FR3138533A1 - Method and device for controlling a vehicle driving assistance system based on a level of performance of the shock absorbers - Google Patents

Abstract

The present invention relates to a method and a device for controlling one or more ADAS systems of a first vehicle (10) as a function of a performance level of the shock absorbers of the first vehicle (10). The performance level is determined by classification of data representative of the dynamic behavior of the vehicle (10) and data representative of the environment (1) of the first vehicle (10) on the basis of a model learned during a phase d learning from dynamic behavior data of a set of second vehicles (11) and data from their environment. Figure for abstract: Figure 1

Description

Method and device for controlling a vehicle driving assistance system based on a level of performance of the shock absorbers

The present invention relates to the methods and devices for controlling at least one driving assistance system, called the ADAS system (from the English “Advanced Driver-Assistance System” or in French “Advanced Driving Assistance System”). "), of a vehicle. The present invention also relates to a method and a device for determining or predicting a performance level of one or more shock absorbers of a vehicle. The present invention also relates to a method and a device for controlling a vehicle, in particular an autonomous or semi-autonomous vehicle.

Technology background

Contemporary vehicles are made up of a large number of components or organs, each susceptible to breakdown, defect or wear.

When the driver of a vehicle is confronted with wear or a defect in a vehicle component such as the vehicle's shock absorbers, the behavior of the vehicle may be affected with a risk to the safety of the vehicle and its occupants. .

One of the problems encountered by the driver is that the defect or wear of the component concerned is not easily detectable by the driver. To establish the diagnosis, it is often necessary to have the vehicle checked by a professional.

To limit the risk of breakdown or defect, to measure the level of wear and prevent associated problems, preventive vehicle maintenance operations are planned. However, these maintenance operations do not guarantee that a vehicle component will encounter a defect or suffer a breakdown or unforeseen wear after the fact.

Such monitoring through the implementation of regular preventive maintenance operations is also synonymous with costs and inconvenience caused by the immobilization of the vehicle during the maintenance action.

Summary of the present invention

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

Another object of the present invention is to prevent the risks associated with wear or defect in the shock absorbers of a vehicle.

Another object of the present invention is to improve the prediction of breakdowns or wear of vehicle shock absorbers.

According to a first aspect, the present invention relates to a method for controlling at least one driving assistance system, called the ADAS system, of a vehicle, the method being implemented by at least one processor and comprising the steps following:

- reception of first data representative of dynamic behavior of the vehicle from a set of sensors on board the vehicle, the set of sensors comprising at least one sensor;

- reception of second data representative of an environment in which the vehicle is traveling;

- determination of first information representative of a performance level of a set of shock absorbers of the vehicle by classification of the first and second data from a classification model learned in a so-called learning phase prior to reception of the first and second data, the performance level being associated with a class of a set of classes output from the classification model;

- control of at least one ADAS system based on the first information.

The use of data representative of the dynamic behavior of the vehicle and of vehicle environment data acquired in real time to cross-reference them with a classification model learned beforehand makes it possible to determine or predict a level of performance (for example a level wear and/or a level of damping efficiency) of one or more shock absorbers of the vehicle in real time. One or more ADAS systems (for example the ABS system, the ESP system and/or a system controlling one or more components of the traction chain) are then controlled according to the level of performance, for example to adapt the driving mode of the vehicle. vehicle to anticipate a problem associated with the determined performance level.

This makes it possible to improve the safety of the vehicle and its passengers and/or to improve the life of the vehicle's shock absorbers by adapting the operation of the vehicle's ADAS systems to the estimated performance level of the shock absorbers.

According to a variant, the first information comprises second information representative of a level of performance of each shock absorber of the set of shock absorbers, the method further comprising the steps of:

- comparison of the second information with each other;

- determination of third information representative of asymmetry between at least two shock absorbers of the set of shock absorbers according to a result of the comparison.

According to another variant, the method further comprises a step of rendering a message representative of the level of performance and/or the asymmetry in a passenger compartment of the vehicle.

According to an additional variant, the at least one ADAS system belongs to a set of ADAS systems comprising:

- an electronic stability control system, called ESC system;

- an anti-lock wheel system, called ABS system;

- a speed control system;

- a trajectory control system;

- a route control system;

- a system for controlling at least one component of a vehicle traction chain.

According to yet another variant, the first data belong to a set of data comprising:

- representative lateral acceleration data;

- data representative of longitudinal acceleration;

- data representative of yaw speed;

- representative speed data;

- representative steering wheel angle data;

- data representative of vertical acceleration;

- representative suspension travel data;

- data representative of noise.

According to an additional variant, the second data belongs to a set of data comprising:

- data representative of meteorological conditions;

- data representative of the condition and/or level of road maintenance;

- data representative of road type;

- data representative of the presence of speed bumps or speed bumps.

According to another variant, the classification is implemented via a k-means partition method implemented by a neural network.

According to an additional variant, the neural network is a recurrent network type network with short and long term memory, called LSTM network.

According to a second aspect, the present invention relates to a device for controlling at least one driving assistance system, called the ADAS system, of a vehicle, the device comprising a memory associated with a processor configured for the implementation steps of the method according to the first aspect of the present invention.

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

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

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

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

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

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

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

Brief description of the figures

Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting examples of embodiment of the present invention below, with reference to the appended Figures 1 to 5, in which:

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

schematically illustrates a grouping or partitioning of data obtained in the environment of the in a predetermined number of groups, according to a particular and non-limiting embodiment of the present invention;

schematically illustrates a short-term and long-term memory cell according to a particular and non-limiting embodiment of the present invention;

schematically illustrates a device 4 configured to control at least one ADAS system of a vehicle of the , according to a particular and non-limiting embodiment of the present invention; And

illustrates a flowchart of the different stages of a method of controlling at least one ADAS system of a vehicle of the , according to a particular and non-limiting embodiment of the present invention.

Description of the implementation examples

A method and a device for controlling at least one ADAS system of a vehicle will now be described in what follows with reference jointly to Figures 1 to 5. The same elements are identified with the same reference signs throughout of the description which follows.

According to a particular and non-limiting example of embodiment of the invention, the control of one or more ADAS systems of a vehicle (for example the ABS system, the ESP system, a speed regulation system, a system controlling the trajectory or the route, etc.) according to a level of performance of one or more shock absorbers of the vehicle determined in real time makes it possible to adapt the driving mode of the vehicle according to the state of the shock absorbers.

For this purpose, first data representative of the dynamic behavior of the vehicle are received or collected from a set of sensors on board the vehicle. Second data representative of the environment in which the vehicle operates are also received or collected.

First information representative of a level of performance (comprising for example information on the state of wear, on a defect, on the level of damping, on an asymmetry) of a set of shock absorbers of the vehicle is determined or predicted by classification of the first and second data from a classification model learned in a so-called learning phase.

Wear or a defect in the shock absorbers results in a reduced capacity of the shock absorbers to keep the vehicle's wheels in contact with the road and/or to absorb road defects for the comfort of the vehicle's passengers.

The classification is for example implemented in a machine learning method, for example implemented with a neural network. Such a method makes it possible to estimate more reliably and in real time the level of performance of the vehicle's shock absorbers as a function of current data from the vehicle and its environment, on the basis of a model learned from the same data. a set of other vehicles.

Such an advantage is obtained by determining, during a learning phase, the correlation between the data collected from a set of vehicles and representing the actual use made of these vehicles with a set of performance levels. shock absorbers of these same vehicles.

Predicting the level of performance makes it possible to anticipate a problem to potentially correct it before it appears by controlling the ADAS system(s) controlling the vehicle, improving the prevention of the defect or a risk for the vehicle and its passengers.

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

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

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

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

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

The wireless communication system allowing the exchange of data between the first vehicle 10 and the remote device 101 corresponds for example to:

- a vehicle-to-infrastructure V2I communication system, for example based on the 3GPP LTE-V or IEEE 802.11p standards of ITS G5; Or

- a cellular network type communication system, for example an LTE type network (from the English “Long-Term Evolution” or in French “Evolution à long term”), LTE-Advanced (or in French LTE-advanced) also called LTE 3G, 4G or 5G; Or

- a WiFi type communication system according to IEEE 802.11, for example according to IEEE 802.11n or IEEE 802.11ac.

The first vehicle 10 corresponds for example to a vehicle circulating under the total supervision of a driver or circulating in an autonomous or semi-autonomous mode. The first vehicle 10 circulates according to a level of autonomy equal to 0 or according to a level of autonomy ranging from 1 to 5 for example, according to the scale defined by the American federal agency which has established 5 levels of autonomy ranging from 1 to 5, level 0 corresponding to a vehicle with no autonomy, whose driving is under the total supervision of the driver, level 1 corresponding to a vehicle with a minimum level of autonomy, whose driving is under the supervision of the driver with minimal assistance from an ADAS system, and level 5 corresponding to a completely autonomous vehicle.

The 5 levels of autonomy in the classification of the federal agency responsible for road safety are:

- level 0: no automation, the vehicle driver has full control over the main functions of the vehicle (engine, accelerator, steering, brakes);

- level 1: driver assistance, automation is active for certain vehicle functions, the driver maintaining overall control over vehicle driving; cruise control is part of this level, like other aids such as ABS (anti-lock braking system) or ESP (programmed electro-stabilizer);

- level 2: automation of combined functions, the control of at least two main functions is combined in the automation to replace the driver in certain situations; for example, adaptive cruise control combined with lane centering allows a vehicle to be classified level 2, as does automatic parking assistance;

- level 3: limited autonomous driving, the driver can cede complete control of the vehicle to the automated system which will then be in charge of critical safety functions; however, autonomous driving can only take place in certain specific environmental and traffic conditions (only on motorways for example);

- level 4: complete autonomous driving under conditions, the vehicle is designed to ensure all critical safety functions alone over a complete journey; the driver provides a destination or navigation instructions but is not required to make himself available to regain control of the vehicle;

- level 5: completely autonomous driving without driver assistance in all circumstances.

According to a particular embodiment, the first vehicle 10 circulates in a semi-autonomous or autonomous mode, that is to say with a level of autonomy greater than or equal to 2 according to the classification above.

The first vehicle 10 advantageously carries one or more ADAS systems for driving in autonomous or semi-autonomous mode. Examples of such ADAS systems are provided by way of illustration in a non-exhaustive manner below:

- anti-lock wheel system, called ABS system (from the German “Antiblockiersystem”);

- electronic stability control system, known as the ESC system (from the English “Electronic Stability Control” or in French “Electronic Stability Control”), DSC (from the English “Dynamic Stability Control” or in French “Dynamic Control”) of stability") or even ESP (from the English "Electronic Stability Program" or in French "Program electronique de lastability");

- trajectory control system such as a system to help keep the vehicle in the lane, known as the LKA system (from the English “Lane-Keeping Assist” or in French “Lane Keeping Assistant”) ;

- cruise control system such as the adaptive cruise control system, called ACC (from the English “Adaptive Cruise Control”) and/or the predictive cruise control, called the PCC system (from the English “Predictive Cruise Control”) ");

- system for controlling one or more components of the traction chain of the first vehicle 10.

A process for controlling one or more ADAS systems of the first vehicle 10 is advantageously implemented by the first vehicle 10, that is to say by a computer or a combination of computers of the on-board system of the first vehicle 10, by example by the computer(s) in charge of controlling the ADAS system(s) of the first vehicle 10 when the latter is traveling in an autonomous or semi-autonomous driving mode.

Such an ADAS system control process comprises operations of a data classification process, which is part, for example, of a process of predicting or determining a level of performance of the set of shock absorbers of the first vehicle 10.

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

The process of predicting/determining the level of performance of the set of shock absorbers of the first vehicle 10 advantageously comprises two phases, each of these phases comprising one or more operations.

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

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

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

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

Learning phase

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

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

In a first operation of the learning phase, the remote device 101 collects a set of data representative of the dynamic behavior of each second vehicle 11, these data being for example transmitted by each of the second vehicles of the set 11 progressively. measurement of the movements of the second vehicles. The data are for example transmitted by each second vehicle via the wireless network infrastructure described above to the remote device 101. The data correspond for example to part or all of the vehicle signals emitted by sensors on board in each second vehicle and/or by sensors of mobile communication devices 103 (for example a tablet, a smartphone) embedded in the second vehicles 11 and for example connected in communication with the second vehicle ( for example via Bluetooth® or Wifi®) which embeds them. These data are for example transmitted continuously at a determined frequency (for example every second, hour, day, etc.) or at each variation of a measured quantity by a sensor or an on-board computer in each second vehicle. The data reported to the remote device 101 correspond for example to the data of a list determined and stored in the memory of a computer of the on-board system of each second vehicle which makes a selection of the data to transmit among all the data or physical quantities that each second vehicle detects or measures.

The data representative of the dynamic behavior of each second vehicle 11 include for example all or part of the following data:

- data representative of lateral acceleration obtained for example from an inertial unit of the vehicle;

- data representative of longitudinal acceleration obtained for example from an inertial unit of the vehicle;

- data representative of yaw speed;

- data representative of speed measured by an odometer for example;

- data representative of steering wheel angle measured by a steering wheel angle sensor;

- data representative of vertical acceleration representative of rebound of the second vehicle and/or loss of contact of the wheel with the roadway;

- representative suspension travel data;

- data representative of noise acquired by one or more microphones on board the second vehicle or in a mobile communication device 103 present in the second vehicle.

These data are for example acquired or measured by accelerometers, microphones, gyroscopic sensors integrated into each second vehicle or integrated into a mobile communication device on board each second vehicle. These sensors make it possible to measure the movements, accelerations and variations in the trajectory of the vehicle in each direction of a three-dimensional marker associated with the vehicle, for example.

In a second operation of the learning phase, the remote device 101 collects a set of data representative of the environment in which each second vehicle 11 is traveling. These data are for example acquired by one or more sensors (for example a camera). embedded in the second vehicles, by one or more sensors arranged in the environment. According to another example, all or part of this data is received from one or more data processing or storage devices (for example a server) connected in communication to the remote device 101, for example a meteorological data server, a server map data, a road maintenance data server, etc.

The data representative of the environment in which the second vehicles 11 are traveling include for example all or part of the following data:

- data representative of meteorological conditions;

- data representative of the condition and/or level of road maintenance;

- data representative of road type;

- data representative of the presence of speed bumps or speed bumps.

In a third operation, the remote device 101 collects data representative of a level of performance of the shock absorbers of each second vehicle 11.

These data are for example obtained from one or more servers 102 associated with after-sales services provided by one or more companies ensuring the repair of vehicles 11 (car garage for example), which ensure the updating of a database. data relating to vehicles having undergone a maintenance operation (preventive or curative), in particular in relation to the shock absorber(s) of each second vehicle 11. The server(s) 102 transmit for example a set of data identifying each second vehicle for which a level performance (degree of wear, defect, level of damping, etc.) of one or more shock absorbers has been measured, detected or determined. The data set advantageously includes for each second vehicle and for each identified breakdown the date on which the measurement or maintenance was carried out. The data set also advantageously comprises an identifier of each second vehicle making it possible to determine one or more of the following information:

- the type of vehicle;

- the vehicle model;

- the brand of the vehicle;

- the hardware and software configuration of the vehicle.

The identifier of each second vehicle corresponds for example to the so-called VIN number (from the English “Vehicle Identification Number” or in French “Number of identification of vehicle”).

The collection of data obtained in the first, second and/or third operations is for example implemented over a determined time interval corresponding for example to several months or several years of use of the second vehicles, for example in real time.

These data relating to the set of second vehicles during the determined time interval are advantageously stored in the memory of the remote device 101 or of a remote device dedicated to the collection and storage of this data and connected to the remote device 101, for example via a wired connection.

According to a variant embodiment, information representative of the time instant at which each data item was measured, transmitted or received is associated with said data item. In other words, the data is timestamped.

In a fourth operation of the learning phase, the remote device 101 processes the data obtained during the first, second and third operations.

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

A performance level is, for example, also called a Key Performance Indicator, or KPI.

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

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

The classification implemented corresponds for example to a classification via a k-means partition method (from the English “k-means”). This k-means partition method is for example associated with a method called “Elbow” (or “elbow” in French) in order to group the different profiles obtained into groups or classes of different levels of damping performance.

The k-means partition method is described alongside the . The principles of such a method associated with the “Elbow” method are known to those skilled in the art and for example described in the document entitled “Determining the appropriate cluster number using elbow method for K-means algorithm”, published by H. Humaira and R. Rasyidah in January 2018.

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

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

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

Such a learning phase thus makes it possible to cross-reference data from (or associated with) second vehicles with data obtained from after-sales services (data on breakdowns / defects / wear level / damping performance levels) encountered by the shock absorbers of these second vehicles).

Prediction phase

In a first operation of the prediction phase, the device in charge of the prediction phase (that is to say the remote device 101 or one or more computers of the on-board system of the first vehicle 10) collects a set of first data representative of the dynamic behavior of the first vehicle 10. These first data correspond for example to the data representative of the dynamic behavior of the second vehicles having been used in the learning phase.

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

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

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

In a second operation of the prediction phase, the device in charge of the prediction phase (that is to say the remote device 101 or one or more computers of the on-board system of the first vehicle 10) collects a set of second data representative of the environment in which the first vehicle 10 circulates or evolves. These second data correspond for example to the environmental data obtained for the second vehicles 11, in a similar or identical manner, which were used in the learning phase.

The collection of the first data and the second data is thus similar or identical to the collection of the dynamic behavior data of the second vehicles 11 and the environmental data of the second vehicles 11 described with regard to the learning phase.

In a third operation of the prediction phase, the first and second data are used to feed the prediction/classification model(s) learned or trained during the learning phase. From this or these models comes information representative of a prediction of the level of damping performance of each shock absorber or of the set of shock absorbers of the first vehicle 10.

The determination of the performance level of the shock absorbers of the first vehicle 10 is for example implemented using the so-called k-means method, which is for example implemented by a neural network, for example of the LSTM type. The LSTM type neural network makes it possible to predict the level of performance of the shock absorber(s) of the first vehicle in real time from the first and second data collected.

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

In a fourth operation, the ADAS system(s) of the first vehicle 10 are controlled according to the level of performance obtained in the fourth operation.

Control of these ADAS systems makes it possible to adapt the driving mode of the first vehicle 10 and/or its route according to the determined level of performance.

For example, when the level of damping is minimal, a route favoring major roads (motorways and expressways) will be favored to avoid roads with poorer quality pavements, to avoid roads with speed bumps or speed bumps. (roads in towns for example).

According to another example, the ABS system and/or the ESP system will be adapted, for example by switching to a so-called sport mode for which the damping is harder when the level of damping performance is degraded.

According to yet another example, the pace of the first vehicle will be adapted to reduce speed and limit acceleration/deceleration when the level of damping performance is degraded. This is for example obtained by controlling the speed regulation system of the first vehicle 10.

Optionally, when a performance level is obtained for each shock absorber of the first vehicle 10, the performance levels of each shock absorber are compared with each other to detect a possible asymmetry between 2 shock absorbers, for example between the left front shock absorber and the Right front shock absorber.

According to another optional variant, an alert message is for example generated according to the determined performance level. For example, when the performance level is below a determined threshold, an alert message is generated and rendered in the passenger compartment of the first vehicle 10, for example for display on a display screen of the first vehicle 10.

Such a message makes it possible to alert the driver of the first vehicle 10 so that the latter can, for example, take the necessary steps to have the shock absorbers repaired/changed.

There schematically illustrates a grouping of dynamic behavior data and environmental data into a predetermined number K of groups according to a particular and non-limiting embodiment of the present invention.

According to this exemplary embodiment, a so-called “k-means” method is used.

The “k-means” method groups data sets (represented by squares) into k (here k=3) groups called “clusters” in English. For this, each set is represented in a multidimensional space (here in a two-dimensional space). Each GRi group is represented in this space by a centroid Ci (here an average) whose initial value is a random value. On the , each centroid Ci is represented by a circle 201, 202, 203. For each added data set, a distance Di is calculated between a point in space representing this set and each centroid Ci. The data set is then added to the group GRi whose distance Di is minimum. The centroid Ci of the group Ci in which the data set has just been added is then updated taking into account the added data set.

Alternatively, multiple clusters of data sets are calculated for different K numbers and one of these clusters is kept.

For example, the preserved grouping is one that minimizes standard deviations between the data sets of the groups of this grouping and the centroid associated with these groups.

There schematically illustrates a cell, denoted 'c', with short-term and long-term memory according to a particular and non-limiting exemplary embodiment of the present invention.

The idea associated with a neural network with short-term and long-term memory, called an LSTM network, is that each cell is linked to two internal states; one says hidden state ( ) and the other says cell state ( ) which plays the role of memory. The transition from an entry state from a cell at a time instant (t-1) to an output state at a following time instant t is done by transfer at constant gain and equal to 1. In this way the errors propagate to the previous steps without the phenomenon of gradient disappearance. Cell state can be changed through an input gate which authorizes or blocks the update. The same for a control door if the cell state is communicated at the cell output. Cells can also use a door allowing the cell state to be reset to zero.

According to an example, considering the following initial values: And , and considering an activation function of these sigmoid type cells and hyperbolic tangent , the states of the reset, output and update gates as well as the hidden, cell and output states are given by:

in which o is an operator symbolizing the Hadamard matrix product (term to term product) and , , , are weights (parameters) and are offset values (parameters).

Alternatively, other functions can be used.

During the learning phase, the neural network learns to produce a prediction of the level of damping performance from a set of data (first and second data) collected.

A classic neural network learning algorithm is then used for learning. The principle of this learning algorithm is that the neural network learns the values of the parameters of the cells by backpropagation of the error gradient, that is to say that the algorithm progressively calculates the values of the parameters so as to minimize a cost function for each of the short-term and long-term memory cells based on the outputs of the neural network.

According to a particular and non-limiting embodiment, the cost function is a precision function given by:

According to a particular and non-limiting embodiment, the cost function is a function of type F1 given by:

This cost function can be minimized using a classical gradient descent algorithm such as the ADAM supervised gradient descent algorithm ( Kingma & Ba (2015) Adam: A Method for Stochastic Optimization. Proceedings of the International Conference for Learning Representations , San Diego (ICLR) ).

During the production phase, the neural network is identical to the neural network in the learning phase, except that the input of the neural network is no longer fed by data from the second vehicles 11 but by the first and second data relating to the first vehicle 10 and its environment. The neural network is then capable of reproducing a prediction of the performance level of the shock absorbers which corresponds to a performance level associated with the group GRi to which the set of first and second data belongs.

According to one variant, the LSTM type neural network comprises a single cell.

Alternatively, the LSTM type neural network comprises several cells.

According to a particular and non-limiting embodiment, the neural network is stored in a memory or a database of the first vehicle 10.

There schematically illustrates a device 4 configured to control one or more ADAS systems of a vehicle, for example the first vehicle 10, according to a particular and non-limiting embodiment of the present invention.

According to another exemplary embodiment, the device 4 is configured to determine or predict a performance level of the set of shock absorbers of a vehicle, for example of the first vehicle 10.

Such a device 4 is configured for the implementation of the learning phase and the production phase of a machine learning method.

According to yet another example, the device 4 is both configured to control one or more ADAS systems of a vehicle and to determine or predict a level of performance of the set of shock absorbers of this vehicle.

The device 4 is for example configured for the implementation of the operations described with regard to Figures 1, 2, and 3 and/or the steps of the method described with regard to the . Examples of such a device 4 include, without being limited to, a computer, a laptop, a server, a mobile phone, a tablet or even on-board electronic equipment such as an on-board computer of a vehicle or a electronic calculator such as an ECU (“Electronic Control Unit. The elements of 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.

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.

The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored on memory 41.

According to a particular and non-limiting example of embodiment, the device 4 comprises a block 42 of interface elements for communicating with external devices, for example a remote server or the “cloud”, other nodes of the ad hoc network. The interface elements of block 42 include one or more of the following interfaces:

- RF radio frequency interface, for example of the Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or of the Bluetooth® type (according to IEEE 802.15.1), in the band frequency at 2.4 GHz, or Sigfox type using UBN radio technology (from English Ultra Narrow Band, in French ultra narrow band), or LoRa in the 868 MHz frequency band, LTE (from English “ Long-Term Evolution” or in French “Long-Term Evolution”), LTE-Advanced (or in French LTE-advanced);

- USB interface (from the English “Universal Serial Bus” or “Bus Universel en Série” in French);

- HDMI interface (from the English “High Definition Multimedia Interface”, or “Interface Multimedia Haute Definition” in French);

- LIN interface (from English “Local Interconnect Network”, or in French “Réseau interconnecté local”).

According to another particular and non-limiting example of 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 CAN type wired network (from the English “Controller Area Network” or in French “Réseau de controlleres”), CAN FD (from the English “Controller Area Network Flexible Data-Rate” or in French “Réseau de controllers à flow flexible data”), FlexRay ( standardized by the ISO 17458 standard) or Ethernet (standardized by the ISO/IEC 802-3 standard).

According to a particular and non-limiting embodiment, the device 4 can provide output signals to one or more external devices, such as a display screen, touch or not, one or more speakers and/or other devices (projection system) via respective output interfaces.

There illustrates a flowchart of the different stages of a method of controlling one or more ADAS systems of a vehicle, for example of the first vehicle 10, according to a particular and non-limiting embodiment of the present invention. The method is for example implemented by a device on board the vehicle, for example by device 4 of the , or by a system comprising the on-board device connected in wireless communication to a remote server or computer type device.

In a first step 51, first data representative of dynamic behavior of the vehicle are received from a set of sensors on board the vehicle, the set of sensors comprising at least one sensor.

In a second step 52, second data representative of an environment in which the vehicle is traveling are received.

In a third step 53, first information representative of a performance level of a set of shock absorbers of the vehicle is determined by classification of the first and second data from a classification model learned in a so-called learning phase. prior to receiving the first and second data, the performance level being associated with a class of a set of classes output from the classification model.

In a fourth step 54, at least one ADAS system is controlled according to the first information.

According to a variant, the variants and examples of the operations described in relation to Figures 1, 2 and 3 apply to the steps of the process of the .

Of course, the present invention is not limited to the exemplary embodiments described above but extends to a method for predicting a level of damping performance of a set of vehicle shock absorbers, which would include steps secondary, without departing from the scope of the present invention. The same would apply to a device configured to implement such a process.

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

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

Claims (10)

Method for controlling at least one driving assistance system, called the ADAS system, of a vehicle (10), said method being implemented by at least one processor and comprising the following steps:
- reception (51) of first data representative of dynamic behavior of said vehicle (10) from a set of sensors on board said vehicle (10), said set comprising at least one sensor;
- reception (52) of second data representative of an environment (1) in which said vehicle (10) is traveling;
- determination (53) of first information representative of a performance level of a set of shock absorbers of said vehicle (10) by classification of said first and second data from a classification model learned in a so-called learning phase prior to said reception of the first and second data, said performance level being associated with a class of a set of classes output from said classification model;
- control (54) of said at least one ADAS system based on said first information. Method according to claim 1, for which said first information comprises second information representative of a performance level of each shock absorber of said set of shock absorbers, said method further comprising the steps of:
- comparison of said second information with each other;
- determination of third information representative of asymmetry between at least two shock absorbers of said set of shock absorbers according to a result of said comparison. Method according to claim 2, further comprising a step of rendering a message representative of said level of performance and/or said asymmetry in a passenger compartment of said vehicle (10). Method according to one of claims 1 to 3, for which said at least one ADAS system belongs to a set of ADAS systems comprising:
- an electronic stability control system, called ESC system;
- an anti-lock wheel system, called ABS system;
- a speed control system;
- a trajectory control system;
- a route control system;
- a system for controlling at least one member of a traction chain of said vehicle. Method according to one of claims 1 to 4, for which said first data belongs to a set of data comprising:
- representative lateral acceleration data;
- data representative of longitudinal acceleration;
- data representative of yaw speed;
- representative speed data;
- representative steering wheel angle data;
- data representative of vertical acceleration;
- representative suspension travel data;
- data representative of noise,
and for which said second data belongs to a set of data comprising:
- data representative of meteorological conditions;
- data representative of the condition and/or level of road maintenance;
- data representative of road type;
- data representative of the presence of speed bumps or speed bumps. Method according to one of claims 1 to 5, for which said classification is implemented via a k-means partition method implemented by a neural network. Method according to claim 6, for which said neural network is a network of the recurrent network type with short and long term memory, called LSTM network. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor. Device (4) for controlling at least one driving assistance system, called the ADAS system, of a vehicle, said device (4) comprising a memory (41) associated with at least one processor (40) configured to the implementation of the steps of the method according to any one of claims 1 to 7. Vehicle (10) comprising the device (4) according to claim 9.