Method and device for controlling acceleration of an electric vehicle

Abstract

The present invention relates to a method and a device for controlling the acceleration of an electric vehicle. For this purpose, first data representative of the speed of the electric vehicle and second data representative of an environment in which the electric vehicle is traveling are received (51). A value representative of a level of risk of traveling in the environment is predicted (52) by feeding a risk level prediction model with the first and second data. A set of acceleration control parameters is determined (53) as a function of a current driving mode of the electric vehicle and the value. The acceleration of the electric vehicle is controlled (54) as a function of the set of acceleration control parameters. Figure for abstract: Figure 5

Description

Title of the invention: Method and device for controlling the acceleration of an electric vehicle Technical field

[0001] The present invention relates to methods and devices for controlling the acceleration of an electric vehicle, in particular but not exclusively a motor vehicle. The present invention also relates to a method and a device for predicting a level of traffic risk in an environment of the electric vehicle. The present invention also relates to a method and a device for controlling a set of on-board systems of a vehicle to adapt a driving mode of the electric vehicle to the level of risk. Technological background

[0002] Contemporary vehicles, in particular electric vehicles, incorporate a system configured to select a driving mode from a plurality of vehicle driving modes such as an economy mode, a sport mode or even a normal mode. The control or operating parameters of vehicle components such as the engine or the transmission are modified according to the driving mode chosen by the driver, the acceleration being, for example, more lively for the sport mode than for the economy mode.

[0003] When a driving mode is selected by the driver, this driving mode applies until the driver selects another one. This can, however, prove dangerous for the occupants of the vehicle but also for other road users.

[0004] Furthermore, the driving modes are generally defined during the design of the vehicles, these driving modes thus not being adapted to all the life situations encountered by the vehicle. Summary of the present invention

[0005] An 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 to improve the control of the driving mode of an electric vehicle.

[0007] According to a first aspect, the present invention relates to a method for controlling the acceleration of an electric vehicle, the electric vehicle traveling in an environment according to a current driving mode, the method being implemented by at least one processor embedded in the electric vehicle, the method comprising the following steps: - receiving first data representative of the speed of the electric vehicle and second data representative of the environment from a set of devices on board the electric vehicle; - prediction of a value representative of a level of risk of circulation in the environment from a risk level prediction model supplied by the first and second data; - determining a set of acceleration control parameters of the electric vehicle as a function of the current driving mode and the value; and - acceleration control of the electric vehicle based on the set of acceleration control parameters.

[0008] Such a method makes it possible to determine a level of risk associated with the environment in which an electric vehicle is traveling. Using the level of risk to determine a set of parameters for controlling the acceleration of the vehicle according to the current driving mode of the vehicle makes it possible to adapt the control of the acceleration of the vehicle, as provided by the current driving mode, to the risk associated with the environment. This makes it possible to adapt the behavior provided for each driving mode of the electric vehicle to the environment crossed, for example to modulate the acceleration according to the determined level of risk.

[0009] This makes it possible to improve the safety of vehicle occupants and road users by, for example, avoiding excessive accelerations provided for by a driving mode when the latter is not adapted to the current context of the environment in which the electric vehicle is traveling (for example, a high density of users on the road).

[0010] According to a variant, the method further comprises a step of comparing the value to a threshold value, the determination of the set of acceleration control parameters of the electric vehicle comprising an adjustment of default acceleration control parameters associated with the current driving mode when the value is greater than the threshold value.

[0011] According to another variant, the second data comprises at least part of the following data: - data representative of the presence of objects detected in the environment; and / or - representative data on the density of objects detected in the environment; and / or - data representative of a type of environment; and / or - representative data on the curvature of portions of roads in the environment; and / or - data representative of events in the environment.

[0012] According to another variant, the acceleration control of the electric vehicle is furthermore a function of third data representative of an acceleration command.

[0013] According to a further variant, the acceleration control of the electric vehicle comprises a control of an electric motor of the electric vehicle and / or a control of a transmission of the electric vehicle.

[0014] According to another variant, the method further comprises a step of rendering information representative of the adjustment to at least one occupant of the electric vehicle.

[0015] According to an additional variant, the prediction model implements a set of determined rules for predicting the value.

[0016] According to a second aspect, the present invention relates to a device for controlling acceleration of an electric vehicle, the device comprising a memory associated with a processor configured for implementing the steps of the method according to the first aspect of the present invention.

[0017] According to a third aspect, the present invention relates to an electric vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

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

[0019] Such a computer program may use any programming language, and be in the form of source code, object code, or intermediate code 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 method according to the first aspect of the present invention.

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

[0022] On the other hand, this recording medium may also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or hertzian radio or by self-directed 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 perform or to be used in performing the method in question. Brief description of the figures

[0024] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting exemplary embodiments of the present invention below, with reference to the appended figures 1 to 5, in which:

[0025] [Fig.l] schematically illustrates an environment in which an electric vehicle circulates, according to a particular and non-limiting exemplary embodiment of the present invention;

[0026] [Fig.2] illustrates a flowchart of the different operations of a process of acceleration control of the electric vehicle of [Fig.l], according to a first particular and non-limiting exemplary embodiment of the present invention;

[0027] [Fig.3] illustrates a set of acceleration profiles for a driving mode of the electric vehicle of [Fig.l], according to a first particular and non-limiting exemplary embodiment of the present invention;

[0028] [Fig.4] schematically illustrates a device configured for control acceleration of the electric vehicle of [Fig.l], according to a particular and non-limiting exemplary embodiment of the present invention;

[0029] [Fig.5] illustrates a flowchart of the different stages of a control process acceleration of the electric vehicle of [Fig.l], according to a particular and non-limiting exemplary embodiment of the present invention. Description of examples of implementation

[0030] A method and a device for controlling the acceleration of an electric vehicle will now be described in the following with joint reference to FIGS. 1 to 5. The same elements are identified with the same reference signs throughout the description which follows.

[0031] The terms "first(s)", "second(s)" (or "first(s)", "second(s)"), etc. are used in this document by arbitrary convention to enable different elements (such as operations, means, etc.) implemented in the embodiments described below to be identified and distinguished. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0032] According to a particular and non-limiting example of embodiment of the present invention, the control of the acceleration of an electric vehicle is for example implemented by one or more computers of the vehicle, for example via one or more processors. The control of the acceleration advantageously comprises the determination of a value or a score representing the level of risk associated with the environment in which the electric vehicle is traveling. Such a value is determined or predicted by feeding a previously generated prediction model with first data representative of the speed of the electric vehicle and with second data representative of the environment in which the electric vehicle is traveling (for example the type of environment, the presence of objects such as other vehicles, pedestrians, bicycles or the number or density of these objects). The first data and the second data are received or obtained from devices embedded in the electric vehicle (for example one or more cameras, one or more radars or lidars, a navigation system, an odometer, a wireless communication interface connected to the "cloud", etc.).The value representing the risk level is used to determine a set of vehicle acceleration control parameters (e.g., response parameters of the vehicle's electric motor and / or transmission in response to an acceleration command) associated with a current driving mode, for example, by adjusting the default control parameters associated with the current driving mode of the electric vehicle. Finally, the acceleration of the electric vehicle is controlled based on the determined set of acceleration control parameters.

[0033] [Fig. 1] schematically illustrates an environment 1 in which an electric vehicle 10 operates, according to a particular and non-limiting exemplary embodiment of the present invention.

[0034] The vehicle 10 corresponds to an electric vehicle comprising one or more electric motors powered by a traction battery. The electric vehicle 10 thus corresponds, for example, to a land vehicle, for example an automobile, a truck, a bus, a utility vehicle.

[0035] The vehicle 10 advantageously carries a set of devices or systems configured to obtain data relating to the environment 1 in which the electric vehicle 10 moves. The environment 1 corresponds to a road environment whose type or nature changes as the electric vehicle 10 moves.

[0036] The road environment corresponds for example to an urban type environment, a motorway type environment, a countryside type environment (with national, departmental and / or municipal roads) or any other type of road environment.

[0037] The set of devices or systems for obtaining environmental data comprises one or more of the following devices or systems, in any possible combination: - one or more cameras 110 forming for example a 360° acquisition system with a front camera 110, a rear camera, a right side camera and a left side camera; and / or - one or more object detection sensors 102 (vehicle, motorcycle, bicycle, pedestrian, bicycle, scooter, etc.) such as radars and / or lidars arranged spatially on the vehicle 10 so as to detect any object present around the vehicle 10 in the detection field of each sensor; and / or - a navigation system or a navigation and geolocation system, also called a GNSS system ("Geolocation and Navigation by a Satellite System"), configured to determine on which portion of road the electric vehicle 10 is traveling, based on the geographical position of the electric vehicle 10 obtained from a geolocation system such as the GPS system (from the English "Global Positioning System" or in French "Geo-positioning system by satellites") or Galileo, and from environmental mapping data, the mapping data being for example stored in a memory of the electric vehicle 10 and / or obtained from a remote device such as a server 111 of the "cloud" 100, the mapping data comprising for example information on the environment 1 crossed, such as the type of environment, geometric information on the portions of road (for example the curvature),the presence of specific infrastructure (school, hospital, pedestrian crossing, etc.); and / or, - a wireless communication device or interface for communicating via a wireless connection with one or more remote server-type devices, such as the server 111, for example via an infrastructure of a wireless communication network comprising antennas 110 or roadside units (UBR): such a wireless communication interface is configured to obtain data relating to the environment 1 transmitted according to a wireless communication mode by one or more servers, this data comprising for example information on the presence of accidents, roadworks, meteorological information (rain, wind, ice, snow, fog, etc.), this information being associated with geolocation data making it possible to know the geographical location of these events.

[0038] The electric vehicle 10 advantageously also includes a device or system configured to determine at any time the current speed (for example in km / h or in m / s) of the electric vehicle 10, such a system corresponding for example to an odometer.

[0039] The computers controlling the devices or systems described above form a multiplexed architecture for the realization of different services useful for the proper functioning of the electric vehicle 10. The computers communicate and exchange data between them via one or more computer buses, for example a communication bus of the CAN data bus type (of English "Controller Area Network" or in French "Réseau de contrôles"), CAN FD (from the English "Controller Area Network Flexible Data-Rate" or in French "Réseau de contrôles à débit de données flexible"), FlexRay (according to the ISO 17458 standard), LIN (from the English "Local Interconnect Network" or in French "Réseau interconnecté local") or Ethernet (according to the ISO / IEC 802-3 standard).

[0040] The electric vehicle 10 also includes, for example, a set of driving assistance systems, known as AD AS systems (from the English “Advanced Driver-Assistance System” or in French “Advanced Driving Assistance System”).

[0041] The electric vehicle 10 is advantageously configured to travel according to a driving mode selected from a set comprising several driving modes, for example determined or defined in a development or design phase of the electric vehicle 10 or in a development or design phase of a driving mode control system.

[0042] The set of driving modes comprises for example the following driving modes, or part of the following modes: - sports mode; - aggressive mode; - economy mode; - standard mode; - quiet mode.

[0043] A set of parameters for controlling organs or systems of the electric vehicle is associated with each mode. The set of parameters includes, for example, parameters for controlling acceleration in response to an acceleration command when the driver presses the accelerator pedal of the electric vehicle 10. The acceleration control parameters include, for example, parameters for controlling the electric motor or even parameters for controlling a transmission of the electric vehicle to adapt the acceleration command received to the driving mode chosen by the driver. Thus, for the same acceleration command received from the accelerator pedal, the acceleration will be faster or slower for the sport mode than for the economy or standard mode. The response to the same acceleration command thus varies depending on the driving mode chosen.

[0044] The acceleration control parameters associated with each driving mode correspond to so-called default or factory parameters, i.e. parameters defined in a design phase of the driving mode control system.

[0045] According to a variant, control parameters of other organs or systems of the vehicle are associated with each mode, for example control parameters of the shock absorber system of the electric vehicle 10, control parameters of the steering control system, etc.

[0046] A process for controlling the acceleration of the electric vehicle 10 is advantageously implemented by one or more devices on board the vehicle 10, for example by one or more computers in the on-board network of the vehicle 10. Examples of implementation of such a process are described with regard to [Fig.2] below.

[0047] [Fig.2] illustrates a flowchart of the different operations of a process for controlling the acceleration of the electric vehicle 10, according to particular and non-limiting exemplary embodiments of the present invention.

[0048] A current driving mode is selected and active while the electric vehicle is traveling in the environment 1. The current driving mode corresponds for example to a driving mode selected automatically and by default when starting the electric vehicle 10 or to a driving mode selected from a list of driving modes available for the electric vehicle 10 by the driver via a control means of a human-machine interface (HMI).

[0049] In an operation 201, first data representative of the speed of the electric vehicle 10 and second data representative of the environment are received from a set of devices on board the electric vehicle 10.

[0050] The set of devices comprises all or part of the following devices or systems: - one or more cameras; and / or - one or more object detection sensors (vehicle, motorcycle, bicycle, pedestrian, bicycle, scooter, animals, etc.) such as radars and / or lidars arranged spatially on the electric vehicle 10; and / or - a navigation system or a navigation and geolocation system, also called a GNSS system (“Geolocation and Navigation by a Satellite System”; and / or - a wireless communication device or interface for communicating via a wireless connection with one or more remote server-type devices, such as server 111.

[0051] The second data includes all or part of the following data: - data representative of the presence of objects detected in the environment, these data being obtained from the camera(s) and / or radar(s) and / or lidar(s) of the electric vehicle 10; and / or - data representative of the density of objects detected in the environment, for example the density or number of objects present around the vehicle, these data being for example determined from the object detection data by the camera(s) and / or radar(s) and / or lidar(s) of the electric vehicle 10; and / or - data representative of a type of environment (for example envi urban or non-urban environment, the urban environment being able to be of the city center type, residential area, parking area (parking lot), area with school, area with hospital and the non-urban environment being able to be of the motorway type, expressway, or other (national, departmental, municipal roads), these data being for example obtained from map data controlled by the navigation system; and / or - data representative of the curvature of portions of roads in the environment, these data being for example obtained from map data controlled by the navigation system; and / or - data representative of events (accidents, works, meteorological events, etc.) in the environment, this data being obtained from the wireless communication interface (for example the telecommunications unit of the electric vehicle 10, called TCU (from the English “Telematic Control Unit” or in French “Telematic Control Unit”)) which connects the vehicle 10 to the “cloud” 100 according to a wireless communication mode.

[0052] In an operation 202, a value (or a score) representing the level of traffic risk in the environment 1 is predicted by feeding one or more prediction models with the first and second data. The prediction model(s) 203 comprise for example a set of rules generated to determine the level (represented by a value between 0 and 1 or by a value corresponding to a non-zero positive integer, the risk increasing with the value), these rules or the parameters of the model(s) having been defined in a learning or design phase of the system for determining the level of risk associated with traffic in a given environment at a given speed.

[0053] These rules or model parameters are for example stored in a memory of the computer implementing the process.

[0054] Some examples of rules for predicting a risk level and the associated value are provided as illustrative examples in the list below, this list not being exhaustive: - Rule 1: the density of moving objects detected by the cameras of vehicle 10 for each 120° sector in a distance between 30 m and 150 m is greater than or equal to 1 AND the type of environment corresponds to one of the following types: city center, residential area or parking area, the associated value being 1 when the conditions of rule 1 are met; - Rule 2: the density of moving objects detected by the cameras of vehicle 10 for each 90° sector in a distance between 30 m and 150 m is greater than or equal to 2 AND the type of environment corresponds to one of the following types: city center, residential area or parking area, the associated value being 2 when the conditions of rule 2 are met; - Rule 3: the density of moving objects detected by the cameras of vehicle 10 for each 60° sector in a distance between 25 m and 60 m is greater than or equal to 3 AND the density of moving objects detected by the radars of vehicle 10 for each 90° sector in a distance between 15 m and 30 m is greater than or equal to 2 AND the type of environment corresponds to one of the following types: city center, school, residential area, parking area or area with roads with slight curvatures (i.e. less than a given threshold), the associated value being 3 when the conditions of rule 3 are met; - Rule 4: the density of moving objects detected by the cameras of vehicle 10 for each 45° sector in a distance between 6 m and 30 m is greater than or equal to 5 AND the density of moving objects detected by the radars of vehicle 10 for each 45° sector in a distance between 3 m and 15 m is greater than or equal to 3 AND the type of environment corresponds to one of the following types: city center, school, residential area, parking area or area with roads with slight curvatures (i.e. less than a given threshold), the associated value being 4 when the conditions of rule 4 are met.

[0055] According to another example, the rules and / or the prediction models are learned according to any machine learning method in a learning phase from learning data, the learning method being supervised or unsupervised.

[0056] In an optional operation 204, the value is compared to a determined threshold value, which corresponds to a fixed or adjustable parameter of the acceleration control system of the electric vehicle 10.

[0057] The threshold value is identical for all driving modes available for the electric vehicle 10. According to another example, the threshold value depends on the current driving mode. According to this other example, the threshold value is for example equal to 1 for the sport mode, to 2 for the standard mode and to 3 for the economical or calm mode.

[0058] When the result of the comparison indicates that the value is greater than the threshold value, the process continues with operation 205.

[0059] When the result of the comparison indicates that the value is less than the threshold value, the process loops back to operation 201.

[0060] In an operation 205, a set of acceleration control parameters of the electric vehicle 10 is determined based on the current driving mode and the value predicted in operation 202.

[0061] The set of acceleration control parameters is for example obtained from a register or a memory 206 of the electric vehicle 10. The set of acceleration control parameters is for example selected from a correspondence table, called LUT (from the English “Look-Up Table”), which associates a determined set of acceleration control parameters for each current driving mode input pair / value representative of a risk level.

[0062] According to an alternative embodiment, the set of acceleration control parameters is determined by adjusting the default acceleration control parameters of the current driving mode as a function of the value predicted at operation 202.

[0063] Thus, the higher the risk level, the more the response to the acceleration command received from the accelerator pedal is dampened over time to reduce acceleration.

[0064] As the risk level increases, reducing the intensity of the acceleration allows for more gradual acceleration over time, even if the acceleration command obtained from the accelerator pedal is of high intensity.

[0065] [Fig.3] illustrates a diagram 3 representing different acceleration response profiles each associated with a risk level value. The x-axis represents for example the time (in seconds) and the y-axis the speed (in m / s).

[0066] The response profile 30 represents the acceleration obtained for a given acceleration command with the set of default acceleration control parameters associated with the current driving mode. The response profile 31 represents the acceleration obtained for the given acceleration command with the set of acceleration control parameters associated with the current driving mode and a risk level of value 1. The response profile 32 represents the acceleration obtained for the given acceleration command with the set of acceleration control parameters associated with the current driving mode and a risk level of value 2. The response profile 33 represents the acceleration obtained for the given acceleration command with the set of acceleration control parameters associated with the current driving mode and a risk level of value 3.Response profile 34 represents the acceleration obtained for the given acceleration command with the set of acceleration control parameters associated with the current driving mode and a risk level of value 4.

[0067] As shown in [Fig. 3], the higher the risk level, the slower and lower the acceleration for a given driving mode. The higher the risk level, the lower the maximum acceleration value, and the slower and lower the increase in acceleration.

[0068] The set of acceleration control parameters comprises, for example, parameters for controlling the electric motor of the electric vehicle 10 and, optionally, parameters for controlling the transmission of the electric vehicle 10.

[0069] In an optional additional operation 207, representative information of an adjustment of the current driving mode, that is to say of an adjustment of the response to the acceleration command, are made to at least one occupant of the electric vehicle.

[0070] The rendering comprises for example the control of display of graphic content on a screen of the electric vehicle to display a message and / or a pictogram on the screen warning the occupants, for example the driver, that the behavior of the electric vehicle 10 is automatically adapted to the environment 1, in particular in terms of acceleration.

[0071] According to a variant, the rendering comprises the generation and diffusion of sound content via the speakers of the electric vehicle 10.

[0072] In an operation 208, the acceleration of the electric vehicle 10 is controlled as a function of the set of acceleration control parameters determined in operation 205. The acceleration is controlled on the basis of an acceleration profile as represented in [Fig. 3] upon receipt of third data representative of an acceleration command (these third data being for example received from a computer controlling the accelerator pedal of the electric vehicle 10).

[0073] The current driving mode is adjusted according to the risk level represented by the value predicted at operation 202.

[0074] The process then loops back to operation 201 to obtain new first and second data to determine whether it is still necessary to adjust the current driving mode.

[0075] When the risk level falls below the threshold (when operation 204 is implemented), the acceleration control parameters then correspond to the default parameters associated with the current driving mode.

[0076] When operation 204 is not implemented, the acceleration control parameters correspond to the default parameters associated with the current driving mode when the predicted risk level is equal to a determined value, for example equal to 1.

[0077] For example, content is rendered to inform the driver that the current driving mode is restored to its default operation.

[0078] According to a particular embodiment, the implementation of the adjustment of the current driving mode is inhibited at any time upon command or request from the driver, for example by receiving data representative of a command to inhibit the function of adjusting the current driving mode (the command being required via a voice command or via pressing a physical or virtual button of an HMI).

[0079] [Fig.4] schematically illustrates a device 4 configured for controlling the acceleration of a vehicle, for example the electric vehicle 10, according to an example particular and non-limiting embodiment of the present invention. The device 4 corresponds for example to a device on board the electric vehicle 10, for example a computer.

[0080] The device 4 is for example configured for the implementation of the operations described with regard to figures 1 to 3 and / or the steps of the method described with regard to [Fig.5]. Examples of such a device 4 include, but are not limited to, on-board electronic equipment such as an on-board computer of a vehicle or an electronic calculator such as an ECU (“Electronic Control Unit”), a TCU. The elements of the device 4, individually or in combination, can be integrated in a single integrated circuit, in several integrated circuits, and / or in discrete components. The device 4 can be produced in the form of electronic circuits or software (or computer) modules or even a combination of electronic circuits and software modules.

[0081] The device 4 comprises one (or more) processor(s) 40 configured to execute instructions for carrying out the steps of the method and / or for executing the instructions of the software(s) embedded in the device 4. The processor 40 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 4 further comprises at least one memory 41 corresponding for example to a volatile and / or non-volatile memory and / or comprises a memory storage device which may comprise volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0082] The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored in the 4L memory.

[0083] According to various particular and non-limiting embodiments, the device 4 is coupled in communication with other similar devices or systems (for example the computer controlling the electric motor and / or the computer controlling the transmission) and / or with communication devices, for example a TCU (from the English “Telematic Control Unit” or in French “Telematic Control Unit”), for example via a communication bus or through dedicated input / output ports.

[0084] According to a particular and non-limiting exemplary embodiment, the device 4 comprises a block 42 of interface elements for communicating with external devices. The interface elements of the block 42 comprise one or more of the following interfaces: - RF radio frequency interface, for example Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox type using a 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”).

[0085] According to another particular and non-limiting exemplary embodiment, the device 4 comprises a communication interface 43 which makes it possible to establish communication with other devices (such as other computers of the on-board system) via a communication channel 430. The communication interface 43 corresponds for example to a transmitter configured to transmit and receive information and / or data via the communication channel 430. The communication interface 43 corresponds for example to a wired network of the CAN (from the English "Controller Area Network" or in French "Réseau de contrôles") type, CAN FD (from the English "Controller Area Network Flexible Data-Rate" or in French "Réseau de contrôles à débit de données flexible"), FlexRay (standardized by the ISO 17458 standard) or Ethernet (standardized by the ISO / IEC 802-3 standard).

[0086] According to a particular and non-limiting exemplary embodiment, the device 4 can provide output signals to one or more external devices, such as a display screen 440, touch-sensitive or not, one or more speakers 450 and / or other peripherals 460 (projection system) via output interfaces 44, 45 and 46 respectively. According to a variant, one or other of the external devices is integrated into the device 4.

[0087] [Fig. 5] illustrates a flowchart of the different steps of a method for controlling the acceleration of an electric vehicle, for example the electric vehicle 10, according to a particular and non-limiting exemplary embodiment of the present invention. The method is for example implemented by a device on board the electric vehicle 10 or by the device 4 of [Fig. 4].

[0088] In a first step 51, first data representative of the speed of the electric vehicle and second data representative of the environment are received from a set of devices on board the electric vehicle.

[0089] In a second step 52, a value representative of a level of risk of circulation in the environment is predicted from a risk level prediction model supplied by the first and second data.

[0090] In a third step 53, a set of acceleration control parameters of the electric vehicle is determined as a function of the current driving mode and the value.

[0091] In a fourth step 54, the acceleration of the electric vehicle is controlled according to the set of acceleration control parameters.

[0092] According to a variant, the variants and examples of the operations described in relation to one of figures 1 to 3 apply to the steps of the method of [Fig.5].

Claims

Claims

1. Method for controlling the acceleration of an electric vehicle (10), said electric vehicle traveling in an environment (1) according to a current driving mode, said method being implemented by at least one processor embedded in said electric vehicle (10), said method comprising the following steps: - receiving (51) first data representative of the speed of said electric vehicle (10) and second data representative of said environment (1) from a set of devices (101, 102) embedded in said electric vehicle (10); - predicting (52) a value representative of a level of traffic risk in said environment (1) from a risk level prediction model supplied by said first and second data; - determining (53) a set of acceleration control parameters of the electric vehicle (10) as a function of said current driving mode and said value;and - control (54) of acceleration of said electric vehicle (10) according to said set of acceleration control parameters.;

2. The method of claim 1, further comprising a step of comparing said value to a threshold value, wherein determining said set of acceleration control parameters of the electric vehicle (10) comprises adjusting default acceleration control parameters associated with said current driving mode when said value is greater than said threshold value.

3. Method according to claim 1 or 2, for which said second data comprises at least part of the following data: - data representative of the presence of objects detected in said environment (1); and / or - data representative of the density of the objects detected in said environment (1); and / or - data representative of a type of the environment (1); and / or - data representative of the curvature of portions of roads in said environment (1); and / or - data representative of events in said environment (1).

4. Method according to one of claims 1 to 3, for which said control acceleration of the electric vehicle (10) is further a function of third data representative of an acceleration command.

5. A method according to one of claims 1 to 4, wherein said acceleration control of the electric vehicle (10) comprises a control of an electric motor of said electric vehicle (10) and / or a control of a transmission of said electric vehicle (10).

6. Method according to one of claims 1 to 5, further comprising a step of rendering information representative of an adjustment of the current driving mode to at least one occupant of said electric vehicle (10).

7. Method according to one of claims 1 to 6, for which said prediction model implements a set of determined rules for predicting said value.

8. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor.

9. Device (4) for controlling the acceleration of an electric vehicle, said device (4) comprising a memory (41) associated with at least one processor (40) configured for implementing the steps of the method according to any one of claims 1 to 7.

10. Electric vehicle (10) comprising the device (4) according to claim 9.

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