Method and device for estimating the wear rate of components of a vehicle traveling on a road

Abstract

The invention relates to methods and devices for estimating the wear rates of a set of at least one component of a vehicle (10) traveling on a first road (1001). To achieve this, the method consists of receiving initial data representative of the characteristics of the first road from a first set of detection devices and identifying a road pattern associated with the first road using a road pattern identification model, the parameters of the road pattern being associated with this road pattern. The method also includes receiving a set of control parameters from at least one on-board active system and estimating the wear rates of the set of at least one vehicle component from at least one wear prediction model associated with the set of components, receiving as input the road configuration parameters and the set of control parameters. Figure 1

Description

Title of the invention: Method and device for estimating the wear rate of components of a vehicle traveling on a road. Technical field

[0001] The present application generally relates to a method for estimating the wear rate of components of a first vehicle traveling on a road. The present application also relates to a method and a device for assisting a driver in controlling a vehicle, for example, an autonomous or semi-autonomous vehicle. The present invention also relates to a method for learning a wear prediction model. Background

[0002] The purpose of this section is to introduce the reader to various aspects of the art, which may be related to various aspects of at least one exemplary embodiment of the present application described and / or claimed below. This discussion is deemed useful in providing the reader with background information that will facilitate a better understanding of the various aspects of this application.

[0003] Many modern vehicles are equipped with advanced driver assistance systems (ADAS). ADAS are passive and active systems designed to eliminate human error in the operation of all types of vehicles and / or to improve driving comfort, for example. ADAS systems use advanced technologies to assist the driver while driving, thereby improving performance. ADAS systems use a combination of sensor technologies to perceive the environment around a vehicle and then provide information to the driver or act on the vehicle's systems, known as active on-board systems, by adjusting their parameters.

[0004] There are several levels of ADAS, acting in particular on the vehicle's active on-board systems, for example adaptive cruise control, suspension control or power steering, by setting parameters to control certain actuators of each active on-board system.

[0005] Vehicle-mounted ADAS systems are powered by data obtained from one or more on-board sensors, such as cameras, or by data received from a remote server. This data can be used, in particular, to detect obstacles in front of the vehicle, such as a pothole, in order to adapt the vehicle's suspension parameters as it passes over them.

[0006] The quality of the vehicle's response to driving conditions depends on its parameters, which can be set at the factory or by a user, for example at the beginning of a journey. However, the settings of the vehicle's active on-board systems are not always optimized, as driving conditions can change during a journey.

[0007] Furthermore, to improve vehicle reliability and passenger safety, vehicles require regular maintenance. Such operations follow a schedule published by a manufacturer, for example, in which they recommend the frequency at which certain mechanical parts should be replaced. These intervals are estimated based on average wear rates, for example, but do not necessarily take into account the actual lifespan of the vehicle. As a result, maintenance operations are carried out more frequently, sometimes more often than necessary, to avoid any risk, making vehicle maintenance expensive. In addition, vehicles may occasionally suffer unusual damage due to certain driving conditions. Unfortunately, these challenging conditions can only be identified by the vehicle's users, and this depends on their own awareness.It is therefore difficult to determine the actual state of wear of a vehicle's components without specific analysis. Summary

[0008] The following section presents a simplified summary of at least one exemplary embodiment to provide a basic understanding of certain aspects of this application. This summary is not an exhaustive overview of an exemplary embodiment. It is not intended to identify the critical elements of an embodiment. The summary that follows merely presents certain aspects of at least one of the exemplary embodiments in a simplified form as a prelude to the more detailed description provided elsewhere in the document.

[0009] According to a first aspect of the present application, a method is provided for estimating the wear rate of a set of at least one component of a first vehicle traveling on a first road, the method comprising the following steps: - receiving first data representative of the characteristics of the first road from a first set of detection devices of said first vehicle; - identifying a road model associated with said first road by feeding said first data into a road model identification model, said road model identification model being learned during a learning phase from training data representative of the characteristics of a set of secondary roads obtained from a set of secondary vehicles, the parameters of the road model being associated with said road model;- receiving a set of control parameters from at least one active embedded system; and; - estimation of the wear rates of said assembly of at least one component of said first vehicle from at least one wear prediction model associated with said assembly of components receiving as input said road model parameters and said set of control parameters.

[0010] Such a method makes it possible to determine the wear rate or fatigue state of the various vehicle components based on the road patterns encountered, which are representative of the actual driving conditions experienced by the vehicle. A relevant preventive maintenance operation can then be easily planned based on this wear rate.

[0011] In an exemplary embodiment, the wear prediction model is associated with a vehicle behavior model associated with the first vehicle, the vehicle behavior model receiving as input the parameters of the road model.

[0012] In another embodiment, the method further comprises receiving second data representative of intrinsic characteristics of said first vehicle (10), said set of control parameters being further determined from the vehicle behavior model receiving said second data as input.

[0013] In another embodiment, the set of control parameters is determined following a minimization function minimizing the wear rates of the set of at least one component of the first vehicle estimated from the wear prediction model receiving as input a first set of loads applied to at least one component. The first set of loads is calculated by the vehicle behavior model as a function of the parameters of the road model.

[0014] In another exemplary embodiment, the wear prediction model is learned in a learning phase from training data representative of a first set of historical maintenance data obtained from a set of third vehicles, the first set of historical maintenance data being associated with a third set of loads applied to component types corresponding to at least one component of the first vehicle.

[0015] In another embodiment, the wear prediction model is fed by a computer-aided design file, and / or a finite element analysis result, and / or road test simulation results, and / or ground test results, and / or mechanical calculations, and / or slice fatigue damage calculations.

[0016] Another example of an embodiment, the method also consists of predicting the maintenance operation based on the wear rates of the assembly of at least one component of the first vehicle.

[0017] According to a second aspect of the present application, a device for estimating the wear rates of a set of at least one component of a vehicle is provided, wherein the device includes a memory associated with at least one processor configured to implement the method in accordance with the first aspect of the present application.

[0018] According to a third aspect of the present application, a computer program product is provided comprising instructions which, when the program is executed by one or more processors, cause one or more processors to execute a method according to the first aspect of the present application.

[0019] According to a fourth aspect of the present application, a non-transient storage medium is provided containing program code instructions for the execution of a method according to the first aspect of the present application.

[0020] The specific nature of at least one of the exemplary embodiments, as well as other objects, advantages, features and uses of at least one of the exemplary embodiments, will become evident from the following description of examples taken in conjunction with the accompanying drawings. Brief description of the drawings

[0021] Reference will now be made, by way of example, to the attached drawings, which show exemplary embodiments of the present application and in which:

[0022] Fig. 1 schematically illustrates a vehicle travelling on a road and its environment according to a particular and non-limiting embodiment of the present invention;

[0023] Figure 2 illustrates a flowchart of the different steps in a method for estimating the wear rate of a set of at least one component of the vehicle in Figure 1, according to a particular and non-limiting example of the present invention; and

[0024] Figure 3 illustrates a functional diagram of an example of a device configured to estimate the wear rates of a set of at least one component of the vehicle of Figure 1, according to at least one exemplary embodiment. Description of exemplary implementation methods

[0025] At least one of the exemplary embodiments is described in more detail below with reference to the accompanying figures, in which examples of at least one of the exemplary embodiments are illustrated. However, an exemplary embodiment can be embodied in many alternative forms and should not be interpreted as being limited to the examples stated herein. Therefore, it should be understood that there is no intention to limit the exemplary embodiments to the particular forms disclosed. On the contrary, the disclosure aims to cover all modifications, equivalents and alternatives falling within the spirit and scope of this application.

[0026] Fig. 1 schematically illustrates an environment 1 comprising a first vehicle 10, according to a particular and non-limiting embodiment of the present invention.

[0027] The first vehicle 10 corresponds, for example, to a vehicle with an internal combustion engine, with electric motor(s), or to a hybrid vehicle with an internal combustion engine and one or more electric motors, for example, a plug-in hybrid vehicle. The first vehicle 10 therefore corresponds, for example, to a land vehicle, for example, a car, a truck, a bus, or a motorcycle.

[0028] The first vehicle 10 can be equipped with various advanced driver assistance systems (ADAS) which are configured to assist the driver of the first vehicle 10 in controlling the first vehicle 10. The first vehicle 10 can be a semi-autonomous vehicle or an autonomous vehicle, for example, a vehicle operating with a level of autonomy which is, for example, greater than or equal to 2 in a range of autonomy between 1 and 5, level 1 corresponding to a vehicle having a minimum level of autonomy and level 5 corresponding to a vehicle having a maximum level of autonomy, for example, a fully autonomous vehicle.

[0029] The first vehicle 10 is equipped with a road condition detection module 11, the purpose of which is to process one or more time-stamped input signals, periodic or not, and not necessarily aligned at the origin in the time or frequency domain. A range of signals extends, but is not limited to, readings from the first set of detection devices comprising at least one sensor from among: - inertial sensors, - wheel speed sensors, - steering wheel sensors, - radars, - lidars, - laser scanners, - cameras, - acceleration sensors, - vibration sensors, - noise sensors, - rain sensors, - the external thermometer, and - the weather station. Please note that this list is not exhaustive.

[0030] The road surface detection module 11 periodically analyzes the recent portion of available signals, pre-processes them as needed, for example by sampling, filtering, imputation of missing values, or removal of outliers, and aligning them, for example by interpolation, to obtain regularly spaced multidimensional data points, called time series, characterizing the evolution of location-attributable road characteristics over the distance recently traveled by the first vehicle. Each of these time series is, for example, also associated with the vehicle identification number, called the VIN, to allow derived information to be attributed to that vehicle at a later stage of processing. The length of the time series can depend on the vehicle's ability to cache the recent portion of the data and can range from a small value, for example, the last minute, to the duration of the ignition cycle.

[0031] According to an exemplary embodiment, the first vehicle 10 is equipped with a communication unit, corresponding for example to a communication module of the TCU (Telematic Control Unit), the ATB (Autonomous Telematic Box) or the RFSB (Radio Frequency Service Box). Such a communication unit is advantageously connected to one or more antennas to, for example, transmit and / or receive data to and / or from a remote device, for example a data center 120 of the "cloud" 100, or a mobile communication device, such as a smartphone or tablet, via a wireless connection, according to OTA (Over The Air) technology for example.Wireless communication between the first vehicle 10 and the remote device 120 is established, for example, via a network infrastructure including, for example, one or more relay antennas or base stations 110, or via a direct communication method (for example, when the mobile communication device is within a distance of the first vehicle 10 that allows for such a communication method, for example, a distance of less than 10, 20, or 50 m). The wireless connection is based, for example, on one or more wireless communication protocols such as Bluetooth®, Wi-Fi® (based on IEEE 802.11), LTE (“Long-Term Evolution”), LTE-Advanced, fourth- or fifth-generation 3GPP (“3rd Generation Partnership Project”), called 3GPP 4G or 5G, or satellite communication such as Starlink®.

[0032] The first vehicle 10 is also equipped with at least one active on-board system. Such an active on-board system acts, for example, on the steering of the first vehicle, the brakes, the speed, the stabilizer bar, the shock absorbers, the suspension parameters or other parameters of the mechatronic system.

[0033] According to an exemplary embodiment, the first vehicle 10 is equipped with a second set of sensing devices comprising, for example, a thermometer, a pressure sensor, a force sensor, or a strain gauge sensor. The second set of sensors makes it possible to know the intrinsic parameters of the vehicle or to capture the physical reactions of the vehicle or its components in its environment.

[0034] According to an exemplary embodiment, the first vehicle 10 is equipped with a navigation and geolocation system, also called a GNSS (Geolocation and Navigation by a Satellite System), for example a Global Positioning System (GPS) satellite positioning system. A geolocation signal is available to provide information on the vehicle's current physical position.

[0035] According to another embodiment, the arbitrary combination of input signals is not assumed to be accompanied by a geolocation signal, but only by vehicle identification, for example, the VIN number. In this case, the vehicle signals, called non-geolocated signals, after being preprocessed and aligned, are converted into multidimensional data points that cannot be associated with any physical location. In the case of these non-geolocated signals from several vehicles, it is not possible to determine where the roads coexist.

[0036] The embedded systems, such as the active on-board system, the communication unit, the ADAS, and the road surface detection module 11, are each controlled by one or more computers, for example, electronic control units (ECUs). These ECUs form, for example, a multiplexed architecture for providing various services useful for the proper functioning of the first vehicle 10 and for assisting the driver and / or passengers of the vehicle in controlling the first vehicle 10 by controlling the embedded systems of the first vehicle 10. The ECUs communicate and exchange data with each other via one or more computer buses, for example, a communication bus such as CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate data bus type), FlexRay (according to ISO 17458), LIN (Local Interconnect Network), or Ethernet (according to ISO / IEC 802-3).

[0037] The first vehicle 10 travels on the first road 1001. The first road is, for example, an open road, such as a county road or a national highway, or an off-road path or trail. The first road 1001 has certain characteristics associated with features. These features include, for example, road roughness, pavement type, gradient, road surface undulations, but also curves, intersections, stops, regulations, and the environment. The road features are then classified into groups called road models, each road model comprising road model parameters.

[0038] A method for controlling an active system on board the first vehicle 10 and / or estimating the wear rate of a set of at least one component of the first vehicle 10 traveling on the first road 1001 is advantageously implemented by one or more processors of one or more computers on board the first vehicle 10.

[0039] During a first operation, the computer receives first data representative of the characteristics of the first road from the first set of sensors of the first vehicle 10.

[0040] In a second operation, a road model associated with said first road 1001 is identified by inputting said first data into a road model identification model, also called a road model identifier. The road model identification model was learned in a training phase from training data representative of the characteristics of a set of secondary roads obtained from a set of secondary vehicles, the parameters of the road model being associated with each road model.

[0041] During the learning phase, appropriate signals indicating various road characteristics are collected in a distributed manner, involving all second vehicles, and over a longer period of time. The data representing these signals can be physically hosted in a data center equipped with several co-located data processing servers, or in a cloud-based infrastructure or any other suitable distributed infrastructure. The means and protocols of data transmission are not limited and can employ any modern technology such as over-the-air (OTA) transmission over wireless or cellular networks.

[0042] For example, data collection takes from one to several months, and the data is collected from hundreds of vehicles of different categories, these vehicles constituting a third set of detection devices similar to the first set of detection devices. The aligned multidimensional signals, each originating from a vehicle in the third set of detection devices, must be transformed into a format that allows for robust comparisons between them. Therefore, a generic feature extraction operation is performed on each signal, and the outputs are merged into a single dataset. Vehicle identification is preserved; for example, after a feature extraction step, the identity of the vehicle that emitted the source signal is always unambiguously assigned to each output record.For computational efficiency, the above transformation must be performed, and the results persist close to the storage of the aforementioned signals, keeping the data at rest to avoid overload. of significant data transmission. Therefore, when sufficient data is collected, the assumptions of certain road models in the space of analyzed signaling features representing specific named or unnamed classes can be verified with greater statistical significance.

[0043] The objective of the road pattern identification model is to repeatedly analyze initial data comprising feature-coded data points in order to identify coherent groups of road features or, alternatively, to confirm the existence of predetermined categories thereof. If no prior knowledge of the road feature signatures is available, an unsupervised technique (clustering) is used. Otherwise, the task can be formulated as a pattern matching / classification, which will require further training using independent data samples with assigned ground truth class labels. In other words, the pattern identification model aims to find a function mapping a multidimensional input entity X to a cluster identifier or one of the predefined class labels.

[0044] If geolocation information, for example latitude, longitude, and possibly altitude, is present among the received data, then the solution to the above grouping or classification problem will reflect, to some extent, the association of road patterns with particular locations on the Earth's surface. The strength of this association will increase with the relative weight assigned to the location variables. In one possible embodiment of this invention, the location variables can be completely separated, and the 2D grouping (or 3D grouping if altitude is also to be treated as a variable) can be performed only on these variables in order to initially find only the locations visited by many vehicles.Next, the grouping in the second step can be performed on the remaining variables, but only in the spatial neighborhood of the most common geographic locations previously found.

[0045] According to an exemplary embodiment, the a priori signatures of the road model categories are available, and it is possible to apply lookalike modeling in cases where the prior class signatures have a different representation from that of the actual features extracted from the available signals. This is possible, for example, in situations where the signatures are constructed under different conditions, for example in a controlled experimental environment or involving more sensors than those available in the vehicles of the second set of vehicles. This lookalike modeling is responsible for optimally assigning the extracted signal features to the closest model, but only by taking into account the common data dimensions. For example, if the grouped features extracted by the set of second vehicles only include aggregated readings of the acceleration of the X and Y axes, but the signature features also contain the aggregated readings of the acceleration of the Z axis, pattern matching can still be performed in a 2-dimensional feature space with the Z-axis acceleration data ignored.

[0046] According to another exemplary embodiment, assuming the availability of predefined pattern signatures and location variables in the data, a variant of the non-maximal suppression technique known from computer vision is proposed to integrate multiple hypotheses of densely generated models around the actual locations of road conditions. The use of this technique implies that only predictions with the highest degree of confidence are initially selected, and that all other predictions with an overlap with the selected predictions above a certain threshold are suppressed. It is particularly useful for avoiding the generation of redundant (almost perfectly co-located) models or an excessive number of models and allows for the efficient achievement of two objectives: clustering followed by classification.Non-maximal suppression is performed for each subset of predictions that produced the same model labels. If multiple model hypotheses were generated around the same physical location, this may indicate low overall confidence in the model identifier. Such predictions may therefore be excluded (or deferred until other signals sampled near that location are available) when no clearly dominant model label is ultimately produced.

[0047] According to an exemplary embodiment, during a third operation, the first data is transmitted to the data center 120 or to the cloud 100 by the first communication unit of the vehicle 10. The data collected by the first vehicle 10 can then be used to enrich the database and update the road model identification model, or even refine the road trajectory identification model.

[0048] According to an exemplary embodiment, during a fourth operation, the position of the first vehicle 10 is acquired by the geolocation system, the first data then being associated with the current position of the first vehicle 10. According to this exemplary embodiment, the first vehicle 10 receives data from the data center 120 or the cloud 100, the data including information guiding the identification of the road pattern of the second operation.

[0049] According to an exemplary embodiment, during a fifth operation, a second data point representing intrinsic characteristics of the first vehicle 10 is received. This data is acquired, for example, by the second set of detection devices. This second set of data is used, for example, as input for a vehicle behavior model. This second set of data includes, for example, data relating to the number of passengers or the payload, actuator setting ranges, tire type, or the condition of a part of the first vehicle 10. Optionally, this data is transmitted to the data center 120 or to the cloud 100 by the first communication unit of the vehicle 10.

[0050] According to a first embodiment, a set of control parameters is received, for example from the data center 120 or the cloud 100, the set of control parameters being determined according to the parameters of the route associated with the first route 1001 and possibly according to the second data by means of an online vehicle behavior model or by means of tables, such as lookup tables (LUTs).

[0051] According to a second embodiment, a set of control parameters is determined from a vehicle behaviour model associated with said first vehicle 10 receiving as input said route model parameters associated with the first route 1001 and possibly as a function of the second data.

[0052] For the first and second embodiments, the vehicle behavior model is constructed, for example, from CAD files, physical calculations, and / or multibody dynamics simulations. Numerous models are constructed for different types and sizes of vehicles. The vehicle behavior model used is the one associated with a vehicle whose characteristics are closest to those of the first vehicle 10. In particular, the vehicle behavior model is learned or improved using data collected via sensors onboard the vehicles, for example, via the second set of detection devices of the first vehicle 10 or similar sensors onboard in other vehicles.The vehicle behavior model is, for example, learned in a learning phase from training data representative of a first set of physical parameter values ​​obtained from a set of third-party vehicles, said first set of physical parameter values ​​being associated with said road pattern and said set of control parameters.

[0053] According to an exemplary embodiment, the set of control parameters is determined in order to optimize the operating parameters for driving and / or piloting the first vehicle 10. The set of control parameters is then determined following a maximization function that maximizes user comfort in the first vehicle 10, user comfort being estimated from a user comfort prediction model receives a second set of loads as input. This second set of loads is calculated by the vehicle behavior model based on the parameters of the road model. In the first example, the vehicle behavior model determines the vibration of the passenger compartment of the first vehicle 10 as a function of the speed of the first vehicle, a road profile, and the stiffness of the suspension system of the first vehicle 10. In the second example, the vehicle behavior model determines an inertial force applied to the passengers as a function of the radius of curvature of a turn and the speed of the first vehicle 10. In the third example, the vehicle behavior model determines a torque to be applied by a driver to a steering wheel as a function of the radius of curvature of a turn, the speed of the first vehicle 10, and the power steering parameters.

[0054] According to an exemplary embodiment, the set of control parameters is further determined following a minimization function that minimizes the wear rates of a set of components of the first vehicle 10, the wear rate being estimated from at least one wear prediction model associated with a set of components. The wear rate model receives as input a first set of loads to determine the wear rates, the first set of loads being calculated by the vehicle behavior model as a function of the road model parameters.

[0055] The set of components includes, for example, all or part of the following components: - suspension components such as the suspension arm, spring and / or shock absorber, - steering components such as the power steering system, the steering shaft and / or the steering rack, and / or - the tires, and / or - braking system components such as brake pads and / or brake discs, and / or - engine components, and / or - chassis components, and / or - shock absorbers and / or silent blocks.

[0056] According to an exemplary embodiment, the wear prediction model is fed by: - a computer-aided design file, and / or - a result of finite element analysis, and / or - results of road test simulations, and / or - results of ground tests, and / or - mechanical calculations, and / or - Fatigue damage calculations in slices.

[0057] The wear prediction model was, for example, learned in a training phase from training data representative of a first set of historical maintenance data obtained from a set of third-party vehicles. This first set of historical maintenance data is associated with a third set of loads applied to different types of components, corresponding to at least one component of the first vehicle. For example, the wear prediction model is learned by matching the third set of loads or simulation results with the analysis of parts recovered from the third-party vehicles.

[0058] With the output of the pattern identification subsystem, for example each historical signal being assigned a distinct road pattern that it probably represents, the following method is proposed to estimate the propensity of vehicles to break down due to a potentially negative impact of certain road conditions or, conversely, the expected duration of their operation remaining without fault.

[0059] For newly designed vehicles, there are few or no historical warranty claim records or service reports from which knowledge could be derived to guide any statistical learning approach for life estimation. In this case, computer-aided engineering (CAE), virtual simulation, and durability calculations combined with a field test (FT) or road test simulation (RTS) are used to estimate the life of the vehicle system and / or components. For a given vehicle, with a given labeled road layout, and given operating parameters for steering, braking, speed, and / or suspension, the sliced ​​damage attributable to the most recent time period or distance traveled can be calculated.Next, it can be correlated with the results of field tests (PG) or road test simulations (RTS) obtained over a longer period or mileage, using different models to determine the total accumulated durability damage. The total damage can then be matched with the expected service life of the vehicle / components, and / or the time to the next scheduled maintenance can be estimated. In the wear prediction model, vehicle conditions corresponding to driving patterns and vehicle operating parameters are included and taken into account. The material properties and the component / system mechanism can be obtained from physical tests, assumptions, or simulations.

[0060] For vehicles in production for a sufficient period, the wear prediction model approach described above can be combined with a statistical learning approach to better estimate the expected durability of the vehicle. It is assumed that a record of historical warranty claims, maintenance reports, or other similar documentation exists where repairs or parts replacements performed on vehicles of a given identity can be unambiguously linked to their root causes. From this record, a subset is selected where the root cause is related to structural durability over a predefined period of time or distance traveled.Next, for the same list of vehicles, the algorithm constructs predictive variables based on information about whether, when, and / or how frequently during the same analysis period each particular vehicle emitted signals that were assigned to the classes or groups found by the road pattern identifier. These variables can range from total counts for all road pattern classes / clusters, to counts per class / cluster, occurrence frequencies, and other simple aggregations, to arbitrarily complex transformations and / or combinations thereof. Auxiliary variables can be used to describe vehicle characteristics, depending on their availability, provided they contribute to greater accuracy in the estimated model.

[0061] Consequently, one or more statistical models are trained to quantify the risk of failure of the vehicle system / component during its use on the road. In other words, the following hazard functions are learned:

[0062] [Math.l] •> [Math.l] = failure[x, 0j]

[0063] where hj is the function of the expected randomness of the occurrence of the j-th type of failure in a predefined time window or distance to be travelled, X is the vector of predictive variables characterizing the historical state of a vehicle, with at least one dimension of this vector reflecting if, when and how often it emitted signals attributed to one of the categories or groups of recognized road models, and 0j is a vector of model parameters to be optimized.

[0064] Any advanced statistical learning approach can be used to estimate the above functions. Furthermore, the notion of conditional probability of failure of a system or vehicle component can have different formulations, but linked. For example, the function being optimized can directly incorporate the temporal aspect of the prediction through modeling:

[0065] [Math.2] ^^failure < 0 Or [Math.2] Al ~ P( -^def alliance —

[0066] where the probability of a type j failure occurring before or after a determined time / mileage t is estimated.

[0067] Once the above functions are estimated, historical data from other vehicles, including the first vehicle 10, which were not seen during the model training stage, can be used to predict if or when a failure of each type considered may occur and thus trigger possible mitigation or prevention activities. Furthermore, for certain classes of learning algorithms, such as logistic regression, ensemble learning algorithms, or the use of model-independent techniques such as LIME or Shapley values, it is possible to quantify the contribution of the road model interaction variables to the model's decisions, both at the level of the entire population of rated vehicles and at the level of individual vehicles.

[0068] During a sixth operation, the active on-board system is controlled according to all the control parameters.

[0069] For example, for a first vehicle 10 equipped with Level 1 or higher autonomous driving capabilities or with an active suspension system, with road information signals collected and their patterns identified, the autonomous driving system or the active suspension system can be used and adjusted to optimize driver handling / passenger comfort, increase safety, and reduce structural damage caused by different road conditions. Speed, steering angle, acceleration, deceleration, preset driving mode, suspension height and roll stiffness, shock absorber damping coefficient, and / or stabilizer bar can all be modified and optimized according to different road patterns.

[0070] If the previously known road patterns and the first data acquired by the first vehicle 10 are geolocated, and the road pattern categories are known a priori (they have a predetermined semantics), given the momentary location, the horizontal speed of a vehicle, possibly a map with the local road network, and the known pre-calculated locations of the road patterns detected from the pattern identification operation, it is It is possible to predict the time required to reach the closest model in space. Therefore, adjustments to operating parameters can be triggered some time before the occurrence of the predicted condition. This adjustment can be guided by the vehicle structure's response to specific conditions, obtained from the vehicle behavior model, as described below.

[0071] If both known road patterns and initial data acquired by the first vehicle 10 are geolocated, but the road pattern categories are not known a priori, it is possible to estimate the time required to reach the anticipated hazardous road segment and to take action in advance. However, since the nature of the condition is initially unknown, a generic and conservative adjustment of the vehicle's operating parameters can be made, such as a speed reduction, or an alert can be issued to the driver in order to minimize the expected negative impact on the vehicle as well as on driver comfort and safety.Over time, however, the impact of "unnamed" road conditions can be empirically quantified, and the rules for updating the respective operating parameters can be determined on a data-driven basis, for example, using reinforcement learning with a real-value action space and a reward function that minimizes the negative impact of parameter updates on the durability of selected components. Reinforcement learning with a real-value action space is, for example, explained in US patent US10776692B2 concerning "continuous control with deep reinforcement learning," written by TP Lillicrap, JJ Hunt, A. Pritzel, NMO Heess, T. Erez, Y. Tassa, D. Silver, and DP Wierstra, and published in 2020.

[0072] If the already known road patterns or the initial data acquired by the first vehicle 10 are not geolocated, it is generally impossible to establish a relationship between the physical location of a vehicle and the location of the patterns. In this case, the first vehicle 10 will only be able to detect the road condition associated with the pattern using its detection instruments if it is already very close to or in direct contact with that condition. This means that the assignment of the pattern to one of the named or unnamed classes will also be delayed until the very last moment, and that the vehicle's operating parameters can therefore only be adapted in advance when the available sensors allow it, an example of such sensors being the front cameras.Otherwise, the detected road condition will generally be classified, and therefore the operating parameters adjusted, only after the vehicle has already begun to be affected, making the entire system reactive rather than predictive. For prolonged impacts, such as long sections of rough surface, this is always advantageous for the durability of the first vehicle.

[0073] In a seventh operation, the wear rates of the set of at least one component of the first vehicle 10 are estimated from at least the wear prediction model associated with the set of components receiving as input the parameters of the road configuration and the set of control parameters.

[0074] During an eighth operation, a maintenance operation is planned based on the wear rates of at least one component of the first vehicle 10. Capable of reasoning about the expected time to failure, the process aims to facilitate decision-making when managing the cost of first vehicle 10 maintenance or planning the supply of spare parts. An estimation of the vehicle condition model is used directly to guide the selection of vehicles to be inspected.

[0075] For example, vehicles that have been assigned a risk of failure or a cumulative damage level exceeding a predefined threshold, or those whose predicted failure time is below a certain threshold, may be placed on the list of vehicles awaiting action. If the component most likely to malfunction is critical—for example, if it could jeopardize the safety of the driver or passengers, or result in a significant negative experience or reputational damage—a customer will normally be asked to have the first vehicle inspected, but this is subject to the customer's agreement. If the component in question is less critical, information about the predicted problem may be used in many other ways, for example, to put the part or system of the vehicle in question on the list of things to be done for the next inspection or routine maintenance.Furthermore, the possible repair or replacement of a component is conditional upon confirmation of the existence of a defect or a general aging effect of the components once the first vehicle 10 has already been recalled for inspection.

[0076] According to an exemplary embodiment, the following operations are carried out based on an estimated wear rate. In a ninth operation, a first user of the vehicle 10 is informed of a required inspection. For example, by using the on-board infotainment system (IVI) to transmit a message by displaying graphic content on a screen or by broadcasting an audio message via on-board speakers. The required inspection may consist of recalling the first vehicle 10 for examination at a workshop or requesting the user to perform a visual inspection.

[0077] In addition, there are other, less tangible aspects of the above process that can nevertheless have a crucial effect on the parties involved. Among other things, early prediction of a fault can prevent a vehicle breakdown in an unexpected road situation and thus minimize both driver stress and the costs associated with towing the vehicle to a garage. Secondly, if the condition of the vehicle presents a danger to the health or safety of the driver or passengers; mitigating risks through early vehicle recall or regular inspection or maintenance can generate far more long-term benefits for all parties than the potential expenses it generates.

[0078] To better support the decision-making process related to the management of predictive vehicle maintenance activities, a distinct embodiment of the present invention includes an optimization module that quantifies the impact of all the above factors and maps them on a common scale to ultimately find the best setting for the prediction model in order to maximize the expected gain. In principle, the formula for the expected gain is designed to allow for the lowest possible maintenance frequency in order to avoid unplanned reactive maintenance, without incurring excessive costs associated with overly extensive preventive maintenance.

[0079] The proposed new method for optimizing the parameters of the failure and wear prediction model involves directly linking the desired cost / benefit factors to the loss function optimized by the wear prediction model during the learning process, as well as to the rating function used to judge the model's quality on a particular dataset. More specifically, assuming that the cost / benefit factors can each be assigned true positives (TP), false positives (FP), true negatives (TN), or false negatives (FN), and expressed on a common scale, for example, an amount of money lost / gained, the following formulas can be used:

[0080] [Math.3] ^TP = IL^TP •> [Math.3] WTN^jfiTN ' WFP= -^^FP' WFN= '^f^FN'

[0081] a = WFN'

[0082] where bj and Ci denote the unit amounts (per vehicle) associated with different benefit or cost factors and related to each true positive, true negative, false positive, and false negative (respectively) produced by the wear prediction model, and a is a measure of the increased importance that the wear prediction model should place on reducing the number of false positive cases (vehicles without inherent defects or not subject to aging, still classified as such) than on reducing the number of false negative cases (vehicles having the real problem but classified as

[0083]

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[0093] (no problem). With the above, the wear prediction model is trained using a weighted binary cross-entropy loss function: [Math.4] £ = X - ay log(p.)-( 1 -y ,)log( 1 -p.) where yj are the ground truth class labels (1 for a vehicle requiring maintenance or 0 otherwise) and :P7 are the probabilities of each training set vehicle requiring maintenance as evaluated by the model. Furthermore, when evaluating each validation dataset during the routine model cross-validation procedure, the following scoring function can be used: [Math.5] s= Or: '1 if the i-th instance is true positive 0 otherwise ' 1 if the i-th instance is true, negative 0 otherwise 5(04)- 1 SJ i-th instance is a false positive 0 otherwise 1 if the i-th instance is false negative, 0 otherwise With such a rating function, the wear prediction model will automatically favor parameter sets that maximize the hypothetical seal gain and minimize the hypothetical seal loss, regardless of the factors that contribute most and in which direction. Figure 3 shows a functional diagram illustrating an example of system or device 3 in which various aspects and exemplary embodiments are implemented. Device 3 can be integrated as one or more devices comprising the various components described below. In various embodiments, the Device 3 can be configured to implement one or more of the aspects described in this application.

[0094] Examples of equipment that may constitute all or part of Device 3 include a calculator, an electronic control unit (ECU), a data processing device, or other communication devices. The elements of Device 3, alone or in combination, may be incorporated into a single integrated circuit (IC), several integrated circuits, and / or discrete components. For example, in at least one embodiment, the processing and encoder / decoder elements of Device 3 may be distributed across several integrated circuits and / or discrete components. In various embodiments, Device 3 may be communicatively coupled to other similar systems or other electronic devices via, for example, a communication bus 320 through dedicated input and / or output interfaces 32.

[0095] The device 3 may include at least one processor 30 configured to execute the instructions loaded therein to implement, for example, the various aspects described in this application. The processor 30 may include integrated memory, an input / output interface, and various other circuits known in the art. The device 3 may include at least one memory 31 (for example, a volatile memory device and / or a non-volatile memory device). The device 3 may include a storage device, which may include non-volatile memory and / or volatile memory, including, but not limited to, electrically erasable programmable EEPROM, read-only memory (ROM), programmable read-only memory (PROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, a magnetic disk drive, and / or an optical disk drive.The storage device may include, but is not limited to, an internal storage device, a tethered storage device, and / or a network-accessible storage device.

[0096] The device 3 may include an encoder / decoder module configured, for example, to process image data to provide encoded / decoded text data, and the encoder / decoder module may include its own processor and memory. The encoder / decoder module may represent one or more modules that may be included in a device to perform encoding and / or decoding functions. As is known, a device may include either or both encoding and decoding modules. Furthermore, the encoder / decoder module may be implemented as a separate component or may be incorporated into the processor 30 as a hardware and software combination, as is known to those skilled in the art.

[0097] The program code to be loaded onto the processor 30 or the encoder / decoder to perform the various aspects described in this application may be stored in a storage device and then loaded into memory 31 for execution by the processor 30. According to the various embodiments, one or more of the processors 30, memory 31, storage devices, and encoder / decoder modules may store one or more of the various elements during the execution of the processes described in this application. These stored elements may include, but are not limited to, data representing the audio content, data representing the video content, haptic data, a bitstream, matrices, variables, and intermediate or final results of the processing of equations, formulas, operations, and operational logic.

[0098] In several embodiments, the memory inside the processor 30 and / or the encoder / decoder module can be used to store instructions and provide working memory for processing that can be performed during data processing, encoding or decoding.

[0099] In other embodiments, however, memory external to the processing device (for example, the processing device may be the processor) may be used for one or more of these functions. The external memory may be memory 31 and / or the storage device, for example, dynamic volatile memory and / or non-volatile flash memory. In at least one embodiment, fast external dynamic volatile memory such as RAM may be used as working memory for data processing.

[0100] Fig. 2 shows a functional diagram of the steps of a method 2 for estimating the wear rate of a set of at least one component of a vehicle, for example of the first vehicle 10 traveling on a first road, in accordance with at least one exemplary embodiment.

[0101] The method can, for example, be implemented by an on-board device of the first vehicle 10 or by the device 3 of the [Fig.3].

[0102] In a first step 21, first representative data of characteristics of the first road are received from a first set of first vehicle detection devices 10.

[0103] In a second step, step 22 consists of identifying a route model associated with the first route 1001 by entering the first data into a route model identification model. The parameters of the route model are associated with the route model.

[0104] The road pattern identification model was learned in a learning phase from training data representative of the characteristics from a set of secondary roads obtained from a set of secondary vehicles.

[0105] In a third step 23, a set of control parameters for at least one active on-board system is received as a function of the route configuration parameters.

[0106] In a fourth step 24, the wear rates of the set of at least one component of the first vehicle 10 are estimated from at least one wear prediction model associated with the set of components receiving as input the parameters of the road configuration and the set of control parameters.

[0107] The terminology used in this document is intended to describe particular embodiments only and is not meant to be restrictive. It shall also be understood that the terms "includes / comprises" and / or "comprising / comprising," when used in this specification, may specify the presence of the indicated features, elements, and / or components, for example, but do not preclude the presence or addition of one or more other features, elements, components, and / or groups thereof. Furthermore, when an element is said to be "reactive" or "connected" to another element, it may be directly reactive or connected to the other element, or intermediate elements may be present. Conversely, when an element is said to be "directly reactive" or "directly connected" to another element, there are no intermediate elements present.

[0108] It should be noted that the use of any of the symbols / terms " / ", "and / or" and "at least one of", for example, in the cases of "A / B", "A and / or B" and "at least one of the terms A and B", may be intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B).As a further example, in the cases of "A, B and / or C" and "at least one of the options A, B and C", this formulation is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A, B and C). This can be extended, as is clear to anyone of ordinary skill in this and related arts, to as many items as there are enumerations.

[0109] Different numerical values ​​may be used in this application. The specific values ​​may be given as examples, and the aspects described are not limited to these specific values.

[0110] It will be understood that, although the terms first, second, etc., may be used here to describe various elements, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, a first element could be called a second element, and, likewise, a second element could be called a first element without departing from the teachings of this application. No order is implied between a first element and a second element.

[0111] The reference to "an exemplary embodiment" or "an implementation" or "an implementation," and other variations thereof, is frequently used to indicate that a particular feature, structure, characteristic, etc. (described in relation to the embodiment / implementation) is included in at least one embodiment / implementation. Thus, the occurrences of the expression "in an exemplary embodiment" or "in an implementation" or "in an implementation," and any other variant thereof, appearing in various places in this application do not necessarily all refer to the same embodiment.

[0112] Similarly, the reference in this document to "in accordance with an exemplary embodiment / example / implementation" or "in an exemplary embodiment / example / implementation", and other variants thereof, is frequently used to indicate that a particular feature, structure or characteristic (described in relation to the exemplary embodiment / example / implementation) may be included in at least one exemplary embodiment / example / implementation.Thus, the occurrences of the phrase "in accordance with an exemplary embodiment / example / implementation" or "in an exemplary embodiment / example / implementation" in various places in the specification do not necessarily all refer to the same exemplary embodiment / example / implementation, and separate or alternative exemplary embodiments / examples / implementations do not necessarily exclude each other from other exemplary embodiments / examples / implementations.

[0113] The reference figures in the claims are given for illustrative purposes only and do not limit the scope of the claims. Although not explicitly described, the embodiments / examples and variants shown may be used in any combination or subcombination.

[0114] A number of implementations have been described. However, it will be understood that various modifications can be made. For example, elements from different implementations can be combined, supplemented, modified, or deleted. to produce other implementations. Furthermore, a person with ordinary skills will understand that other structures and processes can be substituted for those disclosed and that the resulting implementations will perform at least substantially the same function(s), in at least substantially the same manner, to achieve at least substantially the same results as the disclosed implementations. Therefore, these and other implementations are contemplated in this application.

Claims

Demands

1. A method for estimating the wear rate of a set of at least one component of a first vehicle (10) traveling on a first road, the method comprising the following steps: - receiving (21) first data representative of characteristics of the first road from a first set of detection devices of said first vehicle (10); - identifying (22) a road model associated with said first road by feeding said first data into a road model identification model, said road model identification model being learned during a learning phase from training data representative of characteristics of a set of secondary roads obtained from a set of secondary vehicles, the parameters of the road model being associated with said road model;- receiving (23) a set of control parameters of at least one on-board active system, the set of control parameters being determined from a vehicle behavior model associated with said first vehicle (10) and receiving as input said road model parameters associated with the first road, said on-board active system being a system acting on: the steering of the first vehicle, the brakes, the speed, the stabilizer bar, the shock absorbers, the suspension parameters or other parameters of a mechatronic system; and - estimating (24) the wear rates of said set of at least one component of said first vehicle (10) from at least one wear prediction model associated with said set of components receiving as input said road model parameters and said set of control parameters.

2. Method according to claim 1, further comprising receiving second data representative of intrinsic characteristics of said first vehicle (10), said set of control parameters being further determined from the vehicle behavior model receiving said second data as input.

3. A method according to claim 2, wherein said set of control parameters is further determined as a result of a minimization function minimizing the wear rates of said set of at least one component of said first vehicle (10) estimated from said wear prediction model receiving as input a first set of loads applied to said at least one component, said first set of loads being calculated by said vehicle behavior model as a function of said road model parameters.

4. A method according to claim 3, wherein said wear prediction model is learned in a learning phase from training data representative of a first set of historical maintenance data obtained from a set of third vehicles, said first set of historical maintenance data being associated with a third set of loads applied to component types corresponding to at least one component.

5. A method according to claim 4, wherein said wear prediction model is fed by: - ​​a computer-aided design file, and / or - a finite element analysis result, and / or - road test simulation results, and / or - ground test results, and / or mechanical calculations, and / or - slice fatigue damage calculations.

6. A method according to any one of claims 1 to 5, further comprising a prediction of a maintenance operation based on said wear rates of said assembly of at least one component of said first vehicle (10).

7. Computer program product comprising program code instructions for carrying out the process according to any one of claims 1 to 6, when said program is executed on a computer.

8. Device (3) for estimating the wear rate of an assembly of at least one component of a vehicle, wherein said device includes a memory (31) associated with at least one processor (30) configured to implement the method according to any one of claims 1 to 6.

9. Vehicle (10) comprising the device (3) according to claim 8.

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