Process of contextualizing cartographic data

Abstract

Method for contextualizing cartographic data. 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 cartographic information with its associated cartographic positions, the method comprising: - a plurality of acquisitions by the navigation system, at different acquisition times, of a cartographic data point from among the cartographic information, - The determination, for each acquired cartographic data point, of a quantity representative of the cartographic data point at a vehicle location substantially equal to the cartographic position associated with said acquired cartographic data point, - The determination of a polynomial correlation equation between the cartographic data point and the quantity representative of the cartographic data point.- Automatic contextualization of map data at each acquisition by the navigation system based on the determined correlation equation. Figure for the abbreviation: Fig. 3,

Description

Title of the invention: Method for contextualizing cartographic data. Technical field.

[0001] The present invention relates to a method for contextualizing map data by a moving vehicle and a vehicle comprising a computer program product configured to implement a method for contextualizing map information by a moving vehicle. Prior art

[0002] Current automotive navigation systems generally incorporate external map information. This information is obtained 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 by V2V (“Vehicle to Vehicle”) or V2X (“Vehicle to Everything”) protocol.

[0003] Typically, this information provides improved visibility or information about the vehicle's surroundings, particularly the environment in front of the vehicle, and is thus more commonly referred to as electronic horizon information (also called "eHorizon") of the vehicle. It allows for supplementing and / or anticipating information from the vehicle's sensors, for example, a camera, radar, lidar, or sonar.

[0004] This data includes, for example, cartographic information relating to the characteristics of a road, the characteristics of a predetermined route, road signs, and / or external conditions.

[0005] Taking this information into account allows for an anticipation of the situations that said vehicle will encounter, and thus an anticipatory adaptation of the dynamic behavior of the vehicle.

[0006] However, electronic horizon information derived from external sources or directly from other vehicles is pre-recorded data under specific driving conditions. The conditions in which the vehicle operates may differ from those under which the pre-recorded data was recorded. This can create a safety risk for road users if the pre-recorded data is used without considering the context 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 If the system fails to account for low temperature or high humidity, it may be perceived as high even when external conditions make it low. This can lead to risky vehicle control in the event of low grip or road surface roughness due to weather conditions, particularly when the control involves one or more actuators. It is therefore necessary to contextualize the pre-recorded data from the navigation system, which provides information about the road ahead, to ensure reliable data consistent with the specific context in which the vehicle is operating. An "actuator" is defined as a device configured to control, alone or in combination with other actuators, the dynamic behavior of the vehicle based on instructions determined by an algorithm, notably by influencing the brakes, engine, steering, and / or wheel speeds.It is indeed essential to control the actuators with reliable data.

[0007] Today, actuator control, particularly by the chassis system (also known as the "Chassis Domain") or the micro-propulsion system (also known as the "Powertrain Domain"), is essentially reactive, in the sense that the information taken into account by these systems to generate control data is pre-calculated or generated by means of direct ("sensing") or indirect ("virtual sensing") physical measurements of the vehicle. Taking into account, at least partially, the map information provided by the navigation system to have proactive control of the actuators—that is, in anticipation of the occurrence of a future event on the vehicle's predicted route—would be advantageous because it would allow the vehicle's behavior to be anticipated and the actuators to be acted upon in anticipation of the event, thereby improving the vehicle's reaction to the event.For example, if a vehicle is traveling on a road and receives map data relating to the road friction coefficient value downstream of the vehicle, it might be possible to anticipate the vehicle's drift following a sudden decrease in traction by modifying the commands of one or more of the vehicle's wheel actuators, particularly a yaw command. This improves the vehicle's responsiveness and driver comfort. However, such consideration of map data requires reliable navigation system data with reasonably accurate values.

[0008] There is therefore a need to contextualize mapping 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 mapping information in the control of actuators, in particular by the chassis system or the micro-propulsion group. Detailed description

[0009] The invention addresses this need by means of a method for contextualizing map data implemented by a moving vehicle comprising at least one sensor and a navigation system, the navigation system being configured to acquire externally, at an acquisition time, the vehicle's location at the time of acquisition and map information with their associated map positions, the method comprising - a plurality of acquisitions by the navigation system, at different acquisition times, of a cartographic data point from among the cartographic information with the cartographic position associated with said cartographic data point, the cartographic positions associated with said acquired cartographic data point being geographically different from each other, - The determination, for each acquired map data point, of a quantity representative of the map data point determined from at least one measurement by at least one sensor at a vehicle location substantially equal to the map position associated with said acquired map data point, - The determination, from at least a part of the acquired map data points and the corresponding determined representative quantities, of a polynomial correlation equation between the map data point and the representative quantity of the map data point of a rank lower than a predefined rank and of maximum correlation index with the acquired map data points and the corresponding representative quantities used to determine the polynomial correlation equation, - Automatic contextualization of map data at each acquisition by the navigation system based on the correlation equation determined to generate contextualized map data if the correlation index is greater than a predetermined threshold correlation value.

[0010] By "associated map position," it is understood that the navigation system acquires the map position corresponding to the position of each map piece of information on a predetermined route. The navigation system can acquire a map position for each acquired map piece of information or acquire a map position for a plurality of acquired map pieces of information located at the same point on a path predetermined as the most probable. The map position may include the geographic location of a point where the map piece of information is located at the time of acquisition relative to a fixed or moving reference frame. This geographic location may be determined relative to a fixed system, in particular a geodetic system, for example the WGS system. 84 associated with the GPS positioning system. Alternatively, it is determined relative to a mobile system, specifically relative to the vehicle, the geographic location then being the position at the time of acquisition of the map information(s) acquired relative to the vehicle. The map position may include GPS coordinates, latitude, longitude and altitude coordinates, coordinates relative to the vehicle, a distance from the vehicle along the most probable route, or any other means of positioning the map data on a map or relative to the vehicle.

[0011] By "acquisition", it is understood that at an acquisition time, the navigation system receives a data stream containing map information and associated map positions from a remote server through an internet network, including a cloud, or from another vehicle.

[0012] By "representative quantity" is understood a quantity determined from a measurement of at least the sensor which, when the mapping data is accurate and reliable, has a relationship with the acquired mapping data. The relationship is in particular an equality, a fixed difference or a proportionality.

[0013] By "substantially equal," it is understood 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 at the time of measurement less than or equal to 0.5 s, preferably 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, preferably less than or equal to 10 m, even better less than or equal to 5 m, even better less than or equal to 2 m, for example, substantially equal to 1 m.

[0014] The invention allows, 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 place, 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 in order to have cartographic data corresponding to the representative quantity which are reliable for consideration in the vehicle and anticipated over a more or less distant duration or distance depending on the cartographic data.

[0015] It is then possible to use this contextualized map data in several ways, particularly in vehicle control, user alerts or enriching a contextualization database as we shall see subsequently. This allows for anticipating vehicle control based on contextualized map data.

[0016] Such a method is therefore particularly well-suited to the use of proactive algorithms—unlike state-of-the-art algorithms, which are essentially reactive—allowing, in particular, the adaptation of the parameters of a control algorithm for one or more actuators by distributing the instructions to the actuators differently based on acquired mapping information. A system defined in this way is thus made more efficient.

[0017] The polynomial correlation equation can characterize a difference between a map data and a representative quantity which is a function of the context in which the vehicle evolves, in particular meteorological conditions such as rain or snow, temperature, humidity or wind, the parameters of the sensors, in particular their calculation units which may differ from the unit of the map data, the characteristics specific to the vehicle, driving habits or any other context which could generate a difference between a map data and a representative quantity of the map data. Navigation system

[0018] Preferably, the method involves the navigation system determining the most probable path for the vehicle based on user input data, information acquired from one or more vehicle sensors, and / or the vehicle's location determined by the navigation system. The most probable path for the vehicle may be a route validated by the user or a route corresponding to a path determined to be the most probable based on the vehicle's location and one or more vehicle or user data points.

[0019] The acquired cartographic information is associated with a cartographic position on or along the determined most probable path.

[0020] The navigation and location system is preferably a GNSS receiver, for example of the GPS type, and allows in particular the driver to enter a route for the vehicle and / or the latter to know its position in real time.

[0021] The navigation system can acquire map data and / or map information from a remote server via an internet network, including a cloud, or from another vehicle, for example by V2V (“Vehicle to Vehicle”) or V2X (“Vehicle to Everything”) protocol. Map information

[0022] The cartographic information may include: - one or more characteristics of a road, including the coefficient of friction and / or road roughness, the type of road, and / or - one or more characteristics of a predetermined route, including the curvature of a bend, the slope of the road, the presence of obstacles on the road, including the presence of speed bumps, and / or - road signs, including the presence and identification of road signs or lines on the ground, and / or - external conditions, including weather or temperature, or localized information on the maximum vehicle dimensions or the maximum vehicle mass allowed on a road. Mapping data

[0023] The cartographic data may be one of the pieces of information described above.

[0024] Preferably, the map data provides information relating to a physical or structural property of the road along the most probable route determined by the navigation system, exhibiting variability depending on the external context. The map data can be chosen from all map information exhibiting such variability, including one or more physical characteristics of the road, for example, the coefficient of friction and / or the road roughness.

[0025] Preferably, it is chosen from information having a numerical value, preferably substantially continuous over a path. It can be chosen from the curvature of a curve having, for example, a value of the local angle of curvature of the road, the slope of a road having a value of the local angle of inclination of the road, the coefficient of friction of the road having a value of the local coefficient of friction of the road, the coefficient of roughness of the road having a value of the local coefficient of roughness of the road.

[0026] 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.

[0027] The map 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 map data, for example, a coefficient of adhesion, roughness, a slope, or an angle of curvature.

[0028] The method may, for each acquisition, involve acquiring 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 point, a polynomial correlation equation on a subset of the acquisition plurality. Representative size

[0029] The representative quantity may be a value obtained from a measurement by one or more sensors installed in the vehicle. The value may be obtained by a direct measurement from a sensor on the vehicle or deduced from one or more measurements from one or more sensors.

[0030] The representative quantity can be determined using a single sensor.

[0031] The method may include determining the distance along the most probable path between the vehicle at the time of acquisition and the map position associated with the map data acquired at the time of acquisition. Alternatively, this information is acquired directly with the map data for each acquired map data point.

[0032] Determining the representative value for each acquired map data point may involve determining, based on the vehicle's speed at the time of acquisition, the measurement time at which the vehicle's location at the measurement time is substantially equal to the map position associated with the map data point. The measurement time may be determined at least from the distance along the most probable path between the vehicle at the time of acquisition and the map position associated with the acquired map data point at the time of acquisition, and the vehicle's speed at the time of acquisition. The measurement time may also be determined dynamically by integrating any variations in the vehicle's speed between the time of acquisition and the time of measurement to improve said determination.

[0033] Determining the time of measurement of the representative quantity by calculation allows for a relatively precise and reliable method. 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 taken at the optimal time, for example, between two acquisitions by the navigation system.

[0034] Alternatively, the measurement time can be determined by periodically acquiring the vehicle's location and triggering the measurement when the vehicle's location identified by the periodic acquisition is closest to the map position acquired with the map data. Sampling

[0035] The acquisitions of map data can be successive over time or based on the vehicle's positioning along its route. Each acquisition can be triggered automatically or manually.

[0036] Map information can be acquired by the navigation system at a predefined acquisition frequency, in particular when the vehicle is in motion.

[0037] Map data acquisitions can be sampled over time or based on the vehicle's position along its route at a predetermined sampling frequency that is the same as, or different from, the frequency at which the map information is acquired. In particular, the sampling frequency may correspond to one map data acquisition every N map information acquisitions by the navigation system, where N is an integer. The acquisition times may be spaced at substantially constant intervals or at intervals corresponding to a substantially constant distance traveled by the vehicle.Alternatively, the method may include searching for map data at each receipt of map information and acquiring the map data if it is present at a map position at a distance from the vehicle on the most probable path less than or equal to a predetermined distance and if it has not been acquired previously at a previous acquisition time.

[0038] The acquired map positions are at a distance and / or travel time from 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 distance from the vehicle, in particular less than or equal to 500m, better less than or equal to 100m and / or greater than or equal to 1m, better greater than or equal to 5m.

[0039] The distance, particularly along the most probable 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.

[0040] The confidence index can be determined on at least a portion of the map data acquired over a predetermined acquisition period and / or a predefined number of acquisitions. The acquisition period can be predetermined based on the map data. The number of acquisitions can be predetermined based on the map data, particularly based on the frequency of occurrence of the map data in the case of map data characteristic of a discrete event.

[0041] Preferably, the determination of the confidence index is made at an evaluation time and takes into account at least the cartographic data acquired at the time of acquisition upstream in the time closest to the evaluation time.

[0042] The determination of the polynomial correlation equation can be performed on at least a portion of the map data acquired over a predetermined acquisition period and / or a predefined number of acquisitions. The acquisition period can be predetermined based on the map data. The number of acquisitions can be predetermined based on the map data, in particular based on of the frequency of occurrence of the map data in the case of map data characteristic of a discrete event.

[0043] Preferably, the correlation equation is determined at an evaluation time and takes into account at least the cartographic data acquired at the upstream acquisition time closest to the evaluation time. Polynomial equation

[0044] Preferably, the determined polynomial correlation equation is of rank less than 1, that is, it is a proportionality between the map data and the representative quantity of said map data and / or a constant lag between the map data and the representative quantity of said map data. By "proportionality" is understood a relationship of the type y=cx, where y is the representative quantity, x is the acquired map data, and c is a proportionality factor, which can take any value, in particular being substantially equal to 1. By "constant lag" is understood a relationship of the type y=x+b, where y is the representative quantity, x is the acquired map data, and b is a constant value. Sliding window

[0045] Preferably, the determination of the correlation index is carried out at a correlation instant by comparing at least a part of the differentials, better all the differentials, corresponding to at least a part of the cartographic data acquired 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.This allows the correlation index to be determined over a sliding time window prior to the correlation point, corresponding to a constant predefined duration or a constant number of acquisitions. This provides an up-to-date correlation index for a predetermined number of map data points, ensuring it is both high enough to guarantee stable determination and low enough to limit the risk of error in the correlation assessment. The correlation point can be the same as the assessment point, and / or the predefined correlation duration can be the same as the acquisition duration, or the number of map data points considered can be the same. Correlation

[0046] The method may include determining differentials, each corresponding to a difference between the acquired cartographic data and the corresponding representative quantity, and comparing these differentials of the cartographic data to determine the polynomial correlation equation, the comparison of differentials involving the determination of the polynomial correlation equation

[0047] Preferably, the correlation index is defined such that the smaller the discrepancies between the cartographic 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.

[0048] The method may include determining a correlation index associated with the map data acquired after the determination of the polynomial correlation equation corresponding to the correlation of the map data acquired after the determination of the polynomial correlation equation and the corresponding representative quantity with the polynomial correlation equation. This index may be determined from the difference between the map data acquired after contextualization and the corresponding representative quantity. Such an index makes it possible to characterize, for each map data point, whether or not the determined polynomial equation is respected.

[0049] Preferably, the correlation index is defined such that the smaller the difference between the map data 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 performed using the polynomial correlation equation.

[0050] The method may include determining a new polynomial correlation equation if the correlation index is less than the threshold correlation 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 less than a threshold index value.

[0051] The method may include, as long as the correlation index is greater than the threshold correlation value or the correlation indices of the acquired map data are greater than a threshold index value, the use of the acquired map data after contextualization, in particular by enriching a database with this map data or generating control data at least based on the map data acquired after contextualization, the control data being in particular an input data for a user information device or a vehicle actuator.

[0052] The method may include 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 or absence of rain and / or its intensity, temperature, humidity level, wind speed and / or dew point.

[0053] 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 map data acquired after contextualization and the determined correlation equation.

[0054] Preferably, the method for determining the polynomial equation is chosen from among polynomial interpolation, in particular Lagrangian interpolation, and polynomial regression. In particular, polynomial regression can be carried out by the least squares method, maximum likelihood, Bayesian inference, or by machine learning methods such as support-vector machines (SVMs).

[0055] Preferably, the correlation index is chosen from among the Bravais-Pearson coefficient, Spearman's rho coefficient, Goodman and Kruskal's gamma coefficient, or Kendall's tau coefficient.

[0056] The threshold correlation value may depend on contextual parameters.

[0057] Preferably, the threshold correlation value depends on the type of data cartographics. For example, the threshold correlation value associated with cartographic data relating to temperature is different from that associated with cartographic data relating to the road friction coefficient.

[0058] The threshold correlation value may depend on the number of map data sampled to determine the polynomial correlation equation. Discarding of a clearly erroneous piece of data

[0059] The method may include, for each pair of cartographic data and corresponding representative quantity, the removal of a manifestly erroneous data point 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.

[0060] Preferably, a data point is considered manifestly erroneous if its correlation index is greater in absolute value than a predetermined error threshold and the nearest map data points have correlation indices lower in absolute value than a predetermined error threshold.

[0061] Discarding a manifestly erroneous piece of data makes it possible to obtain a more precise and stable determination. Sensors

[0062] At least one sensor of the vehicle may be of the external type, and perform measurements relating to physical parameters, objects or infrastructure external to the vehicle.

[0063] Conversely, at least one sensor may be of an internal type and perform measurements relating to physical parameters or objects internal to the vehicle, including tire pressure, wheel rotation angle or pressure in the passenger compartment.

[0064] At least one sensor is preferably chosen from among odometers, gyroscopes, accelerometers, thermocouples, RTDs, thermistors, barometers, cameras, sonars, radars or Lidars or a combination of these sensors. Actuator

[0065] 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 wheel speeds, depending at least on the control data.

[0066] The actuator can be configured to control the chassis, in particular the suspensions, the motorization in torque setting, in particular of the internal combustion or electric motor, the wheel motors, the brakes, or even the noise cancellation systems inside the vehicle (from the English "active noise control" or ANC). Vehicle

[0067] The invention also relates to a vehicle equipped with at least one sensor and a navigation system enabling the acquisition, at a given time of acquisition, of cartographic information with its associated cartographic position and the location of the vehicle at the time of acquisition, said vehicle further comprising a computer program product configured so that, when the vehicle is in motion: - locate the vehicle and acquire mapping information, - to perform multiple acquisitions by the navigation system, at different acquisition times, of a map data item from among the map information and of the map position associated with said map data, the acquired map positions being different from each other, - to determine, for each acquired map data item, a quantity representative of the map data from at least one measurement by at least one sensor at a vehicle location calculated to be substantially equal to the map position associated with said map data item. - to determine, for each acquired cartographic data point, a differential for each cartographic data point between the acquired cartographic data and the corresponding quantities determined by measurement and comparison of the differentials of successive cartographic data points, - to determine, from at least a part of the acquired cartographic data and the corresponding representative quantities determined, 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 used to determine the polynomial correlation equation, - contextualized the map data at each acquisition by the navigation system according to the determined correlation equation and generate contextualized map data if the correlation index is greater than a predetermined threshold correlation value.

[0068] The characteristics described above in connection with the process also apply alone or in combination to the above vehicle. Detailed description

[0074] Figure 1 illustrates an example of a motor vehicle 1 in top view having equipment adapted to implement the method according to the invention.

[0075] Vehicle 1 can be thermal, for example of the petrol, diesel, gas, hydrogen, or hybrid type, or even electric.

[0076] Vehicle 1 may include different sensors 3.

[0077] Vehicle 1 is also equipped with a computer 5, which receives data from the various sensors 3.

[0078] The computer 5 includes one or more processors executing one or more programs enabling the implementation of the method according to the invention.

[0079] The computer 5 may be composed of all or part of an embedded 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 optional digital-to-analog converters as well as one or more input / output interfaces used to communicate with the various sensors 3.

[0080] The computer 5 can access an on-board memory storing a plurality of map data, as well as data measured by the sensors 3.

[0081] The computer 5 can be connected to any type of interface allowing information to be presented to the user of the vehicle 1.

[0082] The vehicle 1 further includes 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.

[0083] Vehicle 1 can be configured to have access to a remote server via any type of communication means, for example 4G or 5G network or other.

[0084] The remote server can include a plurality of map information.

[0085] 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 possible via a protocol offering a certain level of security. The level of security may be sufficient for the use of mapping information in certain fields.

[0086] The map information acquired by the navigation system corresponds to recordings made previously and stored in an external database. These recordings were made in a specific context, including external conditions, particularly weather conditions, which may not necessarily be those of the vehicle acquiring the information. It may therefore be necessary to modify the map information, where possible, to adapt it to the specific context and thus enable its use in the vehicle, particularly for vehicle control in the chassis and powertrain sectors, which require the most accurate data possible. The present invention relates to the method of contextualizing map data shown in [Fig. 3].

[0087] In the example considered, the cartographic data to be contextualized are considered reliable. They may, for example, relate to the road roughness coefficient, but the process is generalizable to other types of data, such as the road friction coefficient, the road slope, the outside temperature or pressure, the curvature of a bend, or any other type of cartographic data that can be measured directly or indirectly by one or more sensors 3 of the vehicle 1.

[0088] A plurality of cartographic data acquisitions to be contextualized, here of road roughness coefficient, is carried out by the navigation system 7 during a first step 40.

[0089] In [Fig.3], u successive instants of acquisition of a cartographic data to be contextualized kik2, ... ku are represented by T1, T2 ... Tu. The sampling of the acquisition of the road roughness coefficient values ​​is thus carried out on u acquisitions.

[0090] For each value of road roughness coefficient acquired kik2, ... ku, a measurement of a representative quantity vb v2, ... vu is carried out by the sensor 3 of the vehicle 1, in this case a sensor 3 located at the wheels of said vehicle 1.

[0091] The road roughness coefficient measurements are carried out at different times at locations substantially identical to the map position associated with each map data point.

[0092] Fig. 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.

[0093] The first axis 36 illustrates the operation of the navigation system 7, in particular the different times and positions of acquisition of cartographic data relating to the coefficient of adhesion of the road, as well as the cartographic positions of said data.

[0094] A second equivalent axis 38 is shown below the first axis 36, illustrating the operation of the measuring sensor(s) 3 to determine the corresponding representative quantities, in particular the different positions and times of measurement by the sensor 3 of the vehicle 1 of the representative quantity of the road adhesion coefficient.

[0095] Vehicle 1 acquires at position Pvn a road adhesion coefficient value geolocated to a map position Pdn located in front of it.

[0096] A calculation is performed, notably based on the location of vehicle 1, its speed, and its relative distance to point Pdn, in order to determine a measurement instant Tn' at which vehicle 1 will reach a point Pvn' corresponding substantially to the acquired position Pdn. This determination of the measurement point by the sensor(s) 3 to determine the representative quantity may take into account the speed of vehicle 1 at the acquisition instant Tn and may refine the determination with the speed of vehicle 1 between instant Tn and the measurement.

[0097] Similarly, a new road adhesion coefficient data is acquired at time Tn+i at position Pvn+i, the data being located at a position Pdn+i and the measurement being carried out at a time Tn+i' when vehicle 1 has reached a position Pvn+i' corresponding substantially to the acquired position Pdn+i.

[0098] The distance rn separating the data acquisition point Pvn and the location of the map data at position Pdn is preferably less than a predefined threshold distance rseuU. Similarly, the distance rn+i is less than rseuii. These distances may be identical. Indeed, the map data acquired for determining or updating the confidence level may be at a constant distance from vehicle 1 at the time of acquisition. Alternatively, these distances are not identical. In particular, they may depend on the occurrence of an event corresponding to the map data.

[0099] The distance rn+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.

[0100] In particular, in the case where rn+i' = 0, the measurement of the quantity representing the value of the adhesion coefficient relative to the data geolocated at Pdn is carried out substantially at the same time as the acquisition of the data geolocated at Pdn+i.

[0101] Alternatively, the measurement of the quantity representing the value of the adhesion coefficient relative to the geolocated data at Pdn is carried out after the acquisition of the geolocated data at Pdn+i. In this case, the point Pdn is preferably located after Pvn+i and before Pdn+i.

[0102] The process then includes a step 44 of determining differentials between each value of acquired roughness coefficient kik2, ... ku and the corresponding representative quantities vb v2, ...vu.

[0103] During 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 vb v2, ...vu.

[0104] The determination of the maximum correlation index Ir and the associated correlation model can, for example, be carried out by the least squares method.

[0105] Such a polynomial correlation equation provides an approximation of the roughness coefficient data by the values ​​measured directly by sensor 3. Thus, for a value v of the road roughness coefficient measured by sensor 3 of vehicle 1, the value of the acquired map data k is estimated by a first-rank polynomial equation, for example here of the form k-c'v + b',

[0106] c' being a proportionality coefficient and b' being a constant representing a fixed difference.

[0107] Figure 4 illustrates three examples of correlations observed between map data, for example, the coefficient of friction, and representative quantities measured by a sensor 3 on vehicle 1. The graphs show the value of the coefficient of friction on the y-axis and the position of vehicle 1 corresponding to the data on the x-axis (the map position for the map data and the location of vehicle 1 at the time the representative quantity was determined). In these three examples, seven acquired map data values ​​are shown together with the seven values ​​of representative quantities measured by sensor 3 on vehicle 1.

[0108] The acquired cartographic data are represented on the dashed line curve, and the measurements of representative quantities on the solid line curve.

[0109] The graph in Figure 4a) shows a constant difference b between the acquired cartographic data and the corresponding representative measurements. The polynomial equation determined is thus of degree 1 and of the form y=x+b.

[0110] The graph in Figure 4b) shows a proportional difference with a proportionality coefficient c between the acquired cartographic data and the corresponding representative measurements. The polynomial equation determined is thus of degree 1 and of the type y=cx.

[0111] For these first two cases, there exists a rank 1 polynomial equation exhibiting a very good correlation with the cartographic data and representative quantities and therefore good confidence in the cartographic data.

[0112] The graph in Figure 4c) shows a sample of acquired cartographic data and corresponding representative quantity measurements without a rank 1 polynomial equation with a good determinable correlation index Ir, in particular with a correlation index Ir greater than a threshold correlation value 1rs.

[0113] The correlation index Ir is compared to a threshold correlation index Irs in step 48.

[0114] In a first scenario, the determined correlation index Ir is greater than the threshold correlation index Irs. The map data representing the road roughness coefficient can then be contextualized with the polynomial correlation equation determined in step 50.

[0115] Step 50 may also include generating control data for vehicle 1, in particular for an actuator of vehicle 1, based on contextualized data of corrected road roughness coefficients relating to positions in front of vehicle 1, for which a measurement of a representative quantity by the wheel sensor 3 has not yet been carried out. This data may, for example, indicate an increase or decrease in the road roughness coefficient.

[0116] Finally, the process 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 anticipatory 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 of the road roughness coefficient.

[0117] The method may include recording or sending contextualized data to a remote server when the confidence level L and the correlation level Ir are respectively greater than a threshold confidence value and a threshold correlation value. The method may also include recording context data from data within vehicle 1 or from outside vehicle 1, for example the Temperature, humidity, wind, rain, or ice. This data can be transmitted with contextualized map data to record the context in which the data was acquired.

[0118] In the example considered, the correlation index Ir is evaluated periodically at a predetermined update frequency, based on a new sampling of map data acquired at later times. The confidence in the correlation model of this data thus evolves over time. Alternatively, the correlation index Ir can be updated non-periodically, particularly when a decorrelation with the model is determined.

[0119] The method may include, for any data subsequent to the acquisition of a polynomial correlation equation, determining a correlation index between the acquired map data, the corresponding determined characteristic quantity, and the polynomial equation. Such an index may be determined as a function of a difference between the map data contextualized on the basis of the polynomial correlation equation and the corresponding determined characteristic quantity.

[0120] The method may include discarding a map data whose correlation index is below a predetermined threshold and / or for which a less safe command in the control of vehicle 1 is carried out if it is taken into account.

[0121] The method may include determining a new polynomial correlation equation when a plurality of successive cartographic data in a pre-established quantity are uncorrelated with the determined equation, i.e. present an index characteristic of a decorrelation, in particular below a predetermined threshold.

[0122] Alternatively, the acquisition of map data can be carried out in groups (or “batches”). For example, a predefined number of map data points can be acquired simultaneously with each acquisition. Brief description of the drawings

[0069] The invention will be better understood upon reading the detailed description that follows, the non-limiting examples of embodiments thereof, and upon examination of the accompanying drawing, in which:

[0070] [Fig.1] Fig.1 illustrates, schematically and partially, an example of vehicle equipment adapted to implement the process according to the invention.

[0071] [Fig.2] Fig.2 is a diagram illustrating data acquisition steps cartographic and measurement of corresponding representative quantities according to the invention.

[0072] [Fig. 3] Fig. 3 is a block diagram illustrating steps in an example of method for contextualizing cartographic data according to the invention.

[0073] [Fig.4] Fig.4 schematically and partially illustrates different samples of cartographic data and measurements of corresponding quantities according to the invention.

Claims

Demands

1. A method for contextualizing map 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 externally, at an acquisition time, the location of the vehicle (1) at the acquisition time and map information with its associated map positions, the method comprising: - a plurality of acquisitions (20, 40) by the navigation system (7), at different acquisition times (Ti, ... Tn), of a map data point (ab... an) from among the map information with the map position (Pdi, ... Pdn) associated with said map data point (ai,... an), the map positions (Pdi, ... Pdn) associated with said acquired map data points (ai,... an) being geographically different from each other, - The determination (40), for each acquired map data point (ab...an), of a representative quantity (mb ... mn) of the map data determined from at least one measurement (mb ... mn) by at least one sensor (3) at a location (Pvi', ... Pvn') of the vehicle (1) substantially equal to the map position (Pdi, ... Pdn) associated with said acquired map data (ai,... an), - The determination (46), from at least a part of the acquired map data (ai,... an) and the corresponding determined representative quantities (mi, ... mn), of a polynomial correlation equation between the map data (ai,... an) and the representative quantity (mb ... mn) of the map data of a rank lower than a predefined rank and of maximum correlation index Ir with the acquired map data (ai,... an) and the representative quantities (mb ...mn) corresponding used to determine the polynomial correlation equation (46), - 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 Ir is greater than a predetermined threshold correlation value Irs (48).

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

3. A method according to any one of the preceding claims, wherein the acquired map positions (Pdi, ... Pdn) are at a distance and / or travel time relative to the vehicle (1) that is not zero at the corresponding acquisition time (Tb ... Tn), in particular the distance, especially along the most probable path, between the acquired map positions (Pdb ... Pdn) and the locations of the vehicle (1) at the corresponding acquisition time (Pvb ... Pvn) is substantially constant.

4. A method according to any one of the preceding claims, wherein the determination of the correlation equation (46) is made at an evaluation time and takes into account at least the map data acquired (ab ... an) at the acquisition time (Tb ... Tn) upstream in the time closest to the evaluation time.

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

6. A method according to any one of the preceding claims, wherein the determination of the representative quantity (mb ... mn) for each acquired map data point (ab ... an) comprises determining, as a function of the speed of the vehicle (1) at the acquisition time (Tb ... Tn), the measurement time (Tb, ... Tn') such that the location (Pvi', ... Pvn') of the vehicle (1) at the measurement time is substantially equal to the map position (Pdb ... Pdn) associated with the map data point, the measurement time (Tb, ... Tn') being determined in particular at least from the distance along the most probable path between the vehicle (1) at the acquisition time (Ti, ... Tn) and the map position (Pdb ... Pdn) associated with the map data point acquired at the acquisition time (Tb ... Tn) and the speed of the vehicle (1) at the acquisition time (Tb ... Tn) or in incorporating the possible speed variations of the vehicle (1) between the acquisition time (Tb ... Tn) and the measurement time (T / , ... Tn') to improve said determination.

7. A method according to any one of the preceding claims, wherein the map data (ai,... an) is selected from information having a numerical value, preferably substantially continuous over a route, in particular the curvature of a bend having for example a value of local curvature angle of the road, the slope of a road having a value of local inclination angle of the road, the coefficient of friction of the road having a value of local friction coefficient of the road, the coefficient of roughness of the road having a value of local roughness coefficient of the road, the type of road, localized information on the maximum vehicle dimensions or the maximum vehicle mass permitted on a road.

8. A method according to any one of the preceding claims, wherein the determined polynomial correlation equation (46) is of rank 1, i.e. is a proportionality between the map data (ab ... an) and the representative quantity (mB ... mn) of said map data and / or a constant offset between the map data (ab ... an) and the representative quantity (mb ... mn) of said map data.

9. A method according to any one of the preceding claims, comprising determining differentials (44) each corresponding to a difference between the acquired cartographic data (ab... an) and the corresponding representative quantity (mB ... mn) and comparing the differentials (44) of the cartographic data to determine the polynomial correlation equation (46), the comparison of the differentials (44) comprising determining the polynomial correlation equation (46).

10. A method according to claim 9, wherein the determination of the correlation index Ir (46) is carried out at a correlation instant by comparing at least some, or better yet all, of the differentials corresponding to at least some of the map data acquired (ai,... an) prior to the correlation instant over a time window of predetermined acquisition duration ending at the correlation instant or corresponding to a predetermined number of successive map data acquisitions going back in time from the moment of correlation

11. A method according to any one of the preceding claims, comprising the determination of a correlation index associated with one or each of the map data acquired after the determination of the polynomial correlation equation (46), the correlation index corresponding to the correlation of the map data acquired after the determination of the polynomial correlation equation and the corresponding representative quantity with the polynomial correlation equation, the correlation index being determined in particular by calculating the difference between the contextualized map data and the corresponding representative quantity.

12. A method according to the preceding claim, comprising determining a new polynomial correlation equation if the correlation index Ir is less than the correlation threshold value Irs 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 less than a threshold index value.

13. A method according to one of the two preceding claims, comprising, as long as the correlation index Ir is greater than the threshold correlation value Irs or the correlation indices of the acquired map data are greater than a threshold index value, the use of contextualized map data, in particular by enriching a database with this map data or generating control data at least based on the contextualized map data, the control data being in particular an input data for a user information device or a vehicle actuator (1).

14. A method according to any one of the preceding claims, comprising determining context data from one or more sensors (3) of the vehicle (1) or from external data and the association of context data with the contextualized map data and / or the determined correlation equation (46), the context data including in particular the presence or absence of rain and / or its intensity, temperature, humidity level, wind speed and / or dew point.

15. A 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 / or the combination of the contextualized map data and the determined correlation equation.

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