area identified.

5. The method of claim 4, wherein identifying (204) the soil type comprises applying a second classification algorithm to the second data set, the second classification algorithm being configured to classify the second data set into at least one soil class from a predefined set of multiple soil classes.

6. Method according to claim 4 or 5, in which the second set of data comprises one or more of the following data: - a temperature outside the vehicle (100); - data captured by a frost detector; - data captured by a sensor of the wiping system; - a speed of the vehicle; - a torque of the wheels of the vehicle; and - data from a sensor of an anti-lock braking system.

7. The method of one of claims 4 to 6, wherein the identification (205) of the geographic area comprises applying a third classification algorithm to the third data set, the third classification algorithm being configured to classify the third data set into at least one geographic class from a predefined set of geographic classes.

8. Method according to one of claims 4 to 7, in which the third set of data comprises one or more of the following data: - at least one image captured by a camera (106) of the vehicle (100); - position data of the vehicle; and - altitude data of the vehicle.

9. Computer program comprising instructions for implementing the method according to one of the preceding claims, when these instructions are executed by a processor (301).

10. Device (101) for identifying an off-road driving situation of a motor vehicle (100), the device comprising: - at least one interface (303-310) arranged to collect descriptive data of a driving situation of the vehicle; - a processor (301) configured to detect, based on a first set of data from among the collected data, an off-road driving situation of the vehicle, and, following the detection of the off-road driving situation, to identify a type of off-road driving situation based on at least a second set of data from among the collected data; wherein the processor is further configured to transmit the identified off-road driving situation type to a remote entity of the vehicle and / or to a module (102) of the vehicle capable of implementing at least one vehicle protection function based on the identified off-road driving situation type.

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