FR3163629A1 - Method and device for determining a vehicle's driving environment - Google Patents

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 for the abstract: Figure 1

Description

Method and device for determining a vehicle's driving environment

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 vehicle is operating in an off-road environment.

Technological background

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 roads with asphalt surfaces. However, vehicles sometimes operate in off-road or all-terrain environments, meaning on unpaved surfaces such as sand, gravel, riverbeds, mud, snow, rocks, or other natural terrain.

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 can be used to develop and properly size the various components and organs of the vehicles.

Gaining a better understanding of vehicle usage 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

One 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, for example, to improve the detection of an off-road type environment when a vehicle is traveling in such an environment.

Another object of the present invention is to improve the relevance of vehicle usage data, particularly for the design and modeling of vehicle components or organs.

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.

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 improves confidence in determining the environment in which the vehicle is traveling, without driver intervention.

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 embedded in vehicles operating in off-road environments, in real conditions.

According to one variant, the process further includes the following steps once the fourth data points have been classified into 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.

According to yet another variant, the process also includes a step of generating a set of control instructions for a set of embedded systems adapted to the off-road environment.

According to another variant, the touchscreen corresponds to a touchscreen of the vehicle or to a touchscreen of a mobile communication device connected in communication to the vehicle via a wireless connection.

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

According to yet another variant, the set of indicators includes 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.

According to a second aspect, the present invention relates to a device for determining a vehicle driving environment, 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 a motor vehicle, comprising the device according to the second aspect of the present invention

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.

Such a computer program can 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.

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.

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 even a magnetic recording means or a hard drive.

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 a network such as the Internet.

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

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

FIG. 1 schematically illustrates a vehicle driving environment, according to a particular and non-limiting example of the present invention;

FIG. 2 schematically illustrates a process of determining the vehicle's driving environment. FIG. 1 , according to a particular and non-limiting example of the present invention;

FIG. 3 illustrates a device configured to determine the vehicle's driving environment FIG. 1 , according to a particular and non-limiting example of the present invention.

FIG. 4 illustrates a flowchart of the different stages of a process for determining a vehicle driving environment. FIG. 1 , according to a particular and non-limiting example of the present invention.

Description of examples of achievements

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.

The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to identify and distinguish 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.

There 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.

Vehicle 10, for example, corresponds to a vehicle with an internal combustion engine, an electric motor(s), or 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.

Vehicle 10 is considered a connected vehicle because it is equipped with a communication system configured to communicate with one or more remote devices 101 via a wireless communication network infrastructure. The remote device 101 could be, for example, a server or a computer in the cloud 100.

The communication system of a connected vehicle includes, for example, one or more communication antennas connected to a telematics control unit (TCU), which is itself connected to one or more computers in the vehicle's onboard system. The antenna(s), the TCU, and the computer(s) form a multiplexed architecture for providing various services essential to the proper functioning of the connected vehicle, assisting the driver and/or passengers in controlling the vehicle, and/or 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) data bus, CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (according to ISO 17458) or Ethernet (according to ISO/IEC 802-3).

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 such as a relay antenna (cellular network) or a roadside unit, known as a 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).

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 802.11p 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 802.11n or IEEE 802.11ac.

The vehicle 10 advantageously incorporates a set of sensors and devices configured to detect or determine the type of driving environment 1 in which the vehicle 10 is operating.

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.

Vehicle 10, for example, carries 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.

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 vehicle 10 selecting one mode or the other from a control device provided for this purpose in the passenger compartment of vehicle 10.

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

Vehicle 10 also includes, for example, one or more ADAS systems (Advanced Driver-Assistance System), such as:
- 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 the DST system (from the English "Dynamic Steering Torque" or in French "Couple de direction dynamique"), and/or
- an electronic traction control system, known as an ASR system (from the English "Anti-Slip Regulation" or in French "Control antipatinage"), 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 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 the 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.

There FIG. 2 schematically illustrates a process for determining the type of driving environment 1 of the vehicle 10, according to particular and non-limiting embodiments of the present invention.

The process is implemented, for example, by one or more processors of one or more computers of vehicle 10.

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 on board 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 wireless communication unit or interface (e.g. TCU unit) of the vehicle 10 and/or a mobile communication device (e.g. a smart phone or “Smartphone”) connected wirelessly (e.g. via Bluetooth® or Wifi®) or wired (e.g. via USB (Universal Serial Bus)) to the vehicle 10.

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
- representative data of 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
- representative data of the topography of surface 11 on which vehicle 10 travels, for example obtained from a LIDAR of 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.

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.

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.

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

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

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

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 the mapping 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.

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.

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.

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

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.

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.

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).

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

The classification is implemented by a first prediction model learned during a training phase. The first prediction model is implemented in the form of a neural network, for example a deep neural network.

The training 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.

The classification of the third data results in a classification into a first class representing the first type or into a second class representing the second type.

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

When the classification results in the third data being classified in the first 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.

When the classification results in the third data item 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.

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.

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.

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

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.

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

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 an average energy consumption by the vehicle over a second time interval of duration less than a second threshold, a consumption greater than the average over a reduced time interval (for example less than 10, 15, 30 minutes) being able to be considered as an indicator of an off-road type environment.

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

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.

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

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 vehicle 10 for validation or invalidation by the driver.

This sixth operation 207 allows for human validation of the classifications implemented in the fourth operation 205 and the fifth operation 206, and for refining the parameters of the first prediction model and/or the parameters of the second prediction model according to a method called reinforcement learning.

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.

Receiving the request triggers the display control of graphic content asking the driver to validate whether the environment is road or off-road, for example via a touch press on a first virtual button to validate / confirm that the environment is indeed off-road and via a touch press on a second virtual button to validate / confirm that the environment is road (i.e. invalidate that the environment is off-road).

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

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

When the driver has confirmed 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.

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

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

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 high beams, for adapting the suspension system to the off-road environment, etc.

In one variant, these instructions also include instructions for the vehicle's display 10 to show a set of recommendations for the driver and/or to display a quick emergency contact number in the event of an accident. In yet another variant, these instructions also include the activation of a function to limit the stress on certain components or parts of the vehicle 10.

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

There FIG. 3 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. Device 3 corresponds, for example, to a device embedded in the vehicle, for example a computer.

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 the 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, and a laptop computer. The elements of the device 3, individually or in combination, may be integrated into a single integrated circuit, into several integrated circuits, and/or into discrete components. The device 3 may be implemented as electronic circuits, software (or computer) modules, or a combination of electronic circuits and software modules.

Device 3 includes one or more processors 30 configured to execute instructions for carrying out the steps of the process and/or for executing instructions from the software embedded in Device 3. The processor 30 may include integrated memory, an input/output interface, and various circuits known to those skilled in the art. Device 3 further includes 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.

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.

According to a specific and non-limiting embodiment, device 3 includes a block 32 of interface elements for communicating with external devices such as connected vehicles and/or measuring devices. The interface elements of block 32 include 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").

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 network of the Ethernet type (standardized by the ISO/IEC 802-3 standard).

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 non-touch, 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.

There FIG. 4 illustrates a flowchart of the different stages of 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 of the FIG. 3 .

In a first step 41, first data representing a position of the vehicle and second data representing a map of the driving environment are received.

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.

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.

In a fourth step 44, a second validation of the information is implemented when the third data have been classified into 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.

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

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 of the FIG. 4 .

Claims (10)

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:
- receipt (41) of first data representing a position of said vehicle and of second data representing mapping of said driving environment (1);
- determination (42) of information representative of a type of said driving environment (1) from among a first type and a second type by projection of said position onto said mapping, 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 the 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. 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);
- receipt 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. 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. 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. 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. A method according to any one of claims 1 to 5, wherein said set of indicators comprises at least one indicator from:
- a first representative indicator of a vehicle driving time (10) in said driving environment (1);
- a second indicator representative of an average vibration intensity measured in said vehicle (10);
- a third indicator representative of a relief of said driving environment (1);
- a fourth indicator representing a ratio between a set of gear ratio 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 four-wheel drive mode; and
- an eighth indicator representing average energy consumption by said over a second time interval of duration less than a second threshold. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor. 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. 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. Vehicle (10) comprising the device according to claim 9.