Method and device for determining a vehicle's driving environment

Abstract

The present invention relates to a method and device for determining the driving environment (1) of a vehicle (10). To this end, information representative of one type of driving environment (from a first type and a second type) is determined by projecting the position of the vehicle (10) onto a map of the environment (1). This information is validated by a first model for predicting the type of environment (1) and then by a second model for predicting the type of environment (1). Figure 1

Description

Title of the invention: Method and device for determining a vehicle's driving environment technical field

[0001] The invention relates to methods and devices for determining the driving environment of a vehicle, particularly, but not exclusively, motor vehicles. The invention also relates to a method and device for processing vehicle data. In particular, the invention relates to a method and device for validating the type of environment in which the vehicle is operating, especially when the environment is off-road. The invention also relates to a method and device for controlling a set of on-board vehicle systems when the type of environment in which the vehicle is operating is off-road. Technological background

[0002] Modern vehicles are made up of a large number of components or parts developed and designed to operate in different types of environments, primarily on-road environments including roads with asphalt pavements. However, vehicles sometimes operate in so-called off-road or all-terrain environments, that is, on unpaved surfaces such as sand, gravel, riverbeds, mud, snow, rocks, or other natural terrain.

[0003] Detecting the type of environment in which the vehicle is operating is an important issue, particularly for identifying data associated with driving a vehicle in an off-road environment, the data thus collected being able to be used to develop and properly size the various components and parts of the vehicles.

[0004] Gaining a better understanding of vehicle use in real-world conditions is an important issue for car manufacturers, for example to improve mission profile calculations to better size components according to their actual use by customers or to learn failure prediction models on the training database reflecting the actual use of vehicles. Summary of the present invention

[0005] One object of the present invention is to solve at least one of the problems of the technological background described above.

[0006] Another object of the present invention is, for example, to improve the detection of an off-road type environment when a vehicle is traveling in such an environment.

[0007] Another object of the present invention is to improve the relevance of vehicle usage data, in particular for the design and modeling of vehicle components or organs.

[0008] According to a first aspect, the present invention relates to a method for determining a vehicle driving environment, the method being implemented by at least one processor and comprising the following steps: - reception of initial data representing a vehicle position and second data representing a map of the driving environment; - determination of information representative of a type of driving environment from a first type and a second type by projection of the position onto the map, the first type corresponding to a road environment and the second type to an off-road environment; - when the information is representative of the second type, first validation of the information by classification of third data representative of the driving environment into a first class representative of the first type or a second class representative of the second type, the third data being received from at least one camera on board the vehicle, the classification being implemented by a first prediction model learned in a first learning phase; - when the third data points have been classified in said second class, a second validation of the information by classification of fourth data points representative of a set of indicators of the driving environment into a third class representative of the first type or a fourth class representative of the second type, the set of indicators being determined from fourth data points representative of vehicle use and received from a set of devices on board the vehicle, the classification being implemented by a second prediction model learned in a second learning phase; and - determination of the driving environment based on a result of the classification of the fourth data.

[0009] The detection of an off-road environment in which the vehicle is traveling, according to the first aspect of the invention, is achieved in several stages, including two successive validation stages based on the use of predictive models employing machine learning techniques, or even deep learning techniques, fed by data acquired from sensors or devices embedded in the vehicle. This allows to improve confidence in determining the environment in which the vehicle is traveling, without driver intervention.

[0010] The data thus collected can be associated with the type of environment detected and then, for example, used to establish different mission profile calculations necessary for the sizing of components or organs on board vehicles operating in off-road environments, under real conditions.

[0011] According to one variant, the process further comprises the following steps when the fourth data have been classified in said fourth class: - transmission of a request to display a request for confirmation of the classification of the fourth data in the fourth class to a touch screen embedded in the vehicle; - receiving fifth data representing a response to the query from the touch screen; - refinement of parameters of the first prediction model and / or parameters of the second prediction model based on the fifth data points.

[0012] According to yet another variant, the method further includes a step of generating a set of control instructions for a set of embedded systems adapted to the off-road environment.

[0013] According to another variant, the touch screen corresponds to a touch screen of the vehicle or to a touch screen of a mobile communication device connected in communication to the vehicle via a wireless connection.

[0014] According to a further variant, the method further includes a processing step of the third and fourth data implemented before the first validation and the second validation, the processing including a filtering of at least a part of the third and fourth data and / or a smoothing of at least a part of the third and fourth data.

[0015] According to yet another variant, the set of indicators comprises at least one indicator from among: - a first indicator representative of the vehicle's driving time in the driving environment; - a second indicator representing an average vibration intensity measured in the vehicle; - a third indicator representing a relief of the driving environment; - a fourth indicator representing a ratio between a set of gear ratio changes and an average vehicle speed; - a fifth indicator representing the activation of an anti-lock braking system for the vehicle's wheels; - a sixth indicator representing a variation in altitude over a first time interval of duration less than a first threshold; - a seventh indicator representing the activation of a four-wheel drive mode; and - an eighth indicator representing average energy consumption by the vehicle over a second time interval of duration less than a second threshold.

[0016] According to a second aspect, the present invention relates to a device for determining a driving environment of a vehicle, the device comprising a memory associated with a processor configured for the implementation of the steps of the process according to the first aspect of the present invention.

[0017] According to a third aspect, the present invention relates to a vehicle, for example a motor vehicle, comprising the device according to the second aspect of the present invention

[0018] According to a fourth aspect, the present invention relates to a computer program which includes instructions adapted for carrying out the steps of the process according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0019] Such a computer program may use any programming language, and be in the form of source code, object code, or an intermediate form between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0020] 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 carrying out the steps of the process according to the first aspect of the present invention.

[0021] On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means or a hard disk drive.

[0022] 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 transmitted via an electrical or optical cable, by conventional or radio frequency, by self-directing laser beam, or by other means. The computer program according to the present invention can, in particular, be downloaded from an Internet-type network.

[0023] 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 the execution of the process in question. Brief description of the figures

[0024] Other features and advantages of the present invention will become apparent from the description of the particular and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 4, in which:

[0025] [Fig-1] schematically illustrates a vehicle driving environment, according to an example of a particular and non-limiting embodiment of the present invention;

[0026] [Fig.2] schematically illustrates a process for determining the driving environment of the vehicle of [Fig.1], according to a particular and non-limiting example of the present invention;

[0027] [Fig.3] illustrates a device configured to determine the driving environment of the vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention.

[0028] [Fig. 4] illustrates a flowchart of the different steps in a process for determining the driving environment of the vehicle of [Fig. 1], according to a particular and non-limiting embodiment of the present invention. Description of embodiment examples

[0029] A method and device for determining a vehicle driving environment will now be described in what follows with joint reference to Figures 1 to 4. The same elements are identified with the same reference signs throughout the description that follows.

[0030] The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to allow for the identification and distinction of different elements (such as operations, means, etc.) implemented in the embodiments described below. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0031] Fig. 1 schematically illustrates a vehicle 10 traveling in a driving environment 1, according to a particular and non-limiting embodiment of the present invention.

[0032] Vehicle 10 corresponds, for example, to a vehicle with an internal combustion engine, with electric motor(s), or even a hybrid vehicle with an internal combustion engine and one or more electric motors. Vehicle 10 thus corresponds, for example, to a land vehicle, such as a car, a truck, a bus, or a motorcycle.

[0033] The vehicle 10 corresponds to a so-called connected vehicle in that it carries a communication system configured to communicate with one or more remote devices 101 via a wireless communication network infrastructure. The remote device 101 corresponds, for example, to a server or a computer in the "cloud" 100.

[0034] The communication system of a connected vehicle includes, for example, one or more communication antennas connected to a telematic control unit (TCU), which is itself connected to one or more computers of the connected vehicle's embedded system. The antenna(s), the TCU, and the computer(s) form, for example, a multiplexed architecture for providing various services useful for the proper functioning of the connected vehicle and for assisting the driver and / or passengers of the connected vehicle in controlling the vehicle and / or for diagnosing the operation of one or more components of the connected vehicle.The computer(s) and the TCU communicate and exchange data with each other via one or more computer buses, for example a CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (according to ISO 17458) or Ethernet (according to ISO / IEC 802-3) type communication bus.

[0035] The mobile communication infrastructure enabling wireless data communication between the vehicle 10 and the remote device 101 includes, for example, one or more communication devices 102 of the type relay antenna (cellular network) or roadside unit, known as RSU. In a communication mode using such a network architecture, the data is transmitted, for example, by the vehicle connected to the remote device 101 from the "cloud" 100 via a relay antenna 102 (the antenna 102 being, for example, connected to the "cloud" 100 via a wired link and the remote device 101 being itself connected to the network infrastructure of the "cloud" 100 via a wired and / or wireless network).

[0036] The wireless communication system enabling data exchange between the connected vehicle 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 802.1 lp standards of ITS G5; or - a cellular network communication system, for example an LTE (Long-Term Evolution) network, 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 802.1 In or IEEE 802.1 lac.

[0037] The vehicle 10 advantageously carries a set of sensors and devices configured to detect or determine the type of driving environment 1 in which the vehicle 10 is traveling.

[0038] The type of driving environment corresponds to: - a first type corresponding to a road environment, that is to say an environment comprising roads with a pavement made of bitumen and of a regular appearance; or - a second type corresponding to an off-road environment, that is to say an environment without road in the sense of the first type, the surface 11 on which the vehicle 10 travels corresponding to an unpaved surface such as sand, gravel, a riverbed, mud, snow, rocks or other natural terrain in an off-road environment.

[0039] The vehicle 10 carries, for example, one or more of the following sensors: - one or more millimeter-wave radars arranged on the vehicle 10, for example at the front, at the rear, on each front / rear corner of the vehicle; each radar is adapted to emit electromagnetic waves and to receive the echoes of these waves reflected by one or more objects, in order to detect any object or obstacle and their distances from the vehicle 10; and / or - one or more LIDAR(s) (Light Detection and Ranging), a LIDAR sensor corresponding to an optoelectronic system composed of a laser emitter, a receiver including a light collector (to collect the portion of the light emitted by the emitter and reflected by any object located in the path of the light rays emitted by the emitter) and a photodetector that transforms the collected light into an electrical signal; a LIDAR sensor thus makes it possible to detect the presence of objects located in the emitted light beam and to measure the distance between the sensor and each detected object; and / or - one or more cameras, for example a front camera, a camera mounted on each corner of the vehicle, a rear camera; the camera(s) may be configured, for example, for RGB (Red, Green, Blue) image acquisition and / or for thermal (infrared) cameras; and / or - one or more external temperature sensors; and / or - an altimeter; and / or - one or more vibration sensors, for example arranged in different locations on the vehicle chassis 10.

[0040] The vehicle 10 corresponds, for example, to a vehicle configured to drive in a two-wheel drive mode and in a four-wheel drive mode, also called AWD mode (from the English "All-Wheel Drive"), the driver of the vehicle 10 selecting one mode or the other from a control device provided for this purpose in the passenger compartment of the vehicle 10.

[0041] According to one variant, vehicle 10 corresponds to a vehicle configured to drive only in two-wheel drive mode.

[0042] The vehicle 10 may also include, for example, one or more ADAS systems (Advanced Driver-Assistance System), such as, for example: - an electronic stability control system fitted to the vehicle, known by the acronyms ESC (from the English "Electronic Stability Control" or in French "Contrôle électronique de la tranquillité"), DSC (from the English "Dynamic Stability Control" or in French "Contrôle dynamique de la tranquillité") or ESP (from the English "Electronic Stability Program" or in French "Programme électronique de la tranquillité"), - a lane keeping assist system, known as the LKA system (from the English "Lane Keep Assist"), and / or - a traction control system, known as DST (Dynamic Steering Torque), and / or - an electronic traction control system, known as ASR (Anti-Slip Regulation), such an ASR system regulating acceleration to limit the loss of traction of the drive wheels, and / or - a system called ABS (from the German "Antiblockiersystem" or in French "système anti-blocage des roues"), and / or - an adaptive cruise control system, also known as ACC (Adaptive Cruise Control), and / or - a geolocation system enabling the vehicle 10 to obtain data or information representative of its geographical position at any time, for example in the form of coordinates (latitude and longitude), via a satellite link with a set of satellites (not shown on [Fig.1]), the geolocation system corresponding for example to a system of type GPS (from the English “Global Positioning System” or in French “Système de emplacement global”), Galileo or GLONASS.

[0043] Figure 2 schematically illustrates a process for determining the type of the driving environment 1 of the vehicle 10, according to particular and non-limiting embodiment examples of the present invention.

[0044] The process is implemented for example by one or more processors of one or more computers of the vehicle 10.

[0045] In a first operation of the process, a first set of data 201 is obtained or received from a set of sensors, devices or systems embedded in the vehicle 10 and a second set of data is obtained or received from one or more remote devices such as the server 101 via the unit or interface of wireless communication (e.g., TCU unit) of vehicle 10 and / or a mobile communication device (e.g., a smartphone) connected wirelessly (e.g., via Bluetooth® or Wi-Fi®) or wired (e.g., via USB (Universal Serial Bus)) to vehicle 10.

[0046] The first dataset 201 includes, for example: - representative data of a driving time of vehicle 10, for example the duration of a journey made with vehicle 10; and / or - representative vehicle speed data 10; and / or - representative image data of the vehicle's external environment 10, i.e., image data of the driving environment received from the vehicle's camera(s) 10; and / or - data representing vibrations experienced by the vehicle 10, for example by one or more components of the vehicle 10 such as the chassis, shock absorbers, etc.; and / or - data representing the topography of the surface 11 on which the vehicle 10 travels, for example obtained from a LIDAR of the vehicle 10; and / or - representative data of gear changes via the vehicle's gearbox 10; and / or - representative data on the activation or deactivation of embedded systems such as the ABS system; and / or - representative data on switching between two-wheel drive and four-wheel drive modes; and / or - representative data on energy consumption (electricity or fuel depending on the type of engine(s) of the vehicle 10); and / or - representative data on altitude variation; and / or - representative location data of vehicle 10, for example in the form of a pair of longitude and latitude data.

[0047] The above list is provided as a purely illustrative example and is not exhaustive, the first data set 201 comprising all data received from the vehicle 10's sensors, devices or on-board systems, via the vehicle 10's on-board network.

[0048] The second dataset 202 includes, for example: - representative mapping data of the environment in which vehicle 10 is traveling, this data being for example of the OpenStreetMap® type; and / or - representative location data of vehicle 10, for example obtained from a geolocation system implemented in the form of a mobile application running on a mobile communication device connected in communication to vehicle 10.

[0049] The above list is provided as a purely illustrative example and is not exhaustive, the second data set 202 comprising all data received from devices external to the vehicle 10 via a wired or wireless connection.

[0050] In a second optional operation 203 of the process, one or more treatments are applied to all or part of the data of the first dataset 201 and / or the second dataset 202.

[0051] The processing(s) correspond for example to a filtering of part of the data to remove erroneous or aberrant values ​​from the first data set 201 and / or the second data set 202, to a smoothing of the data, and to any appropriate processing to improve the consistency and coherence of the measured or received data.

[0052] In a third operation 204 of the process, information representative of the type of driving environment 1 is determined by comparing first data representative of a position of the vehicle and second data representative of a map of the driving environment 1. The comparison corresponds to a projection of the current position of the vehicle 10 onto a map of the environment in which the vehicle 10 is traveling, thus making it possible to determine whether the vehicle 10 is traveling on a road identified on the map or whether the vehicle 10 is traveling in an area outside of an identified road.

[0053] The information thus determined is representative of the first type corresponding to a road environment or of the second type corresponding to an off-road environment.

[0054] The information is for example coded on one bit, with the value '0' for the first type and the value '1' for the second type, or vice versa.

[0055] This third operation 204 corresponds to a first level of identification of the driving environment 1 in which the vehicle 10 is traveling.

[0056] When it is determined that the type of driving environment corresponds to the first type 21, the process ends and the driving environment is identified as corresponding to a road environment 21.

[0057] When it is determined that the type of driving environment corresponds to the second type (off-road environment), the process continues with the fourth operation 205.

[0058] In a fourth operation 205 of the process, a first validation of the information determined in the third operation 204 is implemented. This first validation is obtained by a classification of third data points representative of the driving environment, these third data points being received from the on-board camera(s).

[0059] These third data thus correspond to image data of the environment 1 acquired by the camera(s) of the vehicle 10 during the movement of the vehicle 10. The third data correspond, for example, to RGB data associated with each pixel of each image acquired.

[0060] The classification is implemented by a first prediction model learned in a learning phase. The first prediction model is implemented in the form of a neural network, for example a deep neural network.

[0061] The learning of the first prediction model corresponds, for example, to a supervised, semi-supervised or even unsupervised type of learning, based on training image data including environment images of the first type and environment images of the second type.

[0062] The classification of the third data results in a classification into a first class representative of the first type or into a second class representative of the second type.

[0063] This fourth operation 205 corresponds to a second level of identification of the driving environment 1 in which the vehicle 10 is traveling.

[0064] When the classification results in the classification of the third data in the first class, the result of the determination of the information of the second operation 203 is invalidated and the driving environment is identified as corresponding to the first type 21. The process ends and the driving environment is identified as corresponding to a road environment 21.

[0065] When the classification results in the third data being classified in the second class, the result of the information determination in the second operation 203 is validated and the driving environment 1 is identified as corresponding to the second type (off-road environment). The process then continues with the fifth operation 206.

[0066] In a fifth operation 206 of the process, a second validation of the information determined in the third operation 204 and validated in the fourth operation 205 is implemented. This second validation is obtained by classifying fourth data points representative of a set of indicators of the driving environment into a third class representative of the first type or a fourth class representative of the second type.

[0067] This set of indicators is determined or calculated from fourth data representing the use of the vehicle 10, this fourth data being received from a set of devices, such as sensors or computers, on board the vehicle 10. This fourth data corresponds to all or part of the first data set 201.

[0068] This new classification is implemented by a second prediction model learned like the first model in a learning phase. The second prediction model is implemented in the form of a neural network, for example a deep neural network.

[0069] The learning of the second prediction model corresponds, for example, to a supervised, semi-supervised or even unsupervised type of learning, based on training data representative of a set of indicators obtained from a set of vehicles (for example, a few thousand vehicles), the indicators obtained for the learning being of the same nature as the indicators determined for vehicle 10 and provided as input to the second prediction model.

[0070] The parameters of this second prediction model are thus generated according to any machine learning technique known to a person skilled in the art.

[0071] The set of indicators provided as input to the second prediction model includes one or more of the following indicators, in any possible combination: - a first indicator representative of the vehicle's driving time in the driving environment, for example to rule out identifying a parking area as corresponding to an off-road environment, a driving time below a threshold (for example 5 or 10 minutes) being considered an indicator of a parking area type environment; and / or - a second indicator representing an average vibration intensity measured in the vehicle; and / or - a third indicator representing the topography of the driving environment, for example determined from image data or data obtained from a LiDAR; and / or - a fourth indicator representing a ratio between a set of gear changes and an average vehicle speed; and / or - a fifth indicator representing an activation of the ABS system; and / or - a sixth indicator representing a variation in altitude over a first time interval of less than a first threshold, a significant variation over a short time interval (for example, less than 10, 15, 30 minutes) being considered an indicator of an off-road type environment; and / or - a seventh indicator representing the activation of a four-wheel drive mode; and / or - an eighth indicator representing the average energy consumption of the vehicle over a second time interval of shorter duration than a second threshold, a consumption higher than the average over a time interval of reduced (e.g. less than 10, 15, 30 minutes) can be considered an indicator of an off-road type environment.

[0072] This fifth operation 206 corresponds to a second level of identification of the driving environment 1 in which the vehicle 10 is traveling.

[0073] When the classification results in the fourth data being classified in the third class, the result of the information determination of the second operation 203 is invalidated and the driving environment is identified as corresponding to the first type 21. The process ends and the driving environment is identified as corresponding to a road environment 21.

[0074] When the classification results in the fourth data being classified into the fourth class, the result of the information determination in the second operation 203 is validated and the driving environment 1 is identified as corresponding to the second type 22 (off-road environment). The process then continues with the sixth operation 207.

[0075] In a sixth operation 207 of the process, the result of the classification of the fourth data into the fourth class is submitted to the driver of the vehicle 10 for validation or invalidation by the driver.

[0076] This sixth operation 207 allows the classifications implemented in the fourth operation 205 and the fifth operation 206 to be validated by a human and to refine the parameters of the first prediction model and / or the parameters of the second prediction model according to a method called reinforcement learning.

[0077] To this end, the sixth operation 207 includes the transmission of a request to display a confirmation request for the classification of the fourth data in the fourth class to a touch screen embedded in the vehicle 10. This touch screen corresponds, for example, to an embedded screen of the vehicle 10, the transmission of the request being made via one or more data buses of the embedded network linking the computer implementing the process to the computer controlling the screen of the vehicle 10. According to another example, this touch screen corresponds to an embedded screen of a mobile communication device connected in communication to the vehicle 10, the transmission of the request being made via a wired connection (for example USB) or wireless connection (for example Bluetooth® or Wifi®) linking the vehicle 10 to the mobile communication device.

[0078] Receiving the request triggers the display of graphical content asking the driver to confirm whether the environment is on-road or off-road, for example by tapping a first virtual button to confirm that the environment is indeed off-road and by tapping on a second virtual button to validate / confirm that the environment is of road type (i.e. invalidate that the environment is of off-road type).

[0079] In response to the transmitted request, the computer implementing the process receives fifth data points representing a response to the request from the touch screen, these fifth data points being representative of a touch press on the first virtual button or on the second virtual button.

[0080] The parameters of the first prediction model and / or the parameters of the second prediction model are then refined according to the fifth data, according to the so-called reinforcement learning method.

[0081] When the driver has validated that the environment is indeed of the off-road type, the process then continues with a seventh operation 208 according to a particular and optional embodiment.

[0082] In a seventh operation 208, a set of control instructions for a set of vehicle on-board systems 10 is generated, these instructions being adapted to the off-road environment.

[0083] These instructions are for example transmitted to the computers controlling this or these embedded systems for the automatic implementation of functions by this or these embedded systems improving driving in an off-road environment.

[0084] The instructions correspond, for example, to instructions for activating or deactivating systems such as ACC or ABS. These instructions correspond, for example, to instructions for activating or deactivating the high beams, for adapting the suspension system to the off-road environment, etc.

[0085] In one variant, these instructions further include instructions for the vehicle screen 10 to display a set of recommendations for the driver and / or to display a rapid emergency contact number in the event of an accident. In yet another variant, these instructions further include the activation of a function to limit the stress on certain components or parts of the vehicle 10.

[0086] All the data collected during the implementation of the process are for example stored in the memory of the vehicle 10 to be subsequently collected by an external device in order to be used later for the enrichment of datasets for example used to define mission profiles and / or model test environments.

[0087] Figure 3 schematically illustrates a device 6 configured to determine the type of driving environment of a vehicle, for example vehicle 10, according to a particular and non-limiting embodiment of the present invention. The device 3 corresponds, for example, to a device embedded in the vehicle, for example a computer.

[0088] Device 3 is, for example, configured to carry out the operations described opposite Figures 1 to 2 and / or the steps of the process described opposite [Fig. 4]. Examples of such a device 3 include, but are not limited to, embedded electronic equipment such as a vehicle's on-board computer, an electronic control unit such as an ECU (Electronic Control Unit), a smartphone, a tablet, or a laptop computer. The elements of device 3, individually or in combination, can be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. Device 3 can be implemented in the form of electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules.

[0089] The device 3 comprises one (or more) processor(s) 30 configured to execute instructions for carrying out the steps of the process and / or for executing instructions from the software embedded in the device 3. The processor 30 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 3 further comprises at least one memory 31, for example, volatile and / or non-volatile memory, and / or includes a memory storage device that may include volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk, or optical disk.

[0090] The computer code of the embedded software(s) including the instructions to be loaded and executed by the processor is for example stored on memory 31.

[0091] According to a particular and non-limiting embodiment, the device 3 comprises a block 32 of interface elements for communicating with external devices such as connected vehicles and / or measuring devices. The interface elements of the block 32 comprise one or more of the following interfaces: - radio frequency RF 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 2.4 GHz frequency band, or of the Sigfox type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced; - USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus" in French); - HDMI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - LIN interface (from the English "Local Interconnect Network", or in French "Réseau interconnecté local").

[0092] According to another particular and non-limiting embodiment, the device 3 includes a communication interface 33 which enables communication with other devices (such as other servers, databases) via a communication channel 330. The communication interface 33 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via the communication channel 330. The communication interface 33 corresponds, for example, to a wired Ethernet network (standardized by ISO / IEC 802-3).

[0093] According to a particular and non-limiting embodiment, the device 3 can provide output signals to one or more external devices, such as a display screen 340, touch or not, one or more speakers 350 and / or other peripherals 360 (projection system) via output interfaces 34, 35 and 36 respectively. According to a variant, one or more of the external devices is integrated into the device 3.

[0094] Figure 4 illustrates a flowchart of the different steps in a method for determining the driving environment of a vehicle, for example vehicle 10, according to a particular and non-limiting embodiment of the present invention. The method is implemented, for example, by one or more processors of a computer in vehicle 10, or by device 3 in Figure 3.

[0095] In a first step 41, first data representing a position of the vehicle and second data representing mapping of the driving environment are received.

[0096] In a second step 42, information representative of a type of driving environment among a first type and a second type is determined by projecting the position onto the map, the first type corresponding to a road environment and the second type to an off-road environment.

[0097] In a third step 43, a first validation of the information is implemented when the information is representative of the second type, the first validation being obtained by classification of third data representative of the driving environment into a first class representative of the first type or a second class representative of the second type, the third data being received from at least one camera on board the vehicle, the classification being implemented by a first prediction model learned in a first learning phase.

[0098] In a fourth step 44, a second validation of the information is implemented when the third data have been classified in said second class, the second validation being obtained by classifying fourth data representative of a set of indicators of the driving environment into a third class representative of the first type or a fourth class representative of the second type, the set of indicators being determined from fourth data representative of vehicle use and received from a set of devices on board the vehicle, the classification being implemented by a second prediction model learned in a second learning phase.

[0099] In a fifth step 45, the vehicle driving environment is determined based on a result of the classification of the fourth data.

[0100] According to one variant, the variants and examples of the operations described in relation to one of Figures 1 to 2 apply to the steps of the process in [Fig.4].

Claims

Demands

1. Method for determining a driving environment (1) of a vehicle (10), said method being implemented by at least one processor and comprising the following steps: - receiving (41) first data representing a position of said vehicle and second data representing a map of said driving environment (1); - determining (42) information representative of a type of said driving environment (1) from among a first type and a second type by projecting said position onto said map, the first type corresponding to a road environment and the second type to an off-road environment;- when said information is representative of the second type, first validation (43) of said information by classification of third data representative of said driving environment into a first class representative of the first type or a second class representative of the second type, said third data being received from at least one camera on board said vehicle (10), said classification being implemented by a first prediction model learned in a first learning phase;- when the third data have been classified in said second class, second validation (44) of said first information by classification of fourth data representative of a set of indicators of the driving environment into a third class representative of the first type or a fourth class representative of the second type, said set of indicators being determined from fourth data representative of use of said vehicle (10) and received from a set of devices on board said vehicle (10), said classification being implemented by a second prediction model learned in a second learning phase; and - determination (45) of the driving environment (1) as a function of a result of the classification of the fourth data.

2. A method according to claim 1, further comprising the following steps when the fourth data have been classified in said fourth class: - transmission of a request to display a request for confirmation of the classification of the fourth data in the fourth class to a touch screen embedded in said vehicle (10); - reception of fifth data representing a response to said request from said touch screen; - refinement of parameters of said first prediction model and / or of parameters of said second prediction model as a function of said fifth data.

3. A method according to claim 2, further comprising a step of generating a set of control instructions for a set of embedded systems adapted to the off-road environment.

4. Method according to claim 2 or 3, wherein said touch screen corresponds to a touch screen of said vehicle (10) or to a touch screen of a mobile communication device connected in communication to said vehicle (10) via a wireless connection.

5. A method according to any one of claims 1 to 4, further comprising a processing step of said third and fourth data implemented before the first validation and the second validation, said processing comprising filtering at least a part of said third and fourth data and / or smoothing at least a part of said third and fourth data.

6. A method according to any one of claims 1 to 5, wherein said set of indicators comprises at least one indicator from among: - a first indicator representing a driving time of the vehicle (10) in said driving environment (1); - a second indicator representing an average vibration intensity measured in said vehicle (10); - a third indicator representing a topography of said driving environment (1); - a fourth indicator representing a ratio between a set of gear changes and an average speed of said vehicle (10); - a fifth indicator representing an activation of an anti-lock braking system of said vehicle (10); - a sixth indicator representing a variation in altitude over a first time interval of duration less than a first threshold; - a seventh indicator representing the activation of a 4-wheel drive driving mode; and - an eighth indicator representing the average energy consumption of said mode over a second time interval of duration less than a second threshold.

7. A computer program comprising instructions for carrying out the method according to any one of the preceding claims, when such instructions are executed by a processor.

8. A computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the process according to any one of claims 1 to 6

9. 1 d O. Device (3) for determining a vehicle driving environment, said device (3) comprising a memory (31) associated with at least one processor (30) configured for carrying out the steps of the method according to any one of claims 1 to 6.

10. Vehicle (10) comprising the device according to claim 9.

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