Method and device for controlling an on-board navigation system of a vehicle

Abstract

The invention relates to methods and devices for controlling an on-board navigation system of a vehicle (10). To achieve this objective, data representative of the geographical points of departure and arrival are first received, and a set of routes (101, 102) is determined. Each route comprises a set of road segments (101a, 101b, 101c, 101d, 102a, 102b). A set of road patterns associated with all the road segments of each route is identified by inputting all the road segments into a road pattern identification model, the road pattern parameters being associated with each road pattern of the set of road patterns. A wear rate for each route is determined based on the set of road patterns and an optimal route is selected based on the wear rate from among the set of routes.Finally, the on-board navigation system is controlled based on the optimal route. Figure 1.

Description

Title of the invention: Method and device for controlling an on-board navigation system of a vehicle Technical field

[0001] The present application generally relates to the field of a method for controlling an on-board navigation system of a vehicle. The present invention also relates to a method for selecting a route based on an estimate of the wear rate. 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. 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 that is described and / or claimed below. This discussion is deemed helpful to provide the reader with background information that will facilitate a better understanding of the various aspects of the present application.

[0003] Many modern vehicles are equipped with advanced driver assistance systems (AD AS). AD AS are passive and active systems designed to eliminate human error in driving 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 vehicle systems called active on-board systems by setting their parameters.

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

[0005] ADAS systems embedded in a vehicle are powered by data obtained from one or more onboard sensors such as, for example, cameras, or by data received from a remote server. For example, this data can be used to detect obstacles in front of the vehicle, such as a pothole, in order to adapt the vehicle's suspension settings 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 start 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] In addition, to improve vehicle reliability and passenger safety, vehicles require regular maintenance. Such operations follow a schedule published by a manufacturer, for example, in which it recommends how often 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 risks, making vehicle maintenance expensive.

[0008] Furthermore, it is not possible for a user to know in advance the driving conditions on upcoming roads. It is therefore difficult to anticipate these conditions in order to avoid certain dangerous roads which could damage the vehicle, or to determine certain parameters associated with the active on-board systems in order to minimize the impact of the driving conditions on the lifetime of the vehicle. Summary

[0009] The following section presents a simplified summary of at least one exemplary embodiment to provide a basic understanding of certain aspects of the present application. This summary is not an exhaustive overview of an exemplary embodiment. It is not intended to identify critical elements of an embodiment. The following summary 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.

[0010] According to a first aspect of the present application, the invention relates to a method for controlling an on-board navigation system of a first vehicle, the method comprising the following steps: - receipt of initial data representative of geographical points of departure and arrival; - determination of a set of routes each comprising a set of road sections according to said first data; - identification of a set of road models associated with all the road sections of each route by feeding said set of road sections into a road layout identification model, said road layout identification model being learned in a learning phase from learning data representative of characteristics of a set of secondary roads and their geographical location obtained from a set of second vehicles, road layout parameters being associated with each road layout of said set of road models; - determination of a wear rate for each route, said wear rate being a function of all road models; - selection of an optimal route based on said wear rate from among said set of routes; - controlling said on-board navigation system based on said optimal route.

[0011] Such a method makes it possible to minimize a wear rate or maximize the lifespan of the vehicle components by selecting the least damaging route from a plurality of routes. Minimizing the wear rate of the vehicle components thus makes it possible to carry out maintenance operations on the vehicle at longer intervals.

[0012] In an exemplary embodiment, the wear rate is determined based on wear rate information associated with each road model of said set of road models, said wear rate information being obtained from said road model identification model.

[0013] In another embodiment, the wear rate is determined according to a wear prediction model receiving as input said road layout parameters associated with each road layout of said set of road models.

[0014] In another embodiment, the method further comprises receiving a set of control parameters from at least one active on-board system for each route, said set of control parameters being determined so as to minimize said wear rate for said set of road models as a function of said road layout parameters.

[0015] In another embodiment, the method further comprises controlling said active on-board system according to said set of control parameters.

[0016] In another embodiment, the wear rate information has a value between 0 and 1.

[0017] In another embodiment, the method further comprises determining distance and duration values ​​for each route, said selection being based on said distance and duration values.

[0018] According to a second aspect of the present application, the invention relates to a device for controlling an on-board navigation system of a vehicle, in which the device comprises a memory associated with at least one processor configured to implement the method in accordance with the first aspect of the present application.

[0019] According to a third aspect of the present application, the invention relates to a computer program product comprising instructions which, when the program is executed by one or more processors, causes one or more processors to execute a method according to the first aspect of the invention.

[0020] According to a fourth aspect of the present application, there is provided a non-transitory storage medium containing program code instructions for executing a method according to the first aspect of the present application.

[0021] 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 apparent from the following description of examples taken in conjunction with the accompanying drawings. Brief description of the drawings

[0022] It will now be necessary to refer, by way of example, to the attached drawings, which show exemplary embodiments of the present application and in which:

[0023] [Fig.l] schematically illustrates a vehicle with its environment according to a particular and non-limiting embodiment of the present invention;

[0024] [Fig.2] illustrates a flowchart of the different stages of a control process of an on-board navigation system of the vehicle of [Fig.l], according to a particular and non-limiting example of the present invention; and

[0025] [Fig.3] illustrates a functional diagram of an exemplary device configured for controlling an on-board navigation system of the vehicle of [Fig.l], in accordance with at least one exemplary embodiment. Description of exemplary embodiments

[0026] 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. An exemplary embodiment, however, may be embodied in numerous alternative forms and should not be construed as limited to the examples set forth herein. Accordingly, it should be understood that there is no intention to limit the exemplary embodiments to the particular forms disclosed. Rather, the disclosure is intended to cover all modifications, equivalents, and alternatives within the spirit and scope of the present application.

[0027] There are methods and devices for controlling an on-board navigation system of a first vehicle. To achieve this objective, data representative of geographical points of departure and arrival are first received, and a set of routes is determined, each route comprising a set of road sections. A set of road layouts associated with all the road sections of each route is identified by feeding all the road sections into a road layout identification model, which is learned in a learning phase from learning data representative of the characteristics of a set of roads and their geographic location obtained from a set of second vehicles, the road configuration parameters being associated with each road configuration of the set of road repetitions.

[0028] A wear rate for each route is determined based on the set of road patterns and an optimal route is selected based on the wear rate from among the set of routes. Finally, the in-vehicle navigation system is controlled based on the optimal route.

[0029] Please note that in the following text, the terms "wear rate", "failure risk", "failure risk", "time to failure" and "lifetime" are used interchangeably, as they relate to the same idea.

[0030] [Fig.l] schematically illustrates an environment 1 comprising a first vehicle 10, according to a particular and non-limiting embodiment of the present invention.

[0031] The first vehicle 10 corresponds for example to a vehicle with a thermal engine, with electric motor(s) or even to a hybrid vehicle with a thermal engine and one or more electric motors, for example a rechargeable 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.

[0032] The first vehicle 10 may be equipped with various advanced driver assistance systems (ADAS) that are configured to assist the driver of the first vehicle 10 in controlling the first vehicle 10. The first vehicle 10 may be a semi-autonomous vehicle or an autonomous vehicle, for example, a vehicle traveling with an autonomy level that is for example greater than or equal to 2 in an autonomy range between 1 and 5, level 1 corresponding to a vehicle having a minimum autonomy level and level 5 corresponding to a vehicle having a maximum autonomy level, for example, a fully autonomous vehicle.

[0033] According to an exemplary embodiment, the first vehicle 10 is equipped with a road condition detection module 11, the objective of which is to process one or more time-stamped input signals, periodic or not, and not necessarily originally aligned 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: - inertial sensors, - wheel speed sensors, - steering wheel sensors, - radars, - lidars, - laser scanners, - the cameras, - acceleration sensors, - vibration sensors, - noise sensors, - rain sensors, - external thermometer and - weather station. Please note that this list is not exhaustive.

[0034] The road condition detection module 11 periodically analyzes the recent portion of the available signals, preprocesses them as needed, for example by sampling, filtering, imputation of missing values ​​or removal of outliers, and aligns them, for example by interpolation, so as to obtain regularly spaced multidimensional data points, called time series, characterizing the evolution of the road characteristics attributable to the location over the distance recently traveled by the first vehicle 10. Each of these time series is, for example, also associated with the vehicle identification number, called VIN number, in order to allow the attribution of derived information to this vehicle at a later stage of the processing.The length of the time series may depend on the vehicle's ability to cache the recent portion of the data and can range from a small value, e.g., the last minute, to the duration of the ignition cycle.

[0035] The first vehicle 10 is equipped with a communication unit, corresponding for example to a communication module of the TCU (Telematic Control Unit), of the ATB (“Autonomous Telematic Box”) or of the RFSB (“Radio Frequency Service Box”). Such a communication unit is advantageously connected to one or more antennas for, for example, transmitting and / or receiving data to and / or from a remote device, for example a data center 120 of the “cloud” 100, or a mobile communication device 11, such as a smartphone or a tablet, via a wireless connection, according to OTA (“Over The Air”) technology for example.The wireless communication between the first vehicle 10 and the data center 120 is established for example via a network infrastructure comprising for example one or more relay antennas or base stations 110, or according to a direct communication mode (for example when the mobile communication device is at a distance from the first vehicle 10 allowing such a communication mode, for example at 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, 3GPP (“3rd Generation Partnership Project”) of the fourth or fifth generation, called 3GPP 4G or 5G, satellite communication such as Starlink®.

[0036] 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.

[0037] According to an exemplary embodiment, the first vehicle 10 is equipped with a second set of detection devices comprising, for example, a thermometer, a pressure sensor, a force sensor or a strain sensor with strain gauge. 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.

[0038] The first vehicle 10 is equipped with a navigation and geolocation system, also called a GNSS (Geolocation and Navigation by a Satellite System) system, for example a satellite positioning system of the Global Positioning System (GPS) type. A geolocation signal is available to provide information on the current position of the first vehicle D. The current position D of the first vehicle 10 is sent, for example, to the data center 120 or to the cloud 100, which allows the mobile communication device 11 connected to the same data center 120 or cloud 100 to collect this current position D to feed a navigation system integrated into this mobile communication device 11.

[0039] The on-board systems, such as the active on-board system, the communication unit, the ADAS and the road condition detection module 11, are each controlled by one or more computers, for example the electronic control unit (ECU). These computers form, for example, a multiplexed architecture for the realization of various services useful for the proper functioning of the first vehicle 10 and for assisting the driver and / or the passengers of the vehicle in the control of the first vehicle 10 via the control of the on-board systems of the first vehicle 10. The computers 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 the ISO 17458 standard), LIN (Local Interconnect Network) or Ethernet (according to the ISO / IEC 802-3 standard).

[0040] A process for controlling an on-board navigation system of the first vehicle 10 is advantageously implemented by one or more processors of one or more on-board computers of the first vehicle 10.

[0041] During a first operation, the computer receives first data representative of the geographical points of departure and arrival A. The departure is, for example, the current position of the first vehicle D. According to another example, the geographical position of the starting point corresponds to an address or to a point of interest, the route being planned even before the first vehicle reaches this starting geographical position.

[0042] According to an exemplary embodiment, the first data is obtained from the navigation system, for example, when a user enters these positions via a human-machine interface.

[0043] In a second operation, a set of routes 101, 102 is determined based on first data. Each route of the set of routes may comprise a set of road sections, which comprises at least one road section. According to the example illustrated in [Fig.l], the set of routes comprises two routes, a first route 101 and a second route 102. The first route 101 comprises the following order of road sections: a first road section 101a, a second road section 101b, a third road section 101c and a fourth road section 101b. The second route 102 comprises the following order of road sections: a first road section 102a and a second road section 102b. However, the example is not limited to a set of two routes, but extends to a set of routes comprising at least two routes, for example 2, 3 or 10 routes.Similarly, the number of road segments for each route is greater than or equal to one, for example 1, 2, 4, 10 or 100 road segments. A road segment is for example an open road, such as a county road or a national road, or an off-road path or trail, that the first vehicle can use.

[0044] In a third operation, a set of road layouts associated with all road sections of each route is identified by inputting all road sections into a road layout identification model, the road layout parameters being associated with each road layout of said set of road layouts. The road model identification model is learned in a training phase from training data representative of the characteristics of a set of secondary roads and their geographical location obtained from a set of secondary vehicles. It should be noted that the road sections mentioned above may also be included in this set of secondary roads.

[0045] Road sections have certain characteristics associated with features. These characteristics include, for example, road roughness, road surface type, slope, oscillations of the road surface, but also curves, intersections, stops, regulations and the environment. The road characteristics are then classified into groups called road models, each road model comprising road model parameters.

[0046] The road map is constructed through road segment analysis. Each road pattern is identified by inputting training data into a model road pattern identification model, also called road pattern identifier. The road pattern 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 pattern being associated with each road pattern.

[0047] During the training phase, appropriate signals indicating various characteristics of the road are collected in a distributed manner from the detection devices of the secondary vehicles, involving all the second vehicles driven on road sections, and over a longer period of time.The training data representative of these signals may be physically hosted in a data center 120 with multiple co-located data processing servers, or in a cloud-based or other suitable distributed infrastructure. The means and protocols for data transmission are not limited and may use any modern technology such as over-the-air (OTA) transmission over wireless or cellular networks.

[0048] For example, collecting training data takes from one to several months, and the training data is collected from thousands of vehicles of different categories, these vehicles comprising a set of detection devices. The aligned multidimensional signals, each from one vehicle of the set of second vehicles, must be transformed into a format that allows robust comparisons between them. Therefore, a generic feature extraction operation is performed on each signal and the outputs are merged into a single data set. Vehicle identification is preserved, e.g., after a feature extraction step, the identity of the vehicle that emitted the source signal is still unambiguously assigned to each output record.For computational efficiency, the above transformation should be performed, and the results persist near the aforementioned signal storage, keeping the data at rest to avoid large data transmission overhead. Therefore, when sufficient data are collected, the hypotheses of some road models in the analyzed signal feature space representing specific named or unnamed classes can be verified with more statistical significance.

[0049] The objective of the road pattern identification model is to repeatedly analyze training data comprising feature-encoded data points to identify coherent groups of road features or, alternatively, to confirm the existence of predetermined categories thereof. If no prior knowledge of the signatures of the road features is available, an unsupervised technique (clustering) is used. Otherwise, the task can be formulated as a pattern matching / classification that will require further training using independent samples of data with ground truth class labels assigned to them. In other words, the pattern identification model aims to find a function mapping an input multidimensional feature X into a cluster identifier or one of the predefined class labels.

[0050] Since the training data includes information about geographic positions, for example latitude, longitude and possibly altitude, the solution to the above clustering or classification problem will to some extent reflect the association with geographic positions on the Earth's surface. The strength of this association will increase with the relative weight assigned to the geographic position variables. In a possible embodiment of this invention, the geographic position variables can be completely separated, and 2-dimensional clustering (or 3-dimensional clustering if altitude is also to be treated as a variable) can be performed only on them in order to first find only those locations visited by many vehicles.Then, the second-step clustering can be performed on the remaining variables, but only in the spatial neighborhood of the most common geographic locations previously found.

[0051] In a fourth operation, a wear rate is determined for each route based on all road models.

[0052] According to an exemplary embodiment, the wear value is determined for a set of components of the first vehicle 10, for example by determining wear rates, each associated with a component of the first vehicle.

[0053] The set of components of the first vehicle 10 comprises, for example, all or part of the following components: - suspension components such as the suspension arm, the spring and / or the shock absorber, - steering components such as the power steering system, the steering shaft and / or the steering rack, and / or - tires, and / or - braking system components such as brake pads and / or brake discs, and / or - engine components, and / or - chassis components, and / or - silent blocks.

[0054] According to a first embodiment, the wear rate is determined based on the wear rate information associated with each road layout of said set of road models, the wear rate information being obtained from the road model identification model. For example, the wear rate information is associated with the road models using tables, such as look-up tables (LUTs).

[0055] According to an exemplary embodiment, the first vehicle 10 is equipped with active on-board systems. Depending on the parameters of these active on-board systems, the wear rate may be different from one set of parameters of the active on-board system to a second set of parameters of the active on-board system. For example, the wear rate of a suspension on bumpy terrain is reduced if the suspension is active and its control parameters are set to make it soft. Thus, the method further comprises, in a fifth operation, receiving a set of control parameters of at least one active on-board system for each route, said set of control parameters being determined to minimize a wear rate of the set of road models according to the parameters of the road model. For example, the values ​​of the control parameters are associated with the road models using tables, such as look-up tables (LUTs) and according to the first type of vehicle.

[0056] According to a second embodiment, a set of control parameters is determined during a seventh operation from a vehicle behavior model associated with the first vehicle 10 receiving as input the parameters of the road configuration associated with the set of road models associated with the set of road sections of each route. The vehicle behavior model is constructed, for example, from CAD files, physical calculations and / or multi-body dynamic simulations. Many models are constructed for different types and dimensions of vehicles. The vehicle behavior model used is the one associated with a vehicle whose characteristics are closest to the characteristics of the first vehicle 10.In particular, the vehicle behavior model is learned or improved using data collected via sensors embedded in the vehicles, for example via detection devices of several vehicles, the detection devices obtaining physical values ​​internal to the vehicles, such as stress, deformation, vibrations, temperature or noise. 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, the first set of physical parameter values ​​being associated with the road layout and the set of control parameters, if any.

[0057] According to an exemplary embodiment, the set of control parameters is further determined following a minimization function minimizing 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 parameters of the wear model. the road.

[0058] According to another exemplary embodiment, the wear rate is determined based on the wear prediction model, receiving as input the road layout parameters associated with each road layout of the set of road models.

[0059] The wear prediction model has, for example, been learned in a learning phase from learning data representative of a first set of historical maintenance data obtained from the set of third-party vehicles, the 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. For example, the wear prediction model is learned by matching the third set of loads or the simulation results with the analysis of the parts, the parts recovered from the vehicles from the set of third-party vehicles.

[0060] With the output of the pattern identification subsystem, e.g., some characteristic historical signals being assigned distinct road patterns that they likely represent, the following method is proposed for estimating the wear rate of vehicles.

[0061] According to an exemplary embodiment, the wear prediction model is powered 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 - cross-sectional fatigue damage calculations.

[0062] For newly designed vehicles, there are no or few historical warranty claim records or maintenance reports from which knowledge could be obtained to guide any statistical learning approach for wear rate estimation. In this case, computer-aided engineering (CAE), virtual simulation, and durability calculation combined with field testing (PG) or road test simulation (RTS) are used to estimate the wear rate of the vehicle system and / or components. For a given vehicle, with a given labeled road layout, along with given operational parameters for steering, braking, speed, and / or suspension, the shear damage attributable to the most recent time period or distance traveled can be calculated.Then it can be correlated with the results of the proving ground (PG) tests or the results of the road test simulation (RTS) obtained over a longer period or mileage and using different models to determine the . Total accumulated durability damage. The total damage can finally be matched with the vehicle / component wear rate and / or the time of the next scheduled maintenance can be estimated. In the wear prediction model, vehicle conditions corresponding to road patterns and vehicle operational parameters are included and considered. Material properties and component / system mechanism can be obtained from physical tests, assumptions, or simulations.

[0063] 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 there is a record of historical warranty claims, maintenance reports or any other document of a similar nature where the facts of repairs or parts replacements carried out on vehicles of a given identity can be unambiguously linked to their root cause. 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.Then, for the same list of vehicles, the algorithm constructs predictor variables based on the information if, when, and / or how often 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, counts per class / cluster, frequencies of occurrence, 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 that they contribute to greater accuracy of the estimated model.

[0064] Accordingly, one or more statistical models are trained to quantify the risk of failure of the vehicle system / component, called wear rate of its use on the roads. In other words, the following hazard functions are learned:

[0065] [Math.l] [Math.l] hrKy = failure 0d)

[0066] where hj is the function of the expected hazard of the occurrence of the j-th type of failure in a predefined time window or distance to be traveled, X is the vector of the predictor variables characterizing the historical state of a vehicle, with at least one dimension of this vector reflecting whether, when and how often it has emitted signals attributed to one of the recognized categories or groups of road models, and Oj is a vector of parameters of the model to be optimized.

[0067] Any state-of-the-art statistical learning approach can be used to estimate the above functions. In addition, the notion of conditional probability of failure of a vehicle system or component can have different, but related, formulations. For example, the function being optimized can integrate the temporal aspect of the prediction directly by modeling:

[0068] [Math.2] P^1failure s or [Math.2] hj^P^failure —

[0069] where the probability that a failure of the j-th type occurs before or after a determined time / mileage t is estimated.

[0070] Once the above functions are estimated, historical data from other vehicles, including the first vehicle 10, that were not seen in the model training stage, can be used to predict if or when a related failure may occur and thus trigger possible mitigation or prevention activities. In addition, for certain classes of learning algorithms, such as logistic regression, ensemble learning algorithms, or the use of agnostic techniques such as LIME or Shapley values, it is possible to quantify the contribution of independent variables to the model's decisions, both at the level of the entire population of vehicles being evaluated and at the level of individual vehicles.

[0071] In order to better support a decision-making process related to the management of predictive maintenance activities of vehicles, a separate embodiment of the present invention comprises an optimization module that quantifies the impact of all the above factors and maps it on a common scale to finally find the best setting of the prediction model in the sense of maximizing the expected gain. In principle, the formula for the expected gain is designed to allow as low a maintenance frequency as possible in order to avoid unplanned reactive maintenance, without incurring excessive costs associated with too much preventive maintenance.

[0072] The proposed new way to perform the optimization of the parameters of the failure and wear prediction model is to connect the cost / desired benefits directly to the loss function optimized by the attrition prediction model in the training process, as well as to the scoring function used to judge the quality of the model on a particular dataset. Specifically, assuming that the cost / benefit factors can each be attributed to true positives (TP), false positives (FP), true negatives (TN), or false negatives (FN), and expressed on a common scale, e.g., an amount of money lost / gained, the following formulas can be used:

[0073] [Math.3] w tp = •> [Math.3] w Y n'TN^TN^ [Math.3] w fp~ [Math.3] WFN ~

[0074] a = ^

[0075] where b; 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 give to reducing the number of false positive cases (vehicles without inherent defect or not subject to aging, still classified as such) than to reducing the number of false negative cases (vehicles having the true problem but classified as without problem). With the above, the wear prediction model is trained using a weighted binary cross-entropy loss function:

[0076] [Math.4] L = E.- av Jog(p.)-( 1 -y.)log( 1 -p.)

[0077] where y, are the ground truth class labels (1 for a vehicle requiring maintenance or 0 otherwise) and P, are the probabilities of each training set vehicle requiring maintenance as evaluated by the model. In addition, when evaluating each validation dataset during the routine model cross-validation procedure, the following scoring function may be used:

[0078] [Math.5] S = w) + » 1 l rl 1 j \ i 7

[0079] where:

[0080] _ 1 if the i-th instance is a true positive 1 0 otherwise

[0081] _ fl if the i-th instance is a false positive 1 l 0 otherwise

[0082] ^oi) _ fl if the i-th instance is a false positive 1 t 0 otherwise

[0083] ^i(0) _ p if the i-th instance is a false negative 1 1 0 otherwise

[0084] With such a scoring function, the wear prediction model will automatically favor parameter sets that maximize the hypothetical seal gain and minimize the hypothetical seal loss, regardless of which factors contribute most and in which direction.

[0085] According to an exemplary embodiment, the wear rate information has a value between 0 and 1.

[0086] According to another embodiment, the method further consists in determining, in an eighth operation, the distance and duration values ​​of each route, by any method known to those skilled in the art.

[0087] In a ninth operation, an optimal route is selected based on the wear rate, and optionally based on the distance and duration values, from among the set of routes. For example, if only the wear rate is considered, the route with the lowest wear rate is selected. In another example, a route is selected based on various input values, including the wear rate and, optionally, the distance and duration values, using a function weighting each of the input values.

[0088] Since the statistical model described above can be trained to predict the failure of a given component or the time remaining until failure based on the use of the vehicle on roads with different road patterns and, together with the failure risk score, can also provide the estimated average contribution of each input predictor variable related to the occurrence of a particular road pattern to the model's decision, these latter contributions can be used to quantify the negative impact on the vehicle 10 that following different routes between the geographical start and end points can have. This makes it possible to guide a route planner but also to justify why an alternative route was not selected during the selection operation, for example by sending a message to a user of the first vehicle describing why The alternative route has not been selected. Such a message may indicate, for example, that the alternative route was not chosen because the road conditions are poor, or that the first vehicle encountered potholes that could damage its suspension.

[0089] More specifically, a separate embodiment of the invention comprises a module that allows increasing the preventive capabilities of the presented system. The module depends on the existence of pre-classified road patterns that, together with their geographical location, have been stored in a pattern identification model. A periodic batch process is executed to efficiently traverse all possible paths between each pair of feature points within the known road network. Along each path, the number of discovered occurrences of each pre-classified road pattern is counted and kept.

[0090] A vehicle that needs to determine the best route from departure to arrival of the geographical points to be chosen can establish the connection with the database storing the road network map, the model identification model, and download it. Then, when requested in advance to do so, apply an appropriate shortest path algorithm to a pair.

[0091] It can determine the path from A to B that directly minimizes the wear rate penalized by the route length / failure risk model response as: — 52 hA^B indicates the estimated failure risk of the model associated with each j-th failure type along the i-th route considered from A to B, when given as input a vector of variables characterizing the number of different road patterns known to exist along that route. is the appropriately scaled length or travel time of the route considered. It should be noted that in the summation of the above formula, the individual components associated with the different failure types considered may be added together, taking into account additional weights reflecting the severity of those failure types or their a priori known frequency of occurrence in real life.

[0092] Alternatively, the optimal path from A to B may be chosen by minimizing the total weighted risk of the route penalized by the route length, expressed as follows

[0093] = + where ck denotes the number of k-th type road patterns known to be present on the path considered from point A to point B. H>k indicates the relative (normalized) weight of the variable coding the frequency of occurrence of the k-th type road patterns. k-th type on the model response, averaged across all models if more than one failure-type-specific model is evaluated. In addition, shortest path calculation can be performed in advance and upon boarding, and at the start of each trip, the vehicle can download the road network graph along with the already pre-calculated costs of each possible route to reduce the amount of on-board processing required for simple search operations.

[0094] The algorithm proposed above aims to choose routes with the least possible road conditions that have the most overall negative impact on the durability of the first vehicle. At the same time, for obvious reasons, the penalty term in the above objective function is necessary to balance the impact of road quality on the future cost of car maintenance and more fundamental criteria given that drivers are primarily focused on finding the shortest or fastest route. More generally, the above optimization function should only be considered as an exemplary formulation of it. In practice, the function may take other similar forms.For example, it may be nonlinear or include additional penalty terms that encompass other typical route planning criteria, such as the presence of toll roads on the route. Furthermore, we do not in any way restrict the range of mathematical / numerical optimization routines that could be used to solve the above minimization problem.

[0095] In a tenth operation, the on-board navigation system is controlled according to the optimal route selected from the set of routes. For example, certain messages or graphic objects are displayed on the screen of a vehicle, or audio instructions are issued via an on-board audio system.

[0096] According to an exemplary embodiment, the method further comprises controlling the active on-board system based on the set of control parameters. For example, for a first vehicle 10 with Level 1 or higher autonomous driving capabilities or with an active suspension system, the autonomous driving system or the active suspension system may be used and adjusted to reduce structural damage caused by the different road conditions that the first vehicle encounters while following the selected route. The speed, steering wheel angle, acceleration, deceleration, predefined driving mode, suspension height and anti-roll stiffness, shock absorber damping coefficient and / or stabilizer bar may all be modified and optimized based on the different road patterns.

[0097] Optionally, during an eleventh operation, a maintenance operation is planned in function of the wear rates of all at least one component of the first vehicle 10. Capable of reasoning on the expected time to failure, the process aims to facilitate decision-making when it comes to managing the cost of the first vehicle 10 maintenance or planning the supply of spare parts. An estimate of the vehicle condition model is used directly to drive the selection of vehicles to be checked.

[0098] For example, vehicles that have been assigned a failure risk or cumulative damage level above a predefined threshold or those with an expected failure time 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 perform a technical inspection of the first vehicle 10, but this is subject to customer agreement. If the affected component is less critical, information about the predicted problem may be used in many other ways, for example to put the affected vehicle part or system on the to-do list for the next regular inspection or maintenance.Furthermore, the possible repair or replacement of a component is conditioned by the confirmation of the existence of a defect or the aging effect of a general component once the first vehicle 10 has already been recalled for inspection.

[0099] This method makes it possible to plan the route of a vehicle via an on-board navigation system in such a way as to minimize wear and tear on the vehicle's components. The service life of these components is thus increased and the cost of servicing or maintaining the vehicle is reduced.

[0100] [Fig. 3] shows a block diagram illustrating an exemplary system or device 3 in which various exemplary aspects and embodiments are implemented. The device 3 controls an on-board navigation system of a vehicle, for example of the first vehicle 10, in accordance with at least one exemplary embodiment.

[0101] The device 3 may be integrated as one or more devices comprising the various components described below. In various embodiments, the device 3 may be configured to implement one or more of the aspects described in the present application.

[0102] Examples of equipment that may constitute all or part of the device 3 include a calculator, an electronic control unit (ECU), a data processing device, or other communication devices. The elements of the device 3, alone or in combination, may be incorporated into a single circuit integrated circuit (IC), multiple ICs and / or discrete components. For example, in at least one embodiment, the processing and encoder / decoder elements of the device 3 may be distributed across multiple integrated circuits and / or discrete components. In various embodiments, the 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.

[0103] The device 3 may include at least one processor 30 configured to execute instructions loaded therein to implement, for example, the various aspects described in the present application. The processor 30 may include an integrated memory, an input and output interface, and various other circuits known in the art. The device 3 may include at least one memory 31 (e.g., 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, magnetic disk drive, and / or optical disk drive.The storage device may include an internal storage device, an attached storage device, and / or a network accessible storage device, by way of non-limiting examples.

[0104] 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 the module(s) that may be included in a device to perform the encoding and / or decoding functions. As is known, a device may include either or both of the encoding and decoding modules. In addition, the encoder / decoder module may be implemented as a separate element or may be incorporated into the processor 30 as a combination of hardware and software known to those skilled in the art.

[0105] The program code to be loaded onto the processor 30 or the encoder / decoder to perform the various aspects described in the present application may be stored in a storage device and then loaded onto the memory 31 for execution by the processor 30. In accordance with the various embodiments, one or more of the processors 30, memory 31, storage devices and encoder / decoder modules may store one or more various elements during execution of the processes described in the present application. These stored elements may include, but are not limited to limit, data representative of audio content, data representative of video content, data related to haptics, a bitstream, matrices, variables and intermediate or final results of the processing of equations, formulas, operations and operational logic.

[0106] In several embodiments, memory within the processor 30 and / or the encoder / decoder module may be used to store instructions and provide working memory for processing that may be performed during data processing, encoding, or decoding.

[0107] In other embodiments, however, memory external to the processing device (e.g., 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, e.g., 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 processing the data.

[0108] [Fig.2] shows a functional diagram of the steps of a method 2 for controlling an on-board navigation system of a vehicle, for example of the first vehicle 10, in accordance with at least one exemplary embodiment.

[0109] The method can, for example, be implemented by an on-board device of the first vehicle 10 or by the device 3 of [Fig.3].

[0110] Firstly, 21 we receive the first data representative of geographical points of departure and arrival.

[0111] In a second step 22, a set of routes is determined according to of first data, each route 101, 102 of the set of routes comprising a set of road sections 101a, 101b, 101c, 101b, 102a, 102b.

[0112] In a third step 23, a set of road layouts associated with all the road sections of each route is identified by entering all the road sections into a road layout identification model, the road layout parameters being associated with each road layout of said set of road layouts. The road model identification model is learned in a learning phase from learning data representative of the characteristics of a set of secondary roads and their geographical location obtained from a set of secondary vehicles.

[0113] In a fourth step 24, a wear rate is determined for each route based on all the road models.

[0114] In a fifth step 25, an optimal route is selected from among all the routes based on the wear rate.

[0115] In a sixth step 26, the on-board navigation system is controlled in optimal route function.

[0116] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. It will be further understood that the terms "comprises / includes" and / or "including / comprising", when used in this specification, may specify the presence of stated features, elements and / or components, for example, but do not exclude the presence or addition of one or more other features, elements, components and / or groups thereof. In addition, when an element is said to be "responsive" or "connected" to another element, it may be directly responsive or connected to the other element, or intermediate elements may be present. In contrast, when an element is said to be "directly responsive" or "directly connected" to another element, there are no intermediate elements present.

[0117] It should be noted that the use of any of the symbols / terms " / ", "and / or" and "at least one of the terms", for example, in the cases of "A / B", "A and / or B" and "at least one of 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 A, B and C", such wording 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 and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, to as many items as there are enumerations.

[0118] Different numerical values ​​may be used in the present application. The specific values ​​may be exemplary and the aspects described are not limited to these specific values.

[0119] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, those elements are not limited by those 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.

[0120] The reference to “an exemplary embodiment” or “an embodiment exemplary embodiment” or “an implementation” or “an implementation,” and other variations thereof, is frequently used to mean that a particular feature, structure, characteristic, etc. (described in connection with the embodiment / implementation) is included in at least one embodiment or implementation. Thus, occurrences of the phrase “in an exemplary embodiment” or “in an exemplary embodiment” or “in an implementation” or “in an implementation,” and any other variations thereof, appearing at various places in this application do not necessarily all refer to the same embodiment.

[0121] Similarly, reference herein to "in accordance with an exemplary embodiment / example / implementation" or "in an exemplary embodiment / example / implementation", as well as other variations thereof, is frequently used to indicate that a particular feature, structure, or characteristic (described in connection with the exemplary embodiment / example / implementation) may be included in at least one exemplary embodiment / example / implementation.Thus, occurrences of the phrase "in accordance with an exemplary embodiment / example / implementation" or "in an exemplary embodiment / example / implementation" at various locations in the specification do not necessarily all refer to the same exemplary embodiment / example / implementation, and separate or alternative exemplary embodiments / examples / implementations are not necessarily mutually exclusive of other exemplary embodiments / examples / implementations.

[0122] The reference numerals in the claims are given for illustrative purposes only and have no limiting effect on the scope of the claims. Although not explicitly described, the embodiments / examples and variations present may be used in any combination or subcombination.

[0123] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, elements of different implementations may be combined, supplemented, modified, or deleted to produce other implementations. In addition, a person of ordinary skill in the art will understand that other structures and processes may 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. Accordingly, these and other implementations are contemplated in this application.

Claims

Claims

1. Method for controlling an on-board navigation system of a first vehicle (10), the method comprising the following steps: - receiving (21) first data representative of geographical points of departure and arrival (A); - determining (22) a set of routes (101, 102) each comprising a set of road sections (101a, 101b, 101c, 101b; 102a, 102b) according to said first data;- identification (23) of a set of road models associated with all the road sections of each route by feeding said set of road sections into a road route identification model, said road route identification model being learned in a learning phase from learning data representative of characteristics of a set of secondary roads and their geographical location obtained from a set of second vehicles, road route parameters being associated with each road route of said set of road models; - determination (24) of a wear rate for each route, said wear rate being a function of all the road models; - selection (25) of an optimal route as a function of said wear rate from said set of routes; - control (26) of said on-board navigation system as a function of said optimal route.;

2. The method of claim 1, wherein said wear rate is determined based on wear rate information associated with each road model of said set of road models, said wear rate information being obtained from said road model identification model.

3. The method of claim 1, wherein said wear rate is determined according to a wear prediction model receiving as input said road layout parameters associated with each road layout of said set of road models.

4. A method according to any one of claims 1 to 3, further comprising receiving a set of control parameters of at least one active on-board system for each route, said set of control parameters being determined so as to minimize said rate wear for said set of road models as a function of said road layout parameters.

5. The method of claim 4, further comprising controlling said active on-board system based on said set of control parameters.

6. A method according to any one of claims 1 to 5, wherein said wear rate information has a value between 0 and 1.

7. A method according to any one of claims 1 to 6, further comprising determining distance and duration values ​​for each route, said selection (25) being based on said distance and duration values.

8. A computer program product comprising program code instructions for executing the method according to any one of claims 1 to 7, when said program is executed on a computer.

9. Device (3) controlling an on-board navigation system of a vehicle, wherein said device comprising a memory (31) associated with at least one processor (30) configured to implement the method according to any one of claims 1 to 7.

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

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