Method for contextualising cartographic data

Abstract

The invention relates to a method for contextualising cartographic data implemented by a moving vehicle including at least one sensor and a navigation system, the navigation system being configured to acquire cartographic information with associated cartographic positions, the method including: - a plurality of acquisitions by the navigation system, at different acquisition instants, of one cartographic datum among the cartographic information; - for each acquired cartographic datum, determination of a quantity representative of the cartographic datum at a location of the vehicle substantially equal to the cartographic position associated with the acquired cartographic datum; - determination of a polynomial equation of correlation between the cartographic datum and the quantity representative of the cartographic datum; and - automatic contextualisation of the cartographic datum at each acquisition by the navigation system according to the determined correlation equation.

Description

[0001] Description

[0002] Title: Process for contextualizing cartographic data

[0003] Technical field

[0004] The present invention relates to a method for contextualizing cartographic data by a moving vehicle and a vehicle comprising a computer program product configured to implement a method for contextualizing cartographic information by a moving vehicle.

[0005] Prior art

[0006] Current automotive navigation systems generally integrate external map information. This information comes in particular from variable resolution maps ("SDmap" or "HDmap") stored on remote servers and accessible via an internet connection, or from other vehicles via a communication system allowing vehicles to exchange information with each other, for example via V2V ("Vehicle to Vehicle") or V2X ("Vehicle to Everything") protocol.

[0007] Usually, this information allows for better visibility or information on the vehicle's environment, particularly the environment in front of the vehicle, and is therefore more commonly referred to as electronic horizon information (otherwise known as "eHorizon") of the vehicle. It makes it possible to supplement and / or anticipate information from the vehicle's sensors, for example a camera, radar, lidar or sonar.

[0008] This data includes, for example, map information relating to the characteristics of a road, the characteristics of a predetermined route, road signs, and / or external conditions.

[0009] Taking this information into account allows for anticipation of the situations that the vehicle will encounter, and thus an adaptation in anticipation of the dynamic behavior of the vehicle.

[0010] However, electronic horizon information from external information or directly from other vehicles is pre-recorded data under specific driving conditions. The conditions in which the vehicle is located may be different from those in which the pre-recorded data was recorded. This may generate a safety risk for users if the pre-recorded data is used without taking the context into account for vehicle control. For example, the roughness or grip of a road may vary depending on the outside temperature or humidity, and the pre-recorded data, if it does not take into account a low temperature or high humidity, may be considered high while the outside conditions make it low.This can generate a risky vehicle control in the case of low grip or roughness related to weather conditions, particularly when the control concerns the control of one or more actuators. It is then necessary to contextualize the pre-recorded data from the navigation system and providing information on the road downstream of the vehicle to enable reliable data consistent with the particular context in which the vehicle is moving. By "actuator", we mean a device configured to control alone or in combination with other actuators the dynamic behavior of said vehicle according to instructions determined by an algorithm, in particular by influencing the brakes, the engine, the steering and / or the speed of the wheels. It is indeed essential to control the actuators with reliable data.

[0011] Today, the control of actuators, in particular by the chassis system (otherwise called "Chassis Domain") or the micro-powertrain (otherwise called "Powertrain Domain"), is essentially reactive, in the sense that the information taken into account by said systems to generate the control data is previously calculated or generated by means of direct physical measurements ("sensing") or indirect ("virtual sensing") of the vehicle. Taking into account at least partially the cartographic information provided by the navigation system to have proactive control of the actuators, that is to say in an anticipated manner with respect to the occurrence of a future event on the predicted path of the vehicle, would be interesting in that it would make it possible to anticipate the behavior of the vehicle and to act on the actuators in anticipation to improve the reaction of the vehicle to the event.For example, if a vehicle is traveling on a road and receives map information relating to the value of the friction coefficient of the road downstream of the vehicle, it could be possible to anticipate the drift of said vehicle following a sudden reduction in grip by modifying the instructions of one or more actuators of the vehicle's wheels, in particular a yaw instruction. This improves the reaction of the vehicle and the comfort of the driver. However, taking into account such map information requires having reliable information from the navigation system and substantially accurate values.

[0012] There is therefore a need to contextualize cartographic information from external sources and received in a vehicle in a sufficiently reliable and continuous manner over time, in particular to allow the use of cartographic information in the control of actuators, in particular by the chassis system or the micro-powertrain.

[0013] Detailed description

[0014] The invention meets this need by a method for contextualizing cartographic data implemented by a moving vehicle comprising at least one sensor and a navigation system, the navigation system being configured to acquire from the outside, at an acquisition time, the location of the vehicle at the acquisition time and cartographic information with their associated cartographic positions, the method comprising a plurality of acquisitions by the navigation system, at different acquisition times, of a cartographic datum from among the cartographic information with the cartographic position associated with said cartographic datum, the cartographic positions associated with said acquired cartographic data being geographically different from each other,

[0015] The determination, for each acquired cartographic data item, of a quantity representative of the cartographic data item determined from at least one measurement by the at least one sensor at a location of the vehicle substantially equal to the cartographic position associated with said acquired cartographic data item,

[0016] The determination, from at least a part of the acquired cartographic data and the corresponding representative quantities determined, of a polynomial correlation equation between the cartographic data and the representative quantity of the cartographic data of a rank lower than a predefined rank and of maximum correlation index with the acquired cartographic data and the corresponding representative quantities having served to determine the polynomial correlation equation, The automatic contextualization of the cartographic data at each acquisition by the navigation system according to the correlation equation determined to generate contextualized cartographic data if the correlation index is greater than a predetermined threshold correlation value.

[0017] By "associated map position", it is understood that the navigation system acquires the map position corresponding to the position of each map information on a predetermined path. The navigation system can acquire a map position per acquired map information or acquire a map position for a plurality of acquired map information located at the same place on a path predetermined as being the most probable. The map position can comprise the geographical location of a point where the map information is located at the time of acquisition relative to a fixed or mobile reference point. This geographical location can be determined relative to a fixed system, in particular a geodetic system, for example the WGS 84 system associated with the GPS positioning system.Alternatively, it is determined relative to a mobile system, in particular relative to the vehicle, the geographical location then being the position at the time of acquisition of the cartographic information(s) acquired relative to the vehicle. The cartographic position may include GPS coordinates, latitude, longitude and altitude coordinates, coordinates relative to the vehicle, a distance relative to the vehicle on the most probable path or any other means making it possible to position the cartographic data on a map or relative to the vehicle.

[0018] By "acquisition" we mean that at an acquisition time, the navigation system receives a data stream containing the map information and the associated map positions from a remote server through an internet network, in particular a cloud, or from another vehicle.

[0019] By "representative quantity" is meant a quantity determined from a measurement of at least one sensor which, when the cartographic data is accurate and reliable, has a relationship with the acquired cartographic data. The relationship is in particular an equality, a fixed difference or a proportionality.

[0020] By "substantially equal" is meant that the location of the vehicle, when the determination of the representative quantity is carried out, is equal to the map position of the acquired data with a predefined acceptable margin of error. The acceptable margin of error may correspond to an error in the measurement time less than or equal to 0.5 s, better still less than or equal to 0.1 s. The acceptable margin of error may correspond to a distance less than or equal to 50 m, better still less than or equal to 10 m, even better still less than or equal to 5 m, even better still less than or equal to 2 m, for example substantially equal to 1 m.

[0021] The invention makes it possible, by comparing the representative quantities of the cartographic data corresponding to correct data determined by the vehicle at its position and cartographic data from acquisitions by the navigation system corresponding to raw data devoid of context or taken in a particular context at the same location, to determine a simple equivalence relationship which can be applied to the cartographic data at positions downstream of the vehicle from acquisitions by the navigation system to have cartographic data corresponding to the representative quantity which are reliable for taking into account in the vehicle and anticipated over a more or less distant duration or distance depending on the cartographic data.

[0022] It is then possible to use this contextualized map data in several ways, in particular in vehicle control, user alerting or enriching a contextualization database as we will see later. This makes it possible to anticipate vehicle control based on the contextualized map data.

[0023] Such a method is thus particularly suitable for the use of proactive algorithms - unlike state-of-the-art algorithms which are essentially reactive - making it possible in particular to adapt the parameters of a regulation algorithm of one or more actuators by distributing the instructions differently to the actuators according to acquired mapping information. A system defined as such is thus made more efficient.

[0024] The polynomial correlation equation can characterize a difference between a cartographic datum and a representative quantity which is a function of the context in which the vehicle is moving, in particular meteorological conditions such as rain or snow, temperature, humidity or wind, sensor parameters, in particular their calculation units which may differ from the unit of the cartographic data, characteristics specific to the vehicle, driving habits or any other context which could generate a difference between a cartographic datum and a quantity representative of the cartographic datum.

[0025] Preferably, the method comprises the navigation system determining the most probable path of the vehicle based on user input data, information acquired from one or more sensors of the vehicle and / or the location of the vehicle determined by the navigation system. The most probable path of the vehicle may be a route validated by the user or a route corresponding to a journey determined as being the most probable in view of the location of the vehicle and one or more data of the vehicle or the user.

[0026] The acquired map information is associated with a map position on or along the determined most probable path.

[0027] The navigation and location system preferably has a GNSS receiver, for example GPS type, and allows the driver to enter a route for the vehicle and / or the latter to know its position in real time.

[0028] The navigation system may acquire the map data and / or map information from a remote server through an internet network, including a cloud, or from another vehicle, for example by V2V (“Vehicle to Vehicle”) or V2X (“Vehicle to Everything”) protocol.

[0029] Information

[0030] The cartographic information may include: one or more characteristics of a road, in particular the coefficient of adhesion and / or the roughness of the road, the type of road, and / or one or more characteristics of a predetermined route, in particular the curvature of a bend, the slope of the road, the presence of obstacles on the road, in particular the presence of speed bumps, and / or road signs, in particular the presence and identification of road signs or lines on the ground, and / or external conditions, in particular weather or temperatures, or localized information on the maximum vehicle size or the maximum vehicle mass authorized on a road.

[0031] Data

[0032] The map data can be any of the information described above.

[0033] Preferably, the map data provides information relating to a physical or structural property of the road on the most probable path determined by the navigation system exhibiting variability depending on the external context. The map data may be chosen from all map information exhibiting such variability, in particular one or more physical characteristics of the road, for example the coefficient of adhesion and / or the roughness of the road.

[0034] Preferably, it is chosen from information comprising a numerical value, preferably substantially continuous over a path. It can be chosen from the curvature of a bend comprising, for example, a value of the local curvature angle of the road, the slope of a road comprising a value of the local inclination angle of the road, the adhesion coefficient of the road comprising a value of the local adhesion coefficient of the road, the roughness coefficient of the road comprising a value of the local roughness coefficient of the road.

[0035] The fact that the data is substantially continuous along the path allows for sampling of acquisitions and easy detection of an erroneous acquisition which would have to be discarded.

[0036] The cartographic data may include the numerical value and one or more labels identifying the nature of the numerical value. The label(s) may characterize the unit of the numerical value and / or the nature of the cartographic data, for example a coefficient of adhesion, roughness, a slope, an angle of curvature.

[0037] The method may comprise, at each acquisition, the acquisition of a plurality of different cartographic data from among the aforementioned cartographic information, each associated with a cartographic position, said cartographic positions being identical or not. The method may then determine for each cartographic data a polynomial correlation equation on a part of the plurality of acquisitions. Quantity

[0038] The representative quantity may be a value derived from a measurement by one or more sensors on board the vehicle. The value may be obtained by a direct measurement from a vehicle sensor or deduced from one or more measurements from one or more sensors.

[0039] The representative quantity can be determined using a single sensor.

[0040] The method may include determining the distance on the most likely path between the vehicle at the acquisition time and the map position associated with the map data acquired at the acquisition time. Alternatively, this information is acquired directly with the map data for each acquired map data.

[0041] The determination of the representative quantity for each acquired map data may include the determination, as a function of the speed of the vehicle at the acquisition time, of the measurement time so that the location of the vehicle at the measurement time is substantially equal to the map position associated with the map data. The measurement time may be determined at least from the distance on the most probable path between the vehicle at the acquisition time and the map position associated with the map data acquired at the acquisition time and the speed of the vehicle at the acquisition time. The determination of the measurement time may also be done dynamically by integrating any variations in the speed of the vehicle between the acquisition time and the measurement time to improve said determination.

[0042] Determining the time of measurement of the representative quantity by calculation allows for a relatively precise and reliable process. In practice, since the vehicle's geolocation is not available continuously, but periodically according to a given geolocation update frequency, this method ensures that the measurement is carried out at the best time, for example between two acquisitions by the navigation system.

[0043] Alternatively, the determination of the measurement time can be done by acquiring the location of the vehicle periodically and triggering the measurement when the location of the vehicle identified by the periodic acquisition is closest to the map position acquired with the map data.

[0044] The acquisition of map data can be successive in time or based on the vehicle's position on its route. Each acquisition can be triggered automatically or manually.

[0045] Map information can be acquired by the navigation system at a predefined acquisition frequency, particularly when the vehicle is in motion.

[0046] The acquisitions of the cartographic data may be sampled in time or on the positioning of the vehicle on its route at a predetermined sampling frequency identical to or different from the frequency of acquisition of the cartographic information, in particular at a sampling frequency corresponding to an acquisition of the cartographic data every N acquisitions of cartographic information by the navigation system, N being an integer. The acquisition times may be spaced apart by a substantially constant duration or are spaced apart by a duration corresponding to a substantially constant distance traveled by the vehicle.Alternatively, the method may include searching for the map data each time map information is received and acquiring the map data if it is present at a map position at a distance from the vehicle on the most likely path less than or equal to a predetermined distance and if it has not been previously acquired at a previous acquisition time.

[0047] The acquired map positions are at a distance and / or travel time relative to the vehicle that is not zero at the corresponding acquisition time. Preferably, the distance to the vehicle is less than or equal to a predetermined vehicle distance, in particular less than or equal to 500m, better still less than or equal to 100m and / or greater than or equal to 1m, better still greater than or equal to 5m.

[0048] The distance, particularly along the most likely path, between the acquired map positions and the vehicle locations at the corresponding acquisition time may be substantially constant. Alternatively, said distance is not constant.

[0049] The determination of the confidence index can be done on at least a part of the cartographic data acquired over a predetermined acquisition duration, and / or a predefined number of acquisitions. The acquisition duration can be predetermined according to the cartographic data. The number of acquisitions can be predefined according to the cartographic data, in particular according to the frequency of appearance of the cartographic data in the case of cartographic data characteristic of a discrete event.

[0050] Preferably, the determination of the confidence index is done at an evaluation time and takes into account at least the cartographic data acquired at the acquisition time upstream in the time closest to the evaluation time.

[0051] The determination of the polynomial correlation equation can be done on at least a part of the cartographic data acquired over a predetermined acquisition duration, and / or a predefined number of acquisitions. The acquisition duration can be predetermined according to the cartographic data. The number of acquisitions can be predefined according to the cartographic data, in particular according to the frequency of appearance of the cartographic data in the case of cartographic data characteristic of a discrete event.

[0052] Preferably, the determination of the correlation equation is done at an evaluation time and takes into account at least the map data acquired at the acquisition time upstream in the time closest to the evaluation time.

[0053] Preferably, the determined correlation polynomial equation is of rank less than 1, that is to say is a proportionality between the cartographic data and the quantity representative of said cartographic data and / or a constant shift between the cartographic data and the quantity representative of said cartographic data. By "proportionality", we mean a relationship of the type y=cx, y being the representative quantity, x the acquired cartographic data and c a proportionality factor, c can take any value, in particular be substantially equal to 1. By "constant shift", we mean a relationship of the type y=x+b, y being the representative quantity, x the acquired cartographic data and b a constant value.

[0054] Window

[0055] Preferably, the determination of the correlation index is carried out at a correlation time by comparing at least a portion of the differentials, better all the differentials, corresponding to at least a portion of the cartographic data acquired upstream of the correlation time over a time window of a predetermined acquisition duration ending at the correlation time or corresponding to a predetermined number of successive cartographic data acquisitions going back in time from the correlation time.This makes it possible to determine the correlation index over a sliding time window upstream of the correlation instant corresponding to a constant predefined duration or a constant number of acquisitions and thus to have an up-to-date correlation index over a predetermined number of cartographic data to be both sufficiently large to allow stability of the determination and at the same time sufficiently low to limit the risk of error in the evaluation of the correlation.

[0056] The correlation time can be the same as the evaluation time and / or the predefined correlation duration can be the same as the acquisition time or the number of map data considered identical.

[0057] Correlation

[0058] The method may comprise the determination of differentials each corresponding to a difference between the acquired cartographic data and the corresponding representative quantity and the comparison between them of the differentials of the cartographic data for determining the polynomial correlation equation, the comparison of the differentials comprising the determination of the polynomial correlation equation

[0059] Preferably, the correlation index is defined such that the smaller the deviations between the map data acquired after contextualization and the corresponding representative quantities, the larger the index, and vice versa. Thus, the correlation index characterizes the quality of the correlation model determined from the polynomial correlation equation.

[0060] The method may comprise determining a correlation index associated with the or each cartographic data item acquired after determining the polynomial correlation equation corresponding to the correlation of the or each cartographic data item acquired after determining the polynomial correlation equation and the corresponding representative quantity with the polynomial correlation equation. This index may be determined from the difference between the cartographic data item acquired after contextualization and the corresponding representative quantity. Such an index makes it possible to characterize for each cartographic data item the compliance or non-compliance with the determined polynomial equation. Preferably, the correlation index is defined such that the smaller the difference between the cartographic data item acquired after contextualization and the corresponding representative quantity, the larger the index, and vice versa.Thus, the correlation index characterizes the quality of the contextualization carried out from the polynomial correlation equation.

[0061] The method may comprise determining a new polynomial correlation equation if the correlation index is lower than the correlation threshold value over a period greater than a predetermined period, in particular the acquisition period, or over a number of successive acquisitions greater than a predetermined number, or if the correlation indices of the map data acquired over a period greater than the predetermined period, in particular the acquisition period, or over a number of successive acquisitions greater than the predetermined number are all lower than a threshold index value.

[0062] The method may comprise, as long as the correlation index is greater than the threshold correlation value or the correlation indices of the acquired cartographic data are greater than a threshold index value, the use of the cartographic data acquired after contextualization, in particular by enriching a database using this cartographic data or generating control data at least as a function of the cartographic data acquired after contextualization, the control data being in particular input data of a user information device or of an actuator of the vehicle.

[0063] The method may comprise determining context data from one or more vehicle sensors or external data and associating the context data with the map data acquired after contextualization and / or with the determined correlation equation, the context data including in particular the presence of rain or not and / or its intensity, the temperature, the humidity level, the wind speed and / or the dew point.

[0064] The method may include sending contextualization information to a database, in particular internal or external, the contextualization information comprising the context data and / or the combination of the cartographic data acquired after contextualization and the determined correlation equation. Preferably, the method for determining the polynomial equation is chosen from polynomial interpolation, in particular Lagrangian, and polynomial regression. In particular, the polynomial regression may be carried out by the least squares method, maximum likelihood, by Bayesian inference, or by machine learning methods such as, for example, support vector machines (SVM).

[0065] Preferably, the correlation index is chosen from the Bravais-Pearson coefficient, the Spearman rho coefficient, the Goodman and Kruskal gamma coefficient, or the Kendall tau coefficient.

[0066] The threshold correlation value may depend on contextual parameters.

[0067] Preferably, the threshold correlation value depends on the type of map data. For example, the threshold correlation value associated with map data relating to temperature is different from that associated with map data relating to the road adhesion coefficient.

[0068] The threshold correlation value may depend on the number of map data sampled to determine the correlation polynomial equation.

[0069] Discarding of clearly erroneous data

[0070] The method may include, for each pair of cartographic data and corresponding representative quantity, the removal of a clearly erroneous data item in the determination of the polynomial correlation equation having a maximum correlation index with the data and representative quantities taken to search for the equation.

[0071] Preferably, a data item is considered manifestly erroneous if its correlation index is greater in absolute value than a predetermined error threshold and the closest cartographic data have correlation indices lower in absolute value than a predetermined error threshold.

[0072] The elimination of clearly erroneous data allows for a more accurate and stable determination. The at least one vehicle sensor may be external and take measurements of physical parameters, objects or infrastructure external to the vehicle.

[0073] Conversely, the at least one sensor may be of the internal type and carry out measurements relating to physical parameters or objects internal to the vehicle, in particular tire pressure, the angle of rotation of the wheels or even the pressure in the passenger compartment.

[0074] The at least one sensor is preferably chosen from odometers, gyrometers, accelerometers, thermocouples, RTDs, thermistors, barometers, cameras, sonars, radars or even Lidars or a combination of these sensors.

[0075] Actuator

[0076] Preferably, the actuator is configured to control alone or in combination with other actuators the dynamic behavior of said vehicle, in particular by influencing the brakes, the engine, the steering and / or the speed of the wheels, depending at least on the control data.

[0077] The actuator can be configured to control the chassis, in particular the suspensions, the torque setpoint motorization, in particular the thermal or electric engine, the wheel motors, the brakes, or even the noise cancellation systems inside the vehicle (from the English "active noise control" or ANC).

[0078] Vehicle

[0079] The invention also relates to a vehicle equipped with at least one sensor and a navigation system allowing the acquisition at an acquisition time of cartographic information with their associated cartographic position and the location of the vehicle at the acquisition time, said vehicle further comprising a computer program product configured so that, when the vehicle is moving: locate the vehicle and acquire cartographic information, carry out a plurality of acquisitions by the navigation system, at different acquisition times, of a cartographic datum from among the cartographic information and of the cartographic position associated with said cartographic datum, the acquired cartographic positions being different from each other, determine, for each acquired cartographic datum,a quantity representative of the cartographic data from at least one measurement by the at least one sensor at a location of the vehicle calculated to be substantially equal to the associated cartographic position at which said cartographic data is acquired, determining, for each cartographic data acquired, a differential for each cartographic data between the cartographic data acquired and the corresponding quantities determined by measurement and comparison of the differentials of the successive cartographic data, determining, from at least a part of the cartographic data acquired and the corresponding representative quantities determined,of a polynomial correlation equation between the cartographic data and the quantity representative of the cartographic data of a rank lower than a predefined rank and of maximum correlation index with the acquired cartographic data and the corresponding representative quantities having served to determine the polynomial correlation equation, contextualized the cartographic data at each acquisition by the navigation system according to the determined correlation equation and generate contextualized cartographic data if the correlation index is greater than a predetermined threshold correlation value.,

[0080] The features described above in connection with the method also apply alone or in combination to the above vehicle.

[0081] Brief description of the drawings

[0082] The invention may be better understood by reading the detailed description which follows, non-limiting examples of its embodiment, and by examining the attached drawing, in which:

[0083] [Fig 1] Figure 1 illustrates, schematically and partially, an example of equipment of a vehicle suitable for implementing the method according to the invention.

[0084] [Fig 2] Figure 2 is a diagram illustrating steps of acquiring cartographic data and measuring corresponding representative quantities according to the invention.

[0085] [Fig 3] Figure 3 is a block diagram illustrating steps of an example of a method for contextualizing cartographic data according to the invention. [Fig 4] Figure 4 schematically and partially illustrates different samples of cartographic data and corresponding measurements of quantities according to the invention.

[0086] Detailed description

[0087] Figure 1 illustrates an example of a motor vehicle 1 in top view having equipment suitable for implementing the method according to the invention.

[0088] Vehicle 1 can be thermal, for example gasoline, diesel, gas, hydrogen, or hybrid, or even electric.

[0089] The vehicle 1 may include different sensors 3.

[0090] Vehicle 1 is also equipped with a computer 5, which receives data from the various sensors 3.

[0091] The computer 5 comprises one or more processors executing one or more programs allowing the implementation of the method according to the invention.

[0092] The computer 5 may be composed of all or part of an on-board computer system comprising, for example, in addition to the aforementioned processor(s), at least one RAM memory, at least one ROM memory used to save the applications of the vehicle 1, one or more possible digital-analog converters as well as one or more input / output interfaces used to communicate with the various sensors 3.

[0093] The computer 5 can access an on-board memory storing a plurality of map data, as well as data measured by the sensors 3.

[0094] The calculator 5 can be connected to any type of interface allowing information to be presented to the user of the vehicle 1.

[0095] The vehicle 1 further comprises a navigation and location system 7, preferably with a GNSS receiver, for example of the GPS type, enabling in particular the driver to enter a route for the vehicle 1 and / or the latter to know its position in real time.

[0096] The vehicle 1 may be configured to access a remote server via any type of communication means, for example a 4G or 5G network or other.

[0097] The remote server may comprise a plurality of map information. The server is accessible by a set of several vehicles 1 according to the invention, the connection of each of the vehicles 1 to the server being able to be carried out via a protocol having a certain level of security. The level of security may be sufficient for the use of the map information in certain areas.

[0098] The cartographic information acquired by the navigation system corresponds to recordings made previously and saved on an external database. These recordings were made in a context, in particular external, in particular weather conditions, which are not necessarily those of the vehicle which acquires the information. It may therefore be necessary to modify the cartographic information which can be modified to adapt it to the particular context and thus be able to use it in the vehicle, in particular for the control of the vehicle on the chassis and powertrain sectors which require the most accurate data possible. The subject of the present invention is the method for contextualizing the cartographic data shown in Figure 3.

[0099] In the example considered, the cartographic data to be contextualized are considered reliable. They may for example relate to the roughness coefficient of the road, but the method is generalizable to other types of data, such as the road grip coefficient, the road slope, the external temperature or pressure, the curvature of a bend, or to any other type of cartographic data and measurable directly or indirectly by one or more sensors 3 of the vehicle 1.

[0100] A plurality of acquisitions of cartographic data to be contextualized, here of road roughness coefficient, is carried out by the navigation system 7 during a first step 40.

[0101] In Figure 3, u times of acquisition of a cartographic data to be contextualized ki, k2, ... k usuccessive are represented by Tl, T2 ... Tu. The sampling of the acquisition of the road roughness coefficient values ​​is thus carried out on u acquisitions.

[0102] For each acquired road roughness coefficient value ki, kz, ... k u , a measure of a representative quantity vi, V2, ...v u is carried out by the sensor 3 of the vehicle 1, in this case a sensor 3 located at the level of the wheels of said vehicle 1. The measurements of the road roughness coefficient are carried out at different times at locations substantially identical to the map position associated with each map data item.

[0103] Figure 2 schematically illustrates a vehicle 1 moving from left to right along a road represented by a first axis 36 and the acquisition of cartographic data and the determination of the corresponding representative quantity.

[0104] The first axis 36 illustrates the operation of the navigation system 7, in particular the different times and positions of acquisition of the cartographic data relating to the road grip coefficient, as well as the cartographic positions of said data.

[0105] A second equivalent axis 38 is represented below the first axis 36, illustrating the operation of the measurement sensor(s) 3 to determine the corresponding representative quantities, in particular the different positions and times of measurements by the sensor 3 of the vehicle 1 of the quantity representative of the road grip coefficient.

[0106] Vehicle 1 acquires position Pv n a road grip coefficient value geolocated at a map position Pd n located in front of him.

[0107] A calculation is carried out, in particular from the location of vehicle 1, its speed and its relative distance to point Pd n , in order to determine a measurement time Tn' where vehicle 1 will reach a point Pv n ' corresponding substantially to the position Pd n acquired. This determination of the measurement point by the sensor(s) 3 to determine the representative quantity can take into account the speed of the vehicle 1 at the acquisition time T n and can refine the determination with the speed of vehicle 1 between time T n and measurement.

[0108] Similarly, a new road grip coefficient data is acquired at time T n+i at position Pv n +i, the data being located at a position Pdn+i and the measurement being carried out at a time T n+ i' when vehicle 1 has reached a position Pvn+i' corresponding substantially to position Pdn+i acquired.

[0109] The distance r n separating the acquisition point from the Pv data n and the location of the map data at position Pd n is preferably less than a predefined threshold distance r threshold. Similarly, the distance r n+i is less than r se uii- They can be identical. Indeed, the map data acquired for the determination or update of confidence can be at a constant distance from vehicle 1 at the time of acquisition. Alternatively, these distances are not identical. They can notably depend on the occurrence of an event corresponding to the map data.

[0110] The distance r n+ i' between the acquired map position and the location of vehicle 1 at the next acquisition time is preferably greater than or equal to 0.

[0111] In particular, in the case where r n+i' = 0, the measurement of the quantity representative of the value of the adhesion coefficient relative to the geolocated data at Pd n is carried out at approximately the same time as the acquisition of the geolocated data at Pd n+ i.

[0112] Alternatively, the measurement of the quantity representative of the value of the adhesion coefficient relative to the geolocated data at Pd n is carried out after the acquisition of the geolocated data at Pd n +i. In this case, the point Pd n is preferably located after Pv n+i and before Pd n+ i.

[0113] The method then comprises a step 44 of determining differentials between each acquired roughness coefficient value ki, k2, ... k u and the corresponding representative quantities vi, V2, ...v u .

[0114] In step 46, a rank 1 polynomial correlation equation with a maximum correlation index Ir is determined from the differentials determined in the previous step 44 and the road roughness coefficient values ​​acquired in step 40 and / or the corresponding representative quantities vi, V2, ...v u .

[0115] Determination of the maximum correlation index I r and the associated correlation model can for example be carried out by the least squares method.

[0116] Such a polynomial correlation equation provides an approximation of the roughness coefficient data by the values ​​measured directly by the sensor 3. Thus for a value v of the roughness coefficient of the road measured by the sensor 3 of the vehicle 1, the value of the acquired cartographic data k is estimated by a polynomial equation of rank 1, for example here of the form k = c'v + b', c' being a coefficient of proportionality and b' being a constant representing a fixed deviation.

[0117] Figure 4 illustrates three examples of correlations observed between cartographic data, for example the adhesion coefficient, and representative quantities measured by a sensor 3 of the vehicle 1. The graphs illustrate on the ordinate the value of the adhesion coefficient and on the abscissa the position of the vehicle 1 corresponding to the data (the cartographic position for the cartographic data and the location of the vehicle 1 at the time of determination of the representative quantity). In these three examples, seven values ​​of acquired cartographic data are represented jointly with the seven values ​​of representative quantities measured by the sensor 3 of the vehicle 1.

[0118] The acquired map data are represented on the dotted line curve, and the measurements of representative quantities on the solid line curve.

[0119] The graph in Figure 4a) shows a constant deviation b between the acquired map data and the corresponding representative quantity measurements. The determined polynomial equation is thus of degree 1 and of type y=x+b.

[0120] The graph in Figure 4b) shows a proportional deviation of proportionality coefficient c between the acquired map data and the corresponding representative quantity measurements. The determined polynomial equation is thus of degree 1 and of type y=cx.

[0121] For these first two cases, there is a rank 1 polynomial equation showing very good correlation with the cartographic data and representative quantities and therefore good confidence in the cartographic data.

[0122] The graph in Figure 4c) shows a sample of acquired map data and corresponding representative quantity measurements without rank 1 polynomial equation with a good correlation index I r determinable, in particular with a correlation index I r greater than a threshold correlation value I rs .

[0123] The correlation index I r is compared to a threshold correlation index I rs at step 48.

[0124] In a first scenario, the correlation index I r determined is greater than the threshold correlation index I rs . The map data representing the road roughness coefficient can then be contextualized with the correlation polynomial equation determined during a step 50.

[0125] Step 50 may also comprise the generation of control data for the vehicle 1, in particular to an actuator of the vehicle 1, as a function of contextualized data of corrected road roughness coefficients relating to positions located in front of the vehicle 1, for which a measurement of a representative quantity by the wheel sensor 3 has not yet been carried out. Said data may, for example, provide information on an increase or decrease in the road roughness coefficient.

[0126] Finally, the method ends with a control step 52 during which the vehicle 1 is controlled from the control data, for example the actuator influences the dynamic behavior of the vehicle 1 in an anticipated manner, for example here on the brakes of the vehicle 1 on the basis of the contextualization of the data considered reliable, informing of the decrease or increase in the roughness coefficient of the road.

[0127] The method may include recording or sending contextualized data to a remote server when the confidence index I c and the correlation index I r are respectively greater than a threshold confidence value and a threshold correlation value. The method may also include recording context data from data of the vehicle 1 or from outside the vehicle 1, for example temperature, humidity, wind, rain or ice. This data may be transmitted with the contextualized map data to record the context of acquisition of said data.

[0128] In the example considered, the correlation index I ris evaluated periodically according to a predetermined update frequency, based on a new sampling of map data acquired at later times. The confidence in the correlation model of said data is thus likely to evolve over time. Alternatively, the update of the correlation index I r can be performed non-periodically, especially when a decorrelation with the model is determined.

[0129] The method may comprise, for any data subsequent to the acquisition of a polynomial correlation equation, the determination of a correlation index between the acquired cartographic data, the corresponding determined characteristic quantity and the polynomial equation. Such an index may be determined as a function of a differential between the cartographic data contextualized on the basis of the polynomial correlation equation and the corresponding determined characteristic quantity.

[0130] The method may comprise the discarding of a cartographic data item whose correlation index is below a predetermined threshold and / or for which a less secure command in the control of the vehicle 1 is carried out if it is taken into account. The method may comprise the determination of a new correlation polynomial equation when a plurality of successive cartographic data items in a pre-established quantity are decorrelated with the determined equation, i.e. have an index characteristic of a decorrelation, in particular below a predetermined threshold. Alternatively, the acquisition of cartographic data may be carried out in groups (or “batches”). For example, a predefined number of cartographic data items may be acquired simultaneously at each acquisition.

Claims

Claims 1. Method for contextualizing cartographic data implemented by a moving vehicle (1) comprising at least one sensor (3) and a navigation system (7), the navigation system (7) being configured to acquire from the outside, at an acquisition time, the location of the vehicle (1) at the acquisition time and cartographic information with their associated cartographic positions, the method comprising: - a plurality of acquisitions (20, 40) by the navigation system (7), at different acquisition times (T i, ... T n ), of a cartographic data (ai,... a n ) among the map information with the map position (Pdi, ... Pd n ) associated with said cartographic data (ai,... a n ), the map positions (Pdi, ... Pd n ) associated with said acquired cartographic data (ai,... a n) being geographically different from each other, - The determination (40), for each acquired cartographic data (ai,. .. a n ), of a representative size (mi, ... m n ) of the cartographic data determined from at least one measurement (mi, ... m n ) by at least one sensor (3) at a location (Pvi', ... Pv n ') of the vehicle (1) substantially equal to the map position (Pdi, ... Pd n ) associated with said acquired cartographic data (ai,... a n ), - The determination (46), from at least part of the acquired cartographic data (ai,... a n ) and representative quantities (nu, . .. m n ) determined corresponding, of a polynomial correlation equation between the cartographic data (ai,... a n ) and the representative quantity (mi, ... m n) of the cartographic data of a rank lower than a predefined rank and of maximum correlation index I r with the acquired map data (ai,... a n ) and the representative quantities (mi, ... m n ) corresponding to the determination of the polynomial correlation equation (46), - The automatic contextualization (50) of the cartographic data at each acquisition by the navigation system (7) according to the correlation equation determined to generate contextualized cartographic data if the correlation index I r is greater than a predetermined threshold correlation value I rs (48).

2. Method according to any one of the preceding claims, in which the acquisitions of the cartographic data are successive and sampled in time or on the positioning of the vehicle (1) on its route at a predetermined sampling frequency identical to or different from the frequency of acquisition of the cartographic information, in particular at an acquisition frequency corresponding to an acquisition of the cartographic data every N acquisitions of cartographic information by the navigation system (7), N being an integer.

3. Method according to any one of the preceding claims, in which the acquired map positions (Pdi, ... Pd n ) are at a distance and / or a travel time relative to the vehicle (1) not zero at the time of acquisition (Ti, ... T n) corresponding, in particular the distance, in particular along the most probable path, between the acquired map positions (Pdi, ... Pd n ) and the vehicle locations (1) at the time of acquisition (Pvi, ... Pv n ) corresponding is substantially constant.

4. Method according to any one of the preceding claims, in which the determination of the correlation equation (46) is done at an evaluation time and takes into account at least the acquired cartographic data (ai, ... a n ) at the acquisition time (Ti, ... T n ) upstream in time closest to the evaluation time.

5. Method according to any one of the preceding claims, in which the acquired map positions are at a non-zero distance, in particular substantially constant, from the location of the vehicle (1) at the corresponding acquisition time.

6. Method according to any one of the preceding claims, in which the determination of the representative quantity (mi, ... m n ) for each acquired map data (ai,... a n ) includes the determination, as a function of the speed of the vehicle (1) at the time of acquisition (Ti, ... T n ), of the measurement time (Ti', . .. T n ') so that the location (Pvi', ... Pv n ') of the vehicle (1) at the measurement time is substantially equal to the map position (Pdi, ... Pd n ) associated with the cartographic data, the measurement time (Tf, ... T n ') being in particular determined at least from the distance on the most probable path between the vehicle (1) at the acquisition time (Ti, ... T n ) and the map position (Pdi, ... Pd n ) associated with the map data acquired at the time of acquisition (Ti, ... T n) and the vehicle speed (1) at the acquisition time (Ti, ... T n ) or by integrating any variations in the vehicle's speed (1) between the instant acquisition (Ti, ... T n ) and the measurement time (Ti', . .. T n ') to improve said determination.

7. Method according to any one of the preceding claims, in which the cartographic data (ai,... a n) is chosen from information comprising a numerical value, preferably substantially continuous over a path, in particular the curvature of a bend comprising for example a value of the local curvature angle of the road, the slope of a road comprising a value of the local inclination angle of the road, the road adhesion coefficient comprising a value of the local adhesion coefficient of the road, the road roughness coefficient comprising a value of the local roughness coefficient of the road, the type of road, localized information on the maximum size of the vehicle or the maximum mass of the vehicle authorized on a road.

8. Method according to any one of the preceding claims, in which the determined correlation polynomial equation (46) is of rank 1, that is to say is a proportionality between the cartographic data (ai, ... a n ) and the representative quantity (mi, ... m n) of said cartographic data and / or a constant offset between the cartographic data (ai, ... a n ) and the representative quantity (mi, ... m n ) of said cartographic data.

9. Method according to any one of the preceding claims, comprising the determination of differentials (44) each corresponding to a difference between the acquired cartographic data (ai,... a n ) and the corresponding representative quantity (mi, ... m n ) and the comparison between them of the differentials (44) of the cartographic data for determining the polynomial correlation equation (46), the comparison of the differentials (44) comprising the determination of the polynomial correlation equation (46).

10. Method according to claim 9, wherein the determination of the correlation index I r(46) is carried out at a correlation time by comparison of at least a part of the differentials, better all the differentials, corresponding to at least a part of the acquired cartographic data (ai,... a n ) upstream of the correlation instant over a time window of a predetermined acquisition duration ending at the correlation instant or corresponding to a predetermined number of successive cartographic data acquisitions going back in time from the correlation instant 11. Method according to any one of the preceding claims, comprising the determination of a difference between a correlation index associated with the or each cartographic data item acquired after the determination of the polynomial correlation equation (46) corresponding to the correlation of the or each cartographic data item acquired after the determination of the polynomial correlation equation and the corresponding representative quantity with the polynomial correlation equation, the index being in particular determined by calculating the difference between the contextualized cartographic data item and the corresponding representative quantity.

12. Method according to any one of the preceding claims, comprising determining a new correlation polynomial equation if the correlation index I r is less than the correlation threshold value I rsover a period greater than a predetermined period, in particular the acquisition period, or over a number of successive acquisitions greater than a predetermined number or if the correlation indices of the cartographic data acquired over a period greater than the predetermined period, in particular the acquisition period, or over a number of successive acquisitions greater than the predetermined number are all lower than a threshold index value.

13. Method according to any one of the preceding claims, comprising, as long as the correlation index I ris greater than the threshold correlation value 1rs or that the correlation indices of the acquired map data are greater than a threshold index value, the use of the contextualized map data, in particular by enriching a database using this map data or generating control data at least as a function of the contextualized map data, the control data being in particular input data of a user information device or of an actuator of the vehicle (1).

14. Method according to any one of the preceding claims, comprising the determination of context data from one or more sensors (3) of the vehicle (1) or external data and the association of the context data with the contextualized cartographic data and / or with the determined correlation equation (46), the context data comprising in particular the presence of rain or not and / or its intensity, the temperature, the humidity level, the wind speed and / or the dew point.

15. Method according to the preceding claim, comprising sending contextualization information to a database, in particular internal or external, the contextualization information comprising the context data and one or the combination of the contextualized cartographic data and the determined correlation equation.

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