Method for detecting the type of road traveled by an equipped vehicle

Abstract

Title: Method for detecting the type of road traveled by an equipped vehicle. This method combines a phase for determining the road type and a phase for learning to identify a class of road type, incorporating an evaluation of the road comfort experienced by a user for a vehicle of a given model and equipment. No figure is provided for the abstract.

Description

Title of the invention: Method for detecting a type of road traveled by an equipped vehicle Technical field of the invention

[0001] The invention relates to a method for detecting, in particular intelligently detecting, a type of road traveled intended for a vehicle, in particular a vehicle in a driving situation. The invention also relates to a system for detecting a road traveled. The invention also relates to a vehicle equipped with such a system and to a computer program product for implementing the steps of the detection method. State of the prior art

[0002] In the automotive industry, it is known to equip vehicles with controlled suspension systems. Conventionally, such systems require knowledge of the state and / or characteristics of a road traveled by an equipped vehicle in order to best control the system according to the characteristics specific to the road traveled.

[0003] To this end, it is possible to execute methods for detecting bad roads and / or obstacles. Such methods depend on detection systems performing explicit calculations of variables and intermediate characteristics, or "features" in English. It is therefore necessary to carry out fine-tuned adjustment and parameterization of the detection system in order to obtain an acceptable level of accuracy. Another disadvantage of such detection systems lies in the fact that they are not suitable for taking into consideration, for a type of bad road or obstacle detected, the variability of the driver's feeling or comfort from one type, or model, of vehicle to another, in particular due to their different equipment.As a result, a road detected as being bad for a certain vehicle will also be considered to be bad, wrongly, for a separate vehicle more suited to traveling on such a type of road, for example due to particular equipment such as more suitable suspension systems. Conversely, a road identified as being good, or passable, could be experienced as being bad and uncomfortable depending on the type of vehicle used. Such methods thus have a portability defect making them unsuitable for application to a set of different vehicles.

[0004] The present invention falls within this context and aims to propose a detection method suitable for a wide range of vehicles having distinct characteristics, for example private vehicles, utility vehicles or others, and at lower cost.

[0005] The present invention relates to a method for detecting a type of road traveled by a vehicle, in particular a vehicle equipped with a variable suspension system, the method being characterized in that it comprises a phase of determining a type of road traveled comprising: - at least one step of acquisition, in real time or for a predetermined duration, of data relating to the vehicle and / or to a route traveled by the vehicle implemented by means of a detection module comprising at least one sensor and / or at least one detection means, such as a camera; - a step of estimating a probability rate of belonging of the road traveled to at least one class of road type, in particular bad roads, predefined according to said acquired data; - a step of classifying the road traveled into one of the road type classes based on the estimated membership probability rates and extracting at least one level of comfort felt by a user previously associated with each road type class based on at least one piece of equipment specific to the vehicle (10) capable of influencing the comfort felt by a user during the driving phase.

[0006] In particular, the method is an intelligent detection method.

[0007] In particular, the method according to the invention may comprise, prior to the execution of the determination phase, at least one supervised learning phase, specific to the equipped vehicle, of the identification of at least one predefined road type class and of the association of at least one level of comfort felt by a user with each road type class as a function of at least one piece of equipment specific to the vehicle capable of influencing the comfort felt by a user during the driving phase, the learning phase being implemented by means of a computer, a human-machine interface and / or a memory element.

[0008] Furthermore, the method may comprise a step of adjusting at least one ancillary system equipping the vehicle, in particular a variable suspension system, so as to correct a level of comfort felt by the user, such a step being implemented when the level of comfort associated with a road type class determined during the determination phase is lower than a predefined adapted comfort level threshold and / or when the determined road type class is included in a predefined class subset identified as requiring the execution of an adjustment step.

[0009] The method may, in addition, comprise a step of updating a map equipping the vehicle so as to provide information on a determined road type class.

[0010] According to an exemplary execution of the method according to the invention, the classification step can be implemented only when the estimated probability rate is greater than a predetermined threshold of 60%.

[0011] According to an example of execution of the method, the estimation step can be executed by a network capable of processing time series of data and of being trained during a learning phase, in particular by a convolutional neural network.

[0012] The data acquisition step can be implemented by: - detection and / or estimation of at least one characteristic relating to a portion of the road traveled located upstream of the vehicle, in particular a height of a road profile; and / or - measurement by at least one sensor of at least one characteristic relating to the vehicle while driving on the said road traveled, in particular suspension travel, wheel speed, longitudinal speed of the vehicle and / or acceleration of the vehicle.

[0013] Also, the determination phase may comprise, subsequent to the estimation step, a step of validating the estimated probability rate, the validation step being carried out on the basis of measurements, by at least one sensor, of at least one characteristic relating to the vehicle in a driving situation on said road traveled, in particular a suspension travel, a wheel speed, a longitudinal speed of the vehicle and / or an acceleration of the vehicle.

[0014] The present invention also relates to a system for detecting a type of road traveled for a vehicle in a driving situation. The system comprises hardware and / or software elements implementing the method described above, the hardware elements comprising at least one data processing unit, a module for detecting at least one characteristic relating to a road traveled and a module for controlling at least one ancillary system equipping the vehicle, in particular a variable suspension system. In particular, the detection system may be an intelligent detection system.

[0015] The invention further relates to a motor vehicle comprising at least one ancillary system capable of affecting a level of comfort felt by a user, such as a variable suspension system, and a detection system according to the invention, capable of controlling said ancillary system.

[0016] The invention may also be extended to a learning method for a detection method according to the invention or for a detection system according to the invention, comprising at least one supervised learning phase, specific to the equipped vehicle, of the identification of at least one predefined road type class and of the association of at least one level of comfort felt by a user with each road type class as a function of at least one piece of equipment specific to the vehicle capable of influencing the comfort felt by a user during the driving phase, the learning phase being implemented by means of a computer, a human-machine interface and / or a memory element.

[0017] The invention further extends to a computer program product comprising program code instructions recorded on a computer-readable medium for implementing the steps of the method according to the invention when said program operates on a computer. Alternatively, the invention may extend to a computer program product downloadable from a communications network and / or recorded on a computer-readable data medium.

[0018] Also, the invention relates to a computer-readable data recording medium on which is recorded a computer program comprising program code instructions for implementing the detection method according to the invention or a computer-readable recording medium comprising instructions which, when executed by a computer, cause the latter to implement said detection method.

[0019] The invention finally relates to a signal of a data medium, carrying the computer program product according to the invention.

[0020] Other details, characteristics and advantages will emerge more clearly on reading the detailed description given below, for informational and non-limiting purposes, in relation to the various exemplary embodiments illustrated in the following figures:

[0021] [Fig.l] is a schematic representation of an embodiment of a detection system according to the invention.

[0022] [Fig.2] is a general flowchart of an example of execution of a method for detecting a road type.

[0023] [Fig.3] is a schematic representation of an example of execution of a data acquisition step.

[0024] [Fig.4] is a flowchart detailing a second example of execution of the method according to the invention. Detailed description

[0025] [Fig. 1] schematically illustrates an exemplary embodiment of a detection system 1, in particular intelligent detection, of a type of road traveled by a vehicle 10. Such a system is intended for a vehicle, in particular a motor vehicle. The vehicle 10 may be, for example, a private vehicle, a utility vehicle, a truck or a bus. In particular, the vehicle considered may be a connected and / or autonomous vehicle. Figures 2 to 4 illustrate examples and variants of execution of a detection method, in particular intelligent detection, of a type of road traveled by such a vehicle 10. The detection method may also be considered as being a method of operation of the detection system 1 according to the invention or as a method of operation of the motor vehicle 10 equipped with such a detection system 1.

[0026] Here, the term “road traveled” means a road or a section of a road, a traffic lane or a roadway on which the vehicle 10 is traveling in real time.

[0027] The vehicle 10 comprises the detection system 1, which comprises hardware and / or software elements capable of implementing, or designed to implement, the detection method according to the invention, as further explained below.

[0028] Said hardware elements comprise at least one data processing unit 2, a detection module 3 of at least one characteristic relating to a road traveled and a control module 4 of at least one ancillary system 5 equipping the vehicle 10, such as a variable suspension system 51, a driving assistance system 52 or a longitudinal speed management system 53.

[0029] The processing unit 2 comprises at least one computer 21 comprising hardware and software resources, more precisely at least one processor, or microprocessor, cooperating with memory elements 6 of the detection system 1 and / or of the vehicle 10. This computer 21 is capable of executing instructions for the implementation of a computer program. In particular, the processing unit 2 can provide at least one other function within the vehicle 10. For example, the computer 21 can be the on-board computer of the vehicle 10, or, generally speaking, any computer 21 embedded in the vehicle 10 permanently or temporarily.

[0030] The detection module 3 may in particular be configured to detect and / or estimate at least one characteristic relating to a portion of the road traveled located upstream of the vehicle 10, such as a height of a road profile. Here, the term “height of road profile” means a height of a point of the road relative to the vehicle. In particular, the at least one characteristic considered may be detected and / or estimated on the basis of images of said portion of road.

[0031] For example, the detection module 3 may comprise at least one optical detection means 31, such as a camera, a scanning laser or a Lidar, capable of providing signals representative of an image of the road traveled which can then be transmitted to the processing unit 2 in order to process and / or archive them. Alternatively or in combination, the detection means 31 may be phonic, for example by ultrasound. Such a detection means 31 may, preferably, be equipped at the front of the vehicle 10 or on a roof of the vehicle 10 in order to acquire data relating to a portion of road arranged upstream of the vehicle 10.

[0032] Alternatively or additionally, the detection module 3 may comprise at least one sensor 32 equipped on the vehicle 10 and capable of measuring at least one characteristic specific to the driving of the vehicle 10, for example relating to the vehicle or to an assembly formed by the vehicle and the road traveled. Such a characteristic may, in a non-limiting manner, be related to suspension travel, wheel rotation speed and / or longitudinal speed of the vehicle.

[0033] The control module 4 is configured to control at least one ancillary system 5 equipping the vehicle 10. Such an ancillary system 5 may be a variable suspension system 51, a driving assistance system 52, a longitudinal speed management system and / or a mapping system, such as a location means 7 equipping the vehicle 10. The control module 4 is in particular capable of executing the switching on, switching off, modification and / or adjustment of all or part of the parameters defined by the at least one ancillary system 5 considered. Advantageously, the control module 4 may be configured so as to control a plurality of ancillary systems equipping the vehicle 10.

[0034] Optionally, the detection system according to the invention may, in addition, comprise all or part of the following options: - a location means 7; - a communication module 8.

[0035] The location means 7 may in particular allow the location of the vehicle 10. It integrates, for example, a system for approximate location of the vehicle 10 and / or a high-definition map of the road infrastructure. In particular, the approximate location of the vehicle 10 may be provided by a GPS type system, from the English acronym “Global Positioning System” allowing the extraction, from a mapping database, of information concerning the road infrastructure around the approximate position of the vehicle 10. Alternatively or in addition, the location means may be a location system on board the vehicle 10.

[0036] The communication module 8 allows in particular the vehicle 10 to transmit and / or receive data from a database, a remote server, one or more connected devices such as a mobile telephone, or a vehicle itself equipped with a communication module 8 and a system according to the invention. Such communication is established via a low-frequency or high-frequency wireless link. It may, for example, be a wireless link based on “cellular” or “Wifi” or “Bluetooth” technologies. As further explained below, the communication module 8 may be implemented in order to extract data relating to the external environment, in particular weather or road traffic conditions, or in order to transmit information relating to the route traveled in order to update mapping data.

[0037] The detection system 1 may, in addition, comprise a memory element 6 or a storage space constituting a recording medium readable by a computer or by the calculator 21 of the processing unit 2 comprising instructions which, when executed by the computer or calculator 21, cause the latter to implement the method as described below.

[0038] An embodiment of the method 100 for detecting a type of road traveled by a vehicle 10 in a driving situation is described below with reference to FIGS. 2 to 4. In general, the detection method 100 according to the invention comprises a phase PI for determining a type of road traveled by the vehicle and, prior to the execution of the phase PI for determining, at least one supervised learning phase P2, specific to the equipped vehicle 10, associating in particular a level of comfort felt by a user with different classes of road type, in particular bad roads. Such a learning phase P2 can also be defined as relating to a training method for a detection method and / or a detection system according to the invention.

[0039] Throughout the description below, a road type, or a road type class, may, in a non-limiting manner, relate to: - one or more types of bad roads likely to cause pumping movements of the body when the vehicle 10 is moving on a road whose spectral amplitude density of vertical stresses is maximum at low frequencies; - one or more types of bad roads likely to generate wheel jolts or bounces when the vehicle 10 is moving on a road whose spectral amplitude density of vertical stresses is maximum at high frequencies; - paved roads or mountain paths; - roads with poor quality surfaces, for example due to adverse weather conditions such as precipitation, frost, or wear; - roads comprising obstacles, such as speed bumps, level crossings, potholes or bumps, depending on their impact on comfort, for example depending on their dimensions, likely to cause vibrations or shocks in the vehicle 10, in particular having a high frequency of said obstacles, i.e. a degraded road with a short distance between obstacles.

[0040] The determination phase PI comprises at least one acquisition step E1, in real time or for a predetermined duration, of data relating to a route traveled by the vehicle 10. According to an exemplary embodiment not detailed in [Fig. 2], the determination phase PI may comprise a plurality of acquisition steps, relating to similar or different types of data. For example, said data may relate to one or more characteristics relating to the route traveled and / or to the vehicle. Alternatively, the acquisition step E1 may comprise the acquisition of different distinct types of data. The acquisition step It is implemented via the detection module 3 as previously explained.

[0041] In order to carry out the classification of the road traveled into at least one predetermined road type class, such as a bad road type, the acquisition step E1 can be carried out so as to comprise a sub-step of detection and / or estimation E11 of at least one characteristic relating to a portion of the road traveled located upstream of the vehicle 10 and / or a sub-step E12 of measurement of at least one characteristic relating to the vehicle 10 in a driving situation on said road traveled. In other words, the acquisition step E1 can implement the acquisition of data so as to anticipate characteristics relating to a future portion of road included in the road traveled and / or so as to react to a portion of road on which the vehicle 10 is moving, that is to say in contact with which at least one of the wheels of the vehicle 10 is in contact.

[0042] The detected and / or estimated data E1 1 based on the portion of the road traveled located upstream of the vehicle 10 may, for example, relate to characteristics such as a height of a road profile relative to the vehicle, in particular to a wheel of the vehicle, or any other information originating from images of the road. For example, such data may be obtained via an optical detection means, such as a camera.

[0043] Optionally but preferably, the detected and / or estimated data E1 1 based on the portion of road located upstream of the vehicle 10 may be associated with a confidence index a representative of a degree of reliability of the detection and / or estimation carried out. In particular, such a confidence index a may be defined on the basis of detected weather or road traffic conditions and / or as a function of the type of road surface, likely to affect the detection and / or estimation. For example, the confidence index a is defined according to whether the road is paved, covered with snow, frost, leaves or sand. Advantageously, the use of a confidence index makes it possible to discard data defined as unreliable, likely to bias the classification of the type of road, for example by discarding data having a confidence index lower than a predefined threshold amin.For example, the confidence index can be defined by the processing unit 2 and / or by the detection module 3.

[0044] The measured data E12 may, in a non-limiting manner, be obtained by at least one sensor 32 and relate to characteristics such as the travel of the suspensions or of a wheel, for example of the center of a wheel, a wheel speed, a longitudinal speed of the vehicle 10 and / or an acceleration of the vehicle 10. The at least one sensor 32 may be included in the detection module 3. Alternatively, the sensor 32 may be an on-board sensor 32 specific to one of the ancillary systems 5 equipping the vehicle 10 and configured to transmit data to said detection module 3. Such sensors 32 may comprise an accelerometer, a travel sensor, a deformation sensor associated with one of the wheels and / or a pressure sensor.

[0045] Preferably, the method according to the invention is at least carried out in anticipation, by estimation and / or detection El 1 of data relating to the portion of road located upstream of the vehicle 10. As further explained below, with reference to [Fig. 4], the implementation of a measurement sub-step, in addition to a detection and / or estimation sub-step Eli, can make it possible to improve the detection and classification of the road traveled by means of a validation step.

[0046] Regardless of the type of acquisition carried out, i.e. in anticipation or in reaction, the detection and / or estimation E11 of data on the one hand and the measurement of data E12 on the other hand are each carried out at least one point on the road. Preferably, the data acquisition is carried out at least two points, for example a point arranged on two lines representative of a virtual trajectory of the front wheels of the vehicle.

[0047] [Fig.3] illustrates a particular example of implementation of the acquisition step El in which the data acquisition is carried out in anticipation by means of an optical detection means so as to allow an estimation of the heights of the profile of the portion of road located upstream of the vehicle 10 by image processing. In the example illustrated, in a non-limiting manner, the data are collected at a plurality of points arranged in front of each of the front wheels of the vehicle 10. In this case, the data are collected, for each front wheel, at ten points. Preferably, for each front wheel, said points have a spacing at a fixed frequency so that the data acquisition is not negatively affected by the longitudinal speed of the vehicle 10. The data relating to each of the twenty measurement points, ten for each front wheel, are transmitted to the processing unit 2.

[0048] For each of the aforementioned points, the estimated value of the height of the profile of the portion of road is associated with a confidence index ax of said estimation. As explained above, such an index can be defined by the detection module 3 and / or the processing unit 2. This results in a set of data comprising forty incoming signals which represent the heights and the confidence indices ax of the different points of the portion of road located upstream of the two front wheels of the vehicle 10.

[0049] Such a principle applies mutatis mutandis to the “reaction” operation explained above, according to which the data are directly obtained by measurements E12 representative of a set formed by the route traveled and vehicle 10, with the difference that no confidence index is then included.

[0050] The processing unit 2 then executes a step E2 of estimating at least one probability rate of belonging of the road traveled to at least one predefined road type class, in particular bad road, as a function of the acquired data. As explained previously, when a confidence index is associated with the acquired data, such a probability rate is also defined on their basis.

[0051] The estimation of the membership rates can be carried out by any type of network capable of carrying out a classification task from time series of data including beforehand a supervised P2 learning phase. The network considered receives as input the data acquired during the acquisition phase, and provides as output estimates of the membership rates of the road traveled to the different predefined road type classes corresponding to a probability rate that the road traveled is similar, or of the same type, as a road type class considered.In other words, such a type of network is previously trained during a supervised learning phase P2, as further detailed below, in order to carry out the identification of different classes of road types on the basis of different types of data and / or ranges of data, and can then subsequently be executed by the processing unit 2 during the implementation of the method in order to carry out the classification of the road traveled into at least one of the classes identified during the learning phase.

[0052] According to an exemplary embodiment, the probability rates of belonging to different classes of road types can be calculated by a neural network, in particular by a convolutional neural network, or CNN from the English “Convolution Neural Network”. This type of model can be used to perform the supervised classification of time series of data. Such a principle is advantageously simple to implement and allows invariance of the processing by temporal translation. According to another exemplary embodiment, non-limiting, the probability rates of belonging to different classes of road types can be calculated by a recurrent neural network, or RNN from the English “Recurrent Neural Network”.

[0053] Based on the obtained membership probability rates, the processing unit 2 implements a step E3 of classification of the road traveled in a road type class and extraction of at least one level of comfort felt by a user previously associated, for example during at least one supervised learning phase P2 as set out below, with each road type class as a function of at least one piece of equipment specific to the vehicle 10 capable of influencing the comfort felt by a user during the driving phase. According to non-limiting examples, the term “equipment” means equipment likely to affect the level of comfort felt by a user such as a suspension system or tires.

[0054] For example, the road traveled is classified into a predefined road type class on the basis of the highest estimated probability rate obtained. In particular, the classification step E3 can be implemented only when the estimated probability rate is greater than a predetermined threshold of 50%, or even 60% or even 75% in order to ensure the accuracy of the classification. The processing unit 2 then extracts at least one level of comfort felt by a user associated with the detected road type class, i.e. previously defined, by calculation implemented by the processing unit 2 or by manual intervention of a user or technician, and recorded on the memory elements 6 of the detection system 1 and / or the vehicle 10.

[0055] According to a non-limiting example of execution, implemented for a vehicle 10 in a driving situation as illustrated in [Fig. 3], at each calculation step the processing unit 2 forms, as input to the network, in particular the neural network, a sliding window of data. In this case, such a window comprises the last forty values ​​relating to the heights of road profiles and confidence indices. The neural network executes an inference in order to obtain a distribution of probabilities on the different classes of roads, in particular bad roads, then the type of road is determined by selecting the class having the highest probability rate.

[0056] The network used to extract features, in other words characteristics, relating to the classes of road types on the basis of the time series of acquired raw data and to define probability rates can be defined, in a non-limiting manner, by a basic block comprising: - a 1D Convolution layer, or filter; then - a ReLU layer, for Rectified Linear Units, corresponding to a correction layer including the ReLU activation function; then - a MaxPooling layer, corresponding to a pooling layer; then - a batch normalization layer, or “Batch Normalization”.

[0057] Such a block may be repeated several times, for example twice, in order to obtain a succession of three blocks. According to a non-limiting exemplary embodiment, these three basic blocks may be assembled with different filter sizes and used as input for a fully connected layer so as to give a distribution of probabilities of belonging to a road type class as output. For example, the filter sizes used in the convolution layers may be defined as follows during the successive execution of the three basic blocks: 128, 256, 128. Similarly, the kernel sizes of the convolutions can be as follows: 5, 3, 3. For the execution of the convolutions of each of the blocks, the padding applied to the input allows the output to keep the same time dimension and the step used is preferably 1.

[0058] Following the implementation of the basic blocks, a softmax layer can be implemented. It provides a probability rate of membership of the route traveled for each of the possible classes, the sum of these probability rates being 1. Conventionally, the softmax function takes as input a vector of dimension K: z=(zi.. .zK) and is defined according to:

[0059] softwaxfy. = ..... 7

[0060] In the present exemplary embodiment, the network, in particular the convolutional neural network, receives as input a matrix of size 40x40 containing a time window containing 40 time steps of the 40 data signals described previously, relating to the profile height of the portion of road located upstream of the vehicle 10 and to the confidence indices. The network provides, as output, a distribution of the probability rates of belonging of the road to each of the classes of possible road types predefined during the learning phase P2. The classification step E3 as explained above is then executed, for example so that the final classification of the road traveled corresponds to the class for which the probability rate obtained at the output of the network is maximum.

[0061] The training of the network, for example the convolutional neural network, is carried out, as previously explained during at least one supervised learning phase P2, at least implemented prior to the PI determination phase as previously explained. By “supervised” is meant that such a learning phase P2 requires, at least partially, human intervention.

[0062] The learning phase P2 aims on the one hand to train the network, in other words the model, so that it is able to enable the identification of a predefined road type class corresponding to a road traveled when the PI determination phase is executed, and, on the other hand, to train the model so that it is able to associate with the different road type classes a level of comfort felt by the user.

[0063] The learning phase P2 can be implemented via a computer 21, a human-machine interface and / or a memory element. Once trained, the model can easily be implemented in a computer 21 of the vehicle 10, for example in the processing unit 2, during the implementation of the PL determination phase.

[0064] Training the network to identify a type of road includes a so-called “inference” phase, carried out according to a principle similar to that set out in reference to the determination phase PI, from data for which the road type class is previously known, for example by manual, i.e. supervised, labeling of a learning database, also the previous description applies mutatis mutandis. To constitute the learning base, data records relating to different types of roads are made, on the basis of one or more types of data relating to characteristics specific to the road traveled and / or to the vehicle, as explained previously with reference to the acquisition step E1.

[0065] These records, for which the desired result of the processing is not the model, are submitted to the model in order to train it to identify, that is to say to execute the estimation and classification step as previously set out, a type of road on the basis of detected and / or estimated data, that is to say in anticipation, and / or on the basis of measured data, that is to say in reaction. The different classes of road types are thus defined as a function of at least one type of data, in particular as a function of ranges of values ​​defined for at least one type of data considered, for example the height of the road profile. Then, as a function of the class or the distribution of the probability rates of belonging of the road to each of the classes of possible predefined road types determined by the model, an estimate of the error made by the model is calculated.

[0066] In the case of networks, in particular convolutional neural networks, a loss or cost function, for example a cross-entropy function, may be used. Optionally but preferably, a weighting may be applied to such a loss function in order to prevent overfitting, or in order to allow artificially accelerated learning if the recordings of the different types of roads are not equally distributed in terms of the number of examples per class.

[0067] The training of the network also aims to identify and / or associate at least one estimated comfort level, representative of the user's comfort and the feeling of the road traveled, with the different classes of road types. Such training can be carried out on the basis of at least one criterion, which can be objective or subjective. Advantageously, the training can be carried out on the basis of a plurality of objective and / or subjective criteria. The different classes of road types can be associated with at least one criterion, in particular with at least one range of values ​​defined for the at least one criterion considered.

[0068] For example, the term “objective criterion” means criteria defined on the basis of values ​​measured, estimated and / or calculated by detection means and / or sensors 32 on board the vehicle 10. For example, an objective criterion may relate to transmissibility, i.e. the ratio between the displacement of the suspended mass in relation to the road stress which makes it possible to evaluate the vibration isolation characteristic of a given suspension. A criterion can also be defined based on the wheel travel in relation to the body when passing through disturbances or obstacles, and in particular the reaching of compression or rebound stops which strongly penalize comfort, or a criterion on the effective value, or RMS, of the vertical acceleration at the center of gravity of the car.

[0069] When the training is based on objective criteria, it can advantageously be executed automatically and is defined specifically according to the equipment specific to the vehicle 10, or to a vehicle model 10, so that the evaluation of the comfort felt associated with the type of road identified is defined in an adapted manner relative to the vehicle 10 equipped with the detection system and its equipment, such as its suspension system, and not in a generic manner.

[0070] Conversely, by "subjective criterion" we mean that the learning is carried out in a supervised manner by a user or by an expert who can, for example, himself associate a predefined level of comfort with each of the different classes of road types depending on the equipment of the vehicle, such as the suspension system. The user or the expert is then able to subjectively classify the type of road encountered depending on his feelings about the response of the vehicle and the resulting comfort for the passengers when traveling on this road.

[0071] Similar to what has been explained above, during comfort training, a loss function and, optionally, a weighting, can be implemented.

[0072] The learning phase P2 can thus advantageously be adapted according to the need and the different types of roads can thus be classified by the user or the expert according to what it is desirable to detect, that is to say according to the equipment of the vehicle 10 and / or according to the ancillary systems 5 likely to be controlled, in particular the suspension or shock absorber system, and according to a desired comfort level defined by the user or the expert.

[0073] During this learning phase P2, the quality and consistency of the training is essential in order to ensure precise and efficient identification of the different types. In this application, the method according to the invention advantageously makes it possible to obtain an overall accuracy of the order of 98% with a weighted Fl score of 97%.

[0074] In the case set out above, at least one learning phase P2 is implemented prior to the execution of the determination phase PL. A plurality of detection phases may be executed following such a learning phase P2, as illustrated by the dotted box P2'. For example, the learning phase P2 may be carried out prior to putting the vehicle into circulation or during maintenance or an overhaul of the vehicle.

[0075] Optionally, the method according to the invention may comprise an additional learning phase P2', implemented after the execution of the phase PI of determining a road type. Such an additional learning phase P2 is executed similarly to what has been explained previously and may be, for example, implemented by a user in order to refine or adapt the training of the identification and / or the level of comfort associated with the different types of road to his preferences or needs.

[0076] Thus, as illustrated in [Fig.2], in driving conditions, once the road type class specific to the road traveled has been identified, that is to say at the end of the classification step E3 of the determination phase PI, the processing unit can extract, for example from a memory element, a comfort level associated with the identified road type class which is then representative of a comfort level felt, in real time, by the user while the vehicle is moving on the road traveled. The detection system 1 can then initiate, via the control module 4, an adjustment step E4 of at least one ancillary system 5 equipping the vehicle 10 so as to correct the comfort level felt by the user.

[0077] The adjustment step E4 can be implemented when the comfort level associated with the identified road type is lower than a predefined suitable comfort level threshold, for example when the determined road type is a bad road associated with a comfort level deemed unsuitable during the learning phase. Such a threshold can be defined beforehand by an expert and / or adjusted by a user. Alternatively, the adjustment step can be implemented when the road type class identified during the determination phase PI is included in a subset of classes previously identified, for example during the learning phase, as requiring a correction of the comfort felt. For example, such a road type can be a “bad road”.

[0078] Here, the term “correct” means that, when the estimated level of perceived comfort is lower than the prerequisite threshold, it is necessary to adapt at least one annex system 5 in order to increase the comfort of a user. Such a principle can be achieved by adapting the suspension system so as to make them more flexible or rigid, by adapting the longitudinal speed of the vehicle, for example by reducing it.

[0079] For example, in the context of controlling a variable suspension or shock absorber system, the damping force can be modified, in particular increased, in various proportions when an obstacle, a speed bump or a low-frequency bad road is detected, said proportions being adapted to the type of obstacle or road detected. Conversely, the damping force can be reduced when passing over a high-frequency bad road or a small impact.

[0080] As previously explained, the annex system 5 in question may be a variable suspension system 51, a longitudinal speed control system and / or a driving assistance system 52. It should be noted that the driving assistance functions may in particular include collision warnings, driver information or alert, emergency braking or even emergency steering. The correction made may then affect one or more of said systems.

[0081] As indicated above, advantageously, the classes of road types, in particular bad roads, can be adapted according to the desired use of the vehicle and / or the detection system, that is to say for example according to the targeted annex system 5 and / or according to an operating mode of the vehicle 10 among conventionally available modes such as a “sporty” or “comfort” driving mode for example. The method according to the invention thus allows, at the time of detection of the type of road of the road traveled, the triggering of a control strategy of the annex systems, such as the variable suspension system 51, specifically adapted to the type of road determined during the PI determination phase and to the vehicle 10 equipped in order to ensure optimal comfort for a user.

[0082] Additionally or alternatively, once the road type class specific to the road traveled has been identified, the detection system 1 can initiate a step E5 of updating a map equipping the vehicle 10 so as to provide information on a road type, in particular a bad road, determined during the determination phase PL. In this sense, during the determination phase PI, for example during the acquisition step E1 and / or during the classification step E3, the acquired data can be associated with GPS data. Optionally, this data can also be associated with a timestamp, in particular in order to ensure suitable mapping when the determined road type has a temporary component, for example due to temporary obstacles or the presence of bad weather such as rain, snow or ice likely to affect the road class.

[0083] The road type class determined for the road traveled, as well as the GPS data, and possibly the timestamp, can then be transmitted to other vehicles, to a database and / or to remote servers via the communication module 8 in order to make this data accessible to other users, in particular other users using a similar or identical model of vehicle 10. It is, for example, then possible to associate in real time a GPS position and possibly a timestamp corresponding to an entry point and an exit point on the type of road detected. This data can, optionally, also be associated with a direction of travel of the vehicle 10 on the road traveled and / or with the longitudinal speed of the vehicle 10.

[0084] Once this information has been transmitted to remote databases or servers, it may be subject to additional processing, in particular to filter, group and / or validate this information before making it available to other users.

[0085] [Fig.4] illustrates a second embodiment of the method according to the invention, in which the determination phase PI comprises, subsequently to the estimation step E2, a validation step E6 of the estimated probability rate carried out on the basis of measurements, by at least one sensor 32, of at least one characteristic relating to the vehicle 10 in a driving situation on the road traveled. In such an embodiment, the estimation step E2 as set out previously can be implemented in anticipation, on the basis of estimated and / or detected data relating to a portion of the road traveled located upstream of the vehicle 10, or in reaction, on the basis of measured data.

[0086] The validation step E6 can be executed simultaneously and / or subsequently to the estimation step E2. The validation step E6 is executed in a similar manner to what was explained above with respect to the processing of data measured during the estimation step E2.

[0087] The classification step E3 of the road traveled into one of the predefined road type classes can then be executed conditionally only when the results of the estimation step E2 and the validation step E6 make it possible to identify an identical road type. In other words, the classification step E3 is only implemented when the probability rates of belonging to a road type class obtained during the estimation step E2 and the validation step E6 are such that they make it possible to identify the same road type class.

[0088] Alternatively, the validation step E6 can be executed in parallel with the estimation step E2 and classification step E3, the validation step E6 reproducing said estimation steps E2 and classification steps E3 on the basis of measured data for the purpose of validation. The method according to the invention then comprises two distinct classification steps, one primary E3, as described with reference to [Fig.2], and the other included in the validation step E6. A classification of the road traveled is then recorded, for example on a memory element 6, and used for the adjustment E4 of an ancillary system only when the two classification steps result in the identification of the same road type class. Such a principle advantageously makes it possible to further optimize the accuracy of the method according to the invention.

[0089] The present invention thus proposes a method for detecting a type of road traveled by a vehicle coupling a phase of determining a type of road and a phase of learning the identification of a class of road type integrating an evaluation of the road comfort felt by a user for a vehicle of a given model and equipment. The method according to the invention can advantageously be transposed to any type of vehicle at a lower cost, the execution of the learning phase making it possible to obtain a detection method adapted to the characteristics of the vehicle, in particular its model and / or its equipment likely to impact road comfort. The invention thus makes it possible to obtain a performance in detecting road types, in particular bad roads, refined and adapted to the equipped vehicle, while ensuring the transportability of the invention to a wide range of vehicles. The method according to the invention then allows more suitable control of the ancillary systems linked to user comfort fitted to the vehicle.

[0090] The present invention cannot, however, be limited to the means and configurations described and illustrated here and it also extends to any equivalent means or configuration and to any technically operative combination of such means insofar as they ultimately fulfill the functionalities described and illustrated in the present document.

Claims

Claims

1. Method for detecting (100) a type of road traveled by a vehicle (10), in particular a vehicle (10) equipped with a variable suspension system (51), the method being characterized in that it comprises a phase of determining (PI) a type of road traveled comprising: - at least one step of acquiring (El), in real time or for a predetermined duration, data relating to the vehicle and / or to a road traveled by the vehicle (10) implemented by means of a detection module (3) comprising at least one sensor (32) and / or at least one detection means (31), such as a camera; - a step of estimating (E2) a probability rate of belonging of the road traveled to at least one class of road type, in particular bad roads, predefined as a function of said acquired data;- a step of classifying (E3) the road traveled into one of the road type classes according to the estimated membership probability rates and extracting at least one level of comfort felt by a user associated with the detected road type class, levels of comfort felt being previously defined by calculation implemented by the processing unit (2) or by manual intervention of a user or technician and associated with each road type class according to at least one piece of equipment specific to the vehicle (10) capable of influencing the comfort felt by a user during the driving phase;- a step of adjusting (E4) at least one ancillary system (5) equipping the vehicle (10), in particular a variable suspension system (51), so as to correct a level of comfort felt by the user, such a step being implemented when the level of comfort associated with a road type class determined during the determination phase (PI) is lower than a predefined adapted comfort level threshold and / or when the determined road type class is included in a predefined class subset identified as requiring the execution of an adjustment step (E4).;

2. Detection method (100) according to the preceding claim, further comprising, prior to the execution of the determination phase (PI), at least one supervised learning phase (P2), specific to the vehicle (10) equipped, with the identification of at least one predefined road type class and the association of at least one level of comfort felt by a user with each road type class as a function of at least one piece of equipment specific to the vehicle (10) capable of influencing the comfort felt by a user during the driving phase, the learning phase (P2) being implemented via a computer (21), a human-machine interface and / or a memory element.

3. Detection method (100) according to one of the preceding claims, further comprising a step of updating (E5) a map equipping the vehicle (10) so as to provide information on a determined road type class.

4. Detection method (100) according to one of the preceding claims, in which the classification step (E3) is implemented only when the estimated probability rate is greater than a predetermined threshold of 60%.

5. Detection method (100) according to one of the preceding claims, in which the estimation step (E2) is executed by a network capable of processing time series of data and of being trained during a learning phase (P2), in particular by a convolutional neural network.

6. Detection method (100) according to one of the preceding claims, in which the data acquisition step (El) is implemented by: - ​​detection and / or estimation of at least one characteristic relating to a portion of the road traveled located upstream of the vehicle (10), in particular a height of a road profile; and / or - measurement by at least one sensor (32) of at least one characteristic relating to the vehicle (10) in a driving situation on said road traveled, in particular a suspension travel, a wheel speed, a longitudinal speed of the vehicle (10) and / or an acceleration of the vehicle (10).

7. Detection method (100) according to one of the preceding claims, in which the determination phase (PI) comprises, subsequent to the estimation step (E2), a validation step (E6) of the estimated probability rate, the validation step (E6) being carried out on the basis of measurements, by at least one sensor (32), of at least one characteristic relating to the vehicle (10) in a driving situation on said road traveled, in particular a suspension travel, a wheel speed, a longitudinal speed of the vehicle (10) and / or an acceleration of the vehicle (10).

8. System (1) for detecting a type of road traveled for a vehicle (10) in a driving situation, the system comprising hardware and / or software elements implementing the method according to one of claims 1 to 7, the hardware elements comprising at least one data processing unit (2), a detection module (3) for at least one characteristic relating to a road traveled and a control module (4) for at least one ancillary system (5) equipping the vehicle (10), in particular a variable suspension system (51).

9. Motor vehicle (10) comprising at least one ancillary system (5) capable of affecting a level of comfort felt by a user, such as a variable suspension system (51), and a detection system (1) according to claim 8, capable of controlling said ancillary system (5).

10. A computer program product comprising program code instructions recorded on a computer-readable medium for implementing the steps of the method according to any one of claims 1 to 7 when said program is running on a computer.

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