Secure method for evaluating cartographic data

Abstract

A secure method for evaluating cartographic data. A method for securely evaluating cartographic information or data acquired by a sensor 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 cartographic data point, - the determination, for each acquired cartographic data point, of a difference between the cartographic data point and the corresponding quantities.- Determining a confidence index by comparing at least some of the differentials; - Generating at least one vehicle control data point based on the confidence index. Figure for the abbreviation: Fig. 2.

Description

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

[0001] The present invention relates to a method for the secure evaluation of map data by a moving vehicle and a vehicle comprising a computer program product configured to implement a method for the secure evaluation of 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 has limited reliability because it is transmitted via the internet, or Wi-Fi for certain V2X configurations, which constitutes a vulnerability, particularly in terms of cybersecurity. Indeed, eHorizon information is transported to the vehicle using technologies that represent a potential attack surface from a cybersecurity perspective, raising concerns about trust in this information and introducing a risk regarding its use as data. of vehicle actuation controls. This vulnerability prevents their use for the safe and direct control of vehicle actuators. 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] French patent application FR3133443Al discloses a method for evaluating and / or validating map data associated with road positioning for the control of a driver assistance system (also known as "ADAS"), in particular its activation, deactivation, or reconfiguration, or for sending an alert to the user regarding the validity of the map data provided by the navigation system. In this context, the map data is not used for the direct control of the vehicle's actuators. The user information informs the user that they cannot rely on the data sent by the navigation system, and the driver assistance system control allows for the deactivation or reconfiguration of the driver assistance system if the data is unreliable.

[0008] 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 mapping 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 grip 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.

[0009] There is therefore a need to evaluate or validate the security of mapping information from external sources received in a vehicle in a sufficiently reliable and continuous manner over time, in particular to allow a use of map information in the control of actuators, in particular by the chassis system or the micropropulsion unit. Description of the invention

[0010] The invention addresses this need, according to a first aspect, by means of a method for the secure evaluation of cartographic information or data acquired by a sensor 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 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 data point 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 cartographic data point, of a quantity representative of the cartographic data point from at least one measurement by at least one sensor at a vehicle location substantially equal to the cartographic position associated with said acquired cartographic data point, - the determination, for each acquired cartographic data point, of a difference between the acquired cartographic data and the corresponding quantities determined by measurement, - the determination of a confidence index by comparing at least some of the determined differentials, - the generation of at least one vehicle control data at least according to the determined confidence index.

[0011] 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 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 by In relation to a mobile system, particularly a vehicle, geographic location is defined as the position at the time of acquisition of the map data relative to the vehicle. 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.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] By "differential" is meant a real relative or absolute difference observed between the value of the cartographic data and that of the corresponding measured representative quantity.

[0016] Thanks to the invention, it is possible, by comparing map data from sources external to the vehicle on several acquisitions and corresponding quantities from measurements in the vehicle, to evaluate whether the map data and the measured data are consistent with each other and thus to establish whether confidence can be attributed to this map data or to the sensor measurements according to which the use of this data presents a safety to be determined.

[0017] In cases where the map data is considered reliable, the confidence index can provide information on the level of confidence to be placed in the vehicle sensor measurements. It can then inform the user of a sensor failure.

[0018] In cases where sensor measurements are considered reliable, the confidence index can provide information on the level of confidence to be placed in the mapping data. and potentially also to map data received by extending trust to all received information, with an acquisition in which map data has been established as reliable. Indeed, trust in this acquired map data can lead to trust in the data stream and therefore in all map information acquired simultaneously with the map data. It is then possible to have reliable information that can be used to generate at least some of the control data in the vehicle. Conversely, it is also possible to establish that external map data is unreliable and thus limit the control data to data originating from the vehicle itself, for the sake of user safety.

[0019] Performing this determination on a plurality of cartographic data at different times enhances security and provides a reliable confidence index. It then becomes possible to assess confidence over a time window between the first and last acquisition times, rather than on a single data acquisition at a single time. Comparing several acquisitions, in particular, makes it possible to detect differences over time that could indicate corruption of the cartographic information or a point error in the cartographic data. Furthermore, the comparison is no longer limited to equality; it is possible to take into account a different context by determining a correlation equation that differs from equality between the cartographic data and the corresponding representative quantities. This is not possible with a single differential.

[0020] The invention thus makes it possible to significantly limit the risks associated with a potential cyberattack, a failure of mapping information, or a sensor failure, without excluding mapping data that exhibits correlation with different contexts. This results in improved user security. The level of confidence in the system's operation is also strengthened.

[0021] Since the electronic horizon, or "eHorizon," is already a feature present on motor vehicles, notably used for ADAS control and confidence index determination using onboard sensors, the invention does not require any significant additional software or technical complexity. The cost of the proposed solution is therefore relatively low.

[0022] Such a method is therefore particularly suitable for 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 regulation algorithm of one or more actuators by distributing the instructions differently to the actuators based on acquired cartographic information. A system defined in this way is thus made more efficient. Navigation system

[0023] 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.

[0024] The acquired cartographic information is associated with a cartographic position on or along the determined most probable path.

[0025] 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.

[0026] 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

[0027] 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 gradient 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

[0028] 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. The map data can be chosen from all the aforementioned map information.

[0029] Preferably, it is chosen from information containing a numerical value, preferably substantially continuous over a path. It can be chosen from the curvature of a curve, for example, containing a value for the local curvature angle of the road; the slope of a road, containing a value for the local inclination angle of the road; the coefficient of friction of the road, containing a value for the local friction coefficient of the road; or the coefficient of roughness of the road, containing a value for the local roughness coefficient of the road. Having a numerical value allows for a more precise characterization of confidence. The fact that the data is substantially continuous over the path allows for sampling of the acquisitions and easy detection of an erroneous acquisition that would then need to be discarded.

[0030] 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.

[0031] Alternatively, the map data indicates the presence of road signs, including road signs, road lines, obstacles, or characteristic environmental features (e.g., historical monuments or rivers). The characteristic data may then include a boolean value, indicating presence or absence, and / or a numerical value, for example, indicating the speed limit, and one or more labels, describing the nature of the map data, for example, the type of sign identified, the type of road line, the nature of an obstacle, and / or the name of a characteristic environmental feature.

[0032] The method may include, for 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 item, a confidence index for a portion of the plurality of acquisitions and the generation of the control data based on the different confidence indices determined, in particular taking into account cartographic information only when confidence is established for all cartographic data, or even in particular taking into account only the cartographic information for which confidence has been established for the corresponding cartographic data, with cartographic information for which the corresponding cartographic data is not reliable being disregarded. Representative size

[0033] 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.

[0034] The representative quantity can be determined using a single sensor.

[0035] 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 piece of map data acquired.

[0036] 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.

[0037] 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.

[0038] 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

[0039] The acquisitions of the map data can be successive in time or based on the vehicle's positioning along its route. Each acquisition can be triggered automatically or manually.

[0040] Map information can be acquired by the navigation system at a predefined acquisition frequency, in particular when the vehicle is in motion.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Preferably, the determination of the confidence index is made at an evaluation time and takes into account at least the map data acquired at the upstream acquisition time closest to the evaluation time. Sliding window

[0046] Preferably, the determination of the confidence index is carried out at an evaluation time by comparing at least some, or better yet all, of the differentials corresponding to at least some of the cartographic data acquired prior to the evaluation time over a time window of duration a predetermined acquisition period ending at the evaluation time or corresponding to a predetermined number of successive map data acquisitions going back in time from the evaluation time. This allows the confidence index to be determined over a sliding time window prior to the evaluation time, corresponding to a constant predefined duration or a constant number of acquisitions, and thus to have an up-to-date confidence index for a predetermined number of map data points that is both large enough to ensure stability in the determination and small enough to limit the risk of error in the confidence assessment. Polynomial equation

[0047] The comparison of the differentials may include the determination, from the acquired cartographic data which enabled the determination of the confidence index and the corresponding representative quantities, 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 which were used to determine the polynomial correlation equation.

[0048] Preferably, the determined polynomial correlation equation has a rank less than or equal to 1, that is, it represents 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. "Proportionality" is understood to mean 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. "Constant lag" is understood to mean 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.

[0049] The method may include assigning the confidence index based on the correlation index, in particular assigning a confidence index greater than the threshold confidence value or equal to a discrete confidence value when the correlation index is greater than a threshold correlation value. For example, it is possible to assign a value of 0 to the confidence index when the correlation index is less than a threshold correlation value and a value of 1 to the confidence index when the correlation index is greater than a threshold correlation value. This makes it possible to determine the existence of a stable relationship between the acquired cartographic data and the corresponding representative quantity, and if such a stable relationship exists, to consider that there is a good level of confidence. This also makes it possible to disregard the context. Indeed, a context different from that corresponding to the The acquired map data can influence the determined value of the representative quantity, which can result in a polynomial equation between the acquired map data and the representative quantity. Since the value of map data can vary depending on certain contextual parameters, including rain, temperature, ice, humidity, or snow depth on the road, the method according to the invention makes it possible to generalize the validity of map data provided without context, or with a context different from that in which the vehicle is operating at the time the map data is acquired. Contextualization

[0050] The method may include the automatic correction of the map data acquired by the navigation system based on the correlation equation determined to contextualize the acquired map data if the correlation index is greater than a predetermined threshold correlation value by applying the determined correlation equation. The control data may be generated at least based on the acquired map data after contextualization when the confidence index is within a predetermined range of values ​​or at a predetermined discrete value.

[0051] Preferably, the determination of the correlation index is carried out at a correlation instant by comparing at least some of the differentials, better all of the differentials, corresponding to at least some 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 that the index 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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).

[0060] 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.

[0061] The threshold correlation value may depend on contextual parameters.

[0062] 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.

[0063] The threshold correlation value may depend on the number of map data sampled to determine the polynomial correlation equation. Confidence index

[0064] The confidence index can be a numerical value between two different numbers, for example between 0 and 1.

[0065] Alternatively, the confidence index may be discrete, in particular of the boolean type or defined by a discrete number of different values ​​each corresponding to different confidence levels, in particular at least three different values.

[0066] The generation of at least one control data can be at least based on a comparison of the confidence index with at least one threshold confidence value.

[0067] The generation of at least one control data can be at least based on a comparison of the confidence index with several different threshold confidence values.

[0068] The method may include generating control data based on a security level of the control data from among a plurality of different security levels and the confidence index, a range of values ​​for the confidence index being assigned to each security level to determine whether or not to take into account one or more mapping information in the generation of the order data. For example, order data can be associated with a first level of security corresponding to a first discrete value or a first range of values ​​of the confidence index and a second level of security corresponding to a second discrete value or a second range of values ​​of the confidence index, the first level of security being associated with order data for which taking into account an erroneous data has a negligible impact on the safety of users and the second level of security being associated with order data for which taking into account an erroneous data presents a risk for the safety of users.This reduces risks for users by allowing the use of potentially erroneous data when the impact of the erroneous data on security is negligible, and by refusing the use of potentially erroneous data if its use presents a risk to the user. Trust levels can be based on user security and / or user convenience related to the acceptance of the data.

[0069] Preferably, the confidence index is determined or updated periodically based on successive determinations of the differentials.

[0070] The frequency of updating the confidence index may be different from the sampling frequency of the map data, in particular the sampling frequency of the map data is a multiple of the frequency of updating the confidence index.

[0071] In particular, the confidence index can be determined or updated after a predefined number of acquisitions of the map data at different successive acquisition times, in particular greater than 2. For example, the confidence index can be updated following the calculation of 5 successive differentials.

[0072] The confidence index can be determined from an error function taking as parameters at least the differences between the acquired cartographic data, in particular after contextualization, i.e. the cartographic data acquired after contextualization, and the corresponding representative quantities, the error function also taking as parameters the said acquired cartographic data, in particular after contextualization, i.e. the cartographic data acquired after contextualization.

[0073] For example, the error function is chosen from the variance (or "L2"), the mean squared error, the absolute norm error (or "L1"), the maximum error or a combination thereof.

[0074] The error function can be relative. By "relative," it is meant that the error function makes it possible to quantify the discrepancies observed between the acquired cartographic data, particularly after contextualization where applicable, and the representative quantities corresponding to the values ​​of said acquired cartographic data, in particular after contextualization where appropriate.

[0075] Preferably, the threshold confidence value(s) depend on the type of map data acquired. For example, the threshold confidence value(s) associated with map data or a temperature sensor are different from those associated with map data or a road friction coefficient sensor.

[0076] The threshold confidence value(s) may depend on the number of map data sampled to determine the value of the confidence index.

[0077] The threshold confidence value(s) preferably depend on contextual parameters, in particular specific context data, for example, weather conditions. For example, the threshold confidence value(s) may differ depending on whether the weather is sunny, rainy, or snowy. Control data

[0078] Preferably, where the confidence index is characteristic of the confidence that can be placed in the map data, and the sensor measurement data is considered reliable, the control data is generated based on at least one of the acquired map data points if the confidence index is greater than or equal to a threshold confidence value. The control data can be generated based on at least one quantity representative of a map data point if the confidence index is less than the threshold confidence value.

[0079] The acquired map information taken into account to generate the control data can be the map data itself. In this case, the control data can be generated based on the acquired map data, particularly after contextualization where appropriate, if the confidence index is greater than or equal to a threshold confidence value.

[0080] Alternatively, at least one of the acquired map data points taken into account to generate the control data may be different from the map data. For example, if the confidence level associated with map data relating to the roughness coefficient is greater than the threshold confidence value, the control data may be established from map information combining temperature map data, as well as other map data relating to atmospheric pressure, or the roughness coefficient.

[0081] Preferably, in the case where the confidence index is characteristic of the confidence that can be placed in the measurements of a sensor, the corresponding map data being considered reliable, the control data can be generated as a function of at least one quantity obtained from the sensor measurement if the confidence index is greater than or equal to the confidence threshold value. The order data can be generated based on at least one of the acquired map data points if the confidence index is less than a confidence threshold value.

[0082] Preferably, the control data is sent to a vehicle system, in particular a vehicle actuator, said actuator being configured to control, alone or in combination with other actuators, the dynamic behavior of said vehicle, in particular by influencing the brakes, engine, steering and / or wheel speeds, depending at least on the input data.

[0083] Alternatively or in combination, the command data may be sent to a vehicle warning device, said warning device being configured to alert at least one user when the confidence index is less than the threshold confidence value.

[0084] The method may include the generation of a plurality of vehicle control data to control different vehicle systems, including different actuators, a vehicle warning device, the AD AS system, in particular for chassis control, in particular suspensions, torque setpoint motorization, in particular the internal combustion or electric motor, wheel motors, brakes, or even noise cancellation systems inside the vehicle (from the English "active noise control" or ANC). Safety direction

[0085] Preferably, when a map data exhibits a difference between the acquired map data, in particular after contextualization where appropriate, and the representative quantity that is less than a threshold value, and when taking into account in the generation of the control data the value of an acquired map data, in particular after contextualization where appropriate, is less secure than taking into account the value of the measurement of the representative quantity, the confidence index is determined to be less than the threshold confidence value temporarily at least for a predetermined period, in particular at least until the next acquisition of the map data.This can also be done based on the correlation index when it is below a threshold index value and when considering the value of map data acquired after contextualization in the generation of the control data is less reliable than considering the value of the measurement of the representative quantity. The confidence index is determined to be below the threshold confidence value temporarily, at least for a predetermined period, specifically at least until the next acquisition of the map data. This allows for the occasional correction of erroneous map data. the ad hoc decision to discard or retain erroneous cartographic data depending on the risks it may generate if taken into account.

[0086] Preferably, when a map data shows a difference between the acquired map data, in particular after contextualization where appropriate, and the representative quantity less than a threshold value and when taking into account in the generation of the control data the value of an acquired map data, in particular after contextualization where appropriate, is safer than taking into account the value of the measurement of the representative quantity, the confidence index is determined to be greater than the threshold confidence value temporarily at least for a predetermined period, in particular at least until the next acquisition of the map data.This can also be done on the basis of the correlation index when it is less than a threshold index value and when taking into account in the generation of the control data the value of said map data acquired after contextualization is safer than taking into account the value of the representative quantity, the confidence index is temporarily determined to be greater than the threshold confidence value at least for a predetermined period, in particular at least until the next acquisition of the map data.

[0087] By "safe," it is meant that taking into account acquired map data, particularly after contextualization where applicable, in the generation of control data may be associated with a certain level of risk depending in particular on the type of map data and its value, and optionally on the context. For example, taking into account a first map data point relating to the road's coefficient of friction with a numerical value of 0.9 is less safe than taking into account a second data point of the same type with a numerical value of 0.5. Indeed, a relatively low coefficient of friction exposes the vehicle to a greater risk of loss of traction than with a relatively higher coefficient of friction, and the vehicle uses safer control data for the user in the case of a lower coefficient of friction.Thus, incorrectly applying a low coefficient of friction value instead of a higher one causes the vehicle to adopt a safe driving position corresponding to low friction. This may result in a temporary loss of vehicle performance but does not endanger the user. Conversely, incorrectly assuming high friction when actual friction is lower can cause the vehicle to adopt a performance driving position, thereby creating a greater risk for road users when actual friction is lower.

[0088] Preferably, the predetermined duration is conditioned by a predefined minimum number of measurements of representative quantities to be carried out, in particular one or two to update the confidence index and / or the polynomial correlation equation, the confidence index and / or the polynomial correlation equation being updated in particular following said measurements. Discarding of a clearly erroneous piece of data

[0089] The comparison of differentials may involve discarding a manifestly erroneous data in the determination of the confidence index and / or the search for the polynomial correlation equation having a maximum correlation index with the representative data and quantities taken to search for the equation.

[0090] Preferably, data is considered manifestly erroneous if the difference between the acquired map data, particularly after contextualization where applicable, and the associated representative quantity is greater in absolute value than a predetermined error threshold, and if the closest acquired map data, particularly after contextualization where applicable, have differences between the acquired map data, particularly after contextualization where applicable, and the associated representative quantity that are less in absolute value than a predetermined error threshold. Thus, initially, the map data is identified as at risk, and the subsequent acquisition allows for the determination of whether the acquisition is erroneous.

[0091] Deleting a manifestly erroneous piece of data allows for a more precise and stable process.

[0092] The impact of a relatively large differential is mitigated or even completely eliminated, this discrepancy being caused by measurement noise from at least one sensor, or by a manifest and isolated error in a map data sensor.

[0093] 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.

[0094] 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.

[0095] 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

[0096] 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.

[0097] The actuator can be configured to control the chassis, in particular the suspension, the torque setpoint motor, in particular 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

[0098] The invention also relates to a vehicle equipped with at least one sensor and a navigation system enabling the acquisition, at an acquisition time, of cartographic information with its associated cartographic position and the location of the vehicle at the acquisition time, said vehicle further comprising a computer program product configured to, when the vehicle is in motion: - locate the vehicle and acquire mapping information, - to perform a plurality of acquisitions by the navigation system, at different acquisition times, of a cartographic data point among the cartographic information and of the cartographic position associated with said cartographic data point, the acquired cartographic positions associated with said acquired cartographic data point being geographically different from each other, - to determine, for each acquired map data point, a quantity representative of the map data point from at least one representative measurement by at least one sensor at a vehicle location calculated to be substantially equal to the associated map position at which said map data point is acquired, - to determine, for each acquired map data point, a difference between the acquired map data point and the corresponding quantities, - determine a confidence index based on the comparison of at least some of the differentials in the cartographic data. - generate order data based on the comparison of the confidence index to a predetermined threshold confidence value.

[0099] .

[0100] The characteristics described above in connection with the process also apply alone or in combination to the above vehicle. Brief description of the drawings

[0101] 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:

[0102] [Fig-1] Fig. 1 illustrates, schematically and partially, an example equipment for a vehicle adapted to implement the process according to the invention.

[0103] [Fig.2] The [Fig.2] is a block diagram illustrating steps of an example of a method for evaluating map data according to the invention.

[0104] [Fig.3] The [Fig.3] is a diagram illustrating steps of acquiring cartographic data and measurements of corresponding representative quantities according to the invention.

[0105] [Fig.4] The [Fig.4] is a block diagram illustrating steps of an example of a method for contextualizing cartographic data according to the invention.

[0106] [Fig. 5] [Fig. 5] schematically and partially illustrates various samples of cartographic data and measurements of corresponding quantities according to the invention. Detailed description

[0107] Figure 1 illustrates an example of a motor vehicle 1 in top view having equipment adapted to implement the method according to the invention.

[0108] Vehicle 1 can be thermal, for example of petrol, diesel, gas, hydrogen, or hybrid type, or even electric.

[0109] Vehicle 1 may include different sensors 3.

[0110] Vehicle 1 is also equipped with a computer 5, which receives data from the various sensors 3.

[0111] The calculator 5 includes one or more processors executing one or more programs enabling the implementation of the method according to the invention.

[0112] 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 vehicle 1 applications, one or more optional digital-to-analog converters and one or more input / output interfaces used to communicate with the various sensors 3.

[0113] The computer 5 can access an on-board memory storing a plurality of map data, as well as data measured by the sensors 3.

[0114] The calculator 5 can be connected to any type of interface allowing information to be presented to the user of the vehicle 1.

[0115] 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 vehicle 1 and / or for the latter to know its position in real time.

[0116] 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.

[0117] The remote server can include a plurality of map information.

[0118] 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 map information in certain areas. However, for use in the chassis system or the micro-powertrain, the level of security of the map information is often insufficient. For this reason, the invention relates in particular to a method for evaluating map information illustrated in [Fig. 2].

[0119] In the example considered, map data relating to the coefficient of adhesion of the road are used, but the method is generalizable to other types of data, such as the coefficient of roughness of the road, the temperature or external pressure, the curvature of a bend, the slope of a road, or any other type of map data measurable directly or indirectly by a sensor 3 of the vehicle 1.

[0120] A plurality of cartographic data acquisitions of road adhesion coefficient ab a2, .. .an with their associated cartographic position Pdi, Pd2, .. .Pdn at different acquisition times Tb T2 ... Tn is carried out by the navigation system 7 during a first acquisition step 20.

[0121] In [Fig. 2], n successive acquisition times of map data are represented by Ti, T2 ... Tn. The sampling of the acquisition of road friction coefficient values ​​ab a2, .. .an is thus performed over N acquisitions. The number of acquisitions is chosen, in particular, according to the map data. The n acquisition times Ti, T2 ... Tn can be spaced regularly in time and cover a predetermined acquisition time (Tn-Ti).

[0122] At this acquisition stage 20, for each value of the coefficient of adhesion acquired by the navigation system 7, a measurement of the coefficient of adhesion mi m 2, .. .mn is carried out by a sensor 3 of the vehicle 1, in this case a sensor 3 located at the wheels of said vehicle 1. The measurement of the representative quantity can be direct by measuring the quantity with a sensor 3 or deduced from data acquired by one or more sensors 3 of the vehicle 1.

[0123] The measurements of the road adhesion coefficient mim2, .. .mn are carried out at n vehicle locations Pvi', Pv2', ...Pvn' substantially equal to the cartographic positions of the cartographic data acquired according to the principle detailed below in connection with [Fig.3].

[0124] The process then includes a comparison step 24 involving the determination of differentials between each value of adhesion coefficient aB a2, ... an and corresponding measurement of sensor 3 mi,m2, .. .mn.

[0125] For example, for an acquired coefficient of adhesion value a of 0.90 and a corresponding measurement m carried out by the sensor 3 of the vehicle 1 of 0.87, the differential d determined in absolute value la-ml is 0.03.

[0126] Step 24, comparing the differentials dH, d2, ..., dn, may involve discarding a clearly erroneous data point so that it is not taken into account in determining the confidence index Ic. This may be the case, for example, if an absolute value differential is greater than a first predetermined error threshold ds i, and the absolute value differentials of the map data acquired at least at the nearest acquisition times are less than a second error threshold ds 2, which is lower than the first error threshold ds[. Alternatively, this may also be the case when a value of the map data or representative quantity is clearly erroneous or aberrant in light of the acquired nearby values, without it being necessary to calculate a deviation. This makes it possible to exclude a map data point that would have an aberrant value compared to the other nearby values.

[0127] During step 26, a confidence index L is determined from the differentials di , d2, .. .dn determined in the previous step 24.

[0128] In the case of a context substantially identical between that in which the map data was when recorded in the database and that in which the vehicle 1 is located, an equality of the values ​​acquired by the navigation system 7 via the map data and the values ​​measured by the sensor 3 of the vehicle 1 is sought.

[0129] In this case, the confidence index Ic is determined from the differentials dB d2, ... dn determined in the preceding step 24 and the values ​​of the road friction coefficients acquired in step 20. For example, for n=3, denoting A = [a^ a2, a3] the list of acquired friction coefficient values ​​and D = [db d2, d3] the list of their respective successive differentials, the confidence index Ic can be defined by Ic = 1- f(D, A) with f the relative error function in absolute magnitude, i.e.

[0130] T _ly^

[0131] For A = [0.90 ;0.89 ;0.80] and D = [0.03 ; -0.03 ; 0.05], the calculation gives Ic= 0.96.

[0132] The confidence index Ic is then compared to a threshold confidence value Ics in step 28.

[0133] The threshold confidence value Ics can be set at 0.95 to limit confidence in the adherence coefficient values ​​within the 5% error limit. Thus, if the confidence index Ic is greater than this value, the data are considered reliable, as in the example above. Conversely, if the confidence index L is less than this threshold value, the data are not sufficiently reliable.

[0134] In the case of a different context between that in which the map data were recorded in the database and that in which vehicle 1 is located, the determination of the confidence index Ic is no longer based on equality. Indeed, a more significant difference can be observed between the values ​​acquired through the map data and the representative quantities determined by the measurements. In most cases, the context will lead to a constant or proportional difference between the values ​​acquired through the map data and the representative quantities determined by the measurements. This difference can substantially follow a known first-order correlation equation or one that can be determined from the comparison of the different differentials dh, d2, ..., dn.

[0135] Thus, step 26 may involve determining a polynomial correlation equation, for example of rank 1, from at least a part of the differentials dh d2, .. .dn. The equation may be of the form y=cx+b, x being the cartographic data, y being the corresponding representative quantity, c being a proportionality factor and b a constant value.

[0136] The polynomial equation is for example determined from a polynomial regression.

[0137] The polynomial equation determined is that having a maximum correlation index L with the cartographic data and the representative quantities taken into account, for example the Bravais-Pearson correlation index.

[0138] Figure 5 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.

[0139] The acquired cartographic data are represented on the dashed line curve, and the measurements of representative quantities on the solid line curve.

[0140] The graph in Figure 5a) 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.

[0141] The graph in Figure 5b) 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.

[0142] 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.

[0143] The graph in Figure 5c) shows a sample of acquired cartographic data and corresponding representative quantity measurements without a rank 1 polynomial equation and with a good determinable correlation index Ir. The confidence index corresponding to such a sample of cartographic data is low.

[0144] In this case, the confidence index Ic may depend on the determined correlation index Ir. It may be greater than a threshold confidence value Ics or equal to a discrete value representing confidence Here in the case where the correlation index Ir is greater than a threshold correlation value Irs.

[0145] For example, if the determined polynomial correlation equation has a correlation index Ir of 0.9 and the threshold correlation value Irs is 0.85, the map data is considered to be of confidence and the confidence index Ic is significant of confidence in the map data.

[0146] Conversely, the confidence index L is determined to be less than the threshold confidence value Ics or equal to a discrete value representing a lack of confidence Lo in the case where the correlation index Ir is less than a threshold correlation value Irs.

[0147] For example, if the polynomial correlation equation with maximum correlation index has a correlation index Ir of 0.5 and the threshold correlation value is 0.85, the correlation between the values ​​of the adhesion coefficient and the corresponding measures is considered poor, the map data is not considered to be of confidence and the confidence index Ic is significant of a lack of confidence.

[0148] Confidence can be determined from the analysis of differences between the cartographic data acquired after contextualization, corresponding to the acquired cartographic data corrected from the polynomial correlation equation, and the representative quantities by seeking an equality of equivalent values. The case is then equivalent to that described previously in the case where the contexts were identical.

[0149] The method includes, in step 28, comparing the confidence index Ic with one or more threshold confidence values. This can be done by comparison with the threshold confidence value Ics or by comparison with discrete values ​​from a plurality of discrete values ​​representing different levels of security [Ic0Ici]-

[0150] A filtering step 30 is then carried out, comprising the generation of a control data for vehicle 1, in particular to an actuator of vehicle 1, according to the comparison of step 28.

[0151] If the map data ai a2, ... an are reliable, the control data can be established based on the adhesion value data acquired by the navigation system 7 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. This data may, for example, indicate a decrease in the road adhesion coefficient to 0.5. Where appropriate, the cartographic data used for the order data can be contextualized upstream by applying the determined correlation equation to it.

[0152] Furthermore, a high level of confidence in the acquired map data can indicate a general level of confidence in the map information acquired by the navigation system 7 at the time of acquisition, and therefore the absence of cybersecurity risks. It is then possible, as long as the confidence index Ic is greater than the threshold confidence value, to use the acquired map information for purposes other than map data. In this context, it is possible to use map information other than that used to determine confidence, for example, information on road signs or the road layout, based on a confidence index Ic determined using the adhesion coefficient.It is also possible to contextualize other map information, in particular that of roughness or road layout according to the method described below, or to validate the measurements made by a sensor 3 of the vehicle 1 from map information according to the previous method by applying the confidence index L to the representative quantities to detect a malfunction of a sensor 3, the map information being considered as reliable.

[0153] Finally, the process ends with a control step 32 during which the vehicle 1 is controlled based on the control data, for example the actuator influences the dynamic behavior of the vehicle 1 in an anticipatory manner, by example here on the brakes of vehicle 1 based on information considered reliable regarding the decrease in road grip.

[0154] If the map data aia2, ... an are not reliable, the control data can be established based on measurements of representative quantities by the wheel sensor 3 only.

[0155] The method may include recording or sending map data acquired after contextualization to a remote server when the data is established as trustworthy. This sending of contextualized data may be accompanied by contextual information.

[0156] Preferably, the confidence index L and the correlation index L are evaluated periodically at different or identical update frequencies, based on a new sampling of map data acquired at later times. Some of the data may overlap between the two samplings. The confidence in said map data, and in the correlation model of said data, thus evolves over time. Alternatively, the update of the confidence index Ic and / or the correlation index Ir can be carried out non-periodically, particularly when a mismatch between the polynomial correlation equation and the map and measurement data is identified over a period exceeding a predetermined time or a predetermined number of acquisitions.Preferably, the determination of the confidence index (Ic) and / or correlation index (Ir) is based on a predetermined number of map data points acquired directly prior to the determination, i.e., directly preceding the determination, or on a predetermined evaluation period ending at the time of the Ic or Ir confidence index evaluation. This allows for a sliding window determination with the evaluation time remaining constantly up-to-date. As explained previously, the number of map data points to be considered for this evaluation can be fixed or variable depending on whether one or more data points are excluded because they are aberrant or less reliable, as explained below.

[0157] When a polynomial correlation equation has already been determined, a correlation index can then be determined for each subsequent acquisition. Such a correlation index establishes the correlation between the acquired map data and the representative quantity based on the previously determined polynomial correlation equation. It can correspond to a difference between the map data acquired after contextualization and the corresponding representative quantity. Such a correlation index can make it possible to identify erroneous map data, as mentioned above, or to identify a Decorrelation with respect to the polynomial correlation equation. A decorrelation across several successive map data points can lead to an update of the polynomial correlation equation or the confidence index Ic.

[0158] The method may include taking into account, or not taking into account, map data, particularly friction coefficients, acquired for the operation of vehicle 1, if taking into account or not taking into account the data exposes vehicle 1 to a safety risk compared to the map data acquired directly previously. The safety risk may arise directly from the road friction coefficient value indicated by the map data, for example, 1.2, whereas the last measurement corresponded to a value of 0.6. In this first case, it may be dangerous to take into account the friction coefficient value of 1.2, which could cause vehicle 1 to behave dangerously if the road has a friction coefficient of 0.6, as before. Therefore, the map data value is chosen not to be taken into account temporarily while the next map data is being viewed.Conversely, if taking the map data into account is safer, we can decide not to disregard it. If the trend is confirmed in either case with the subsequent data, we can then review the correlation equation.

[0159] All the examples described above are based on the presence of a single confidence or correlation threshold value. Alternatively, the method may include a plurality of confidence or correlation threshold values ​​and the determination of a security level associated with each of the value ranges defined by the confidence threshold values. It is then possible to assign to each control data a confidence level necessary for taking into account the acquired mapping data, particularly after contextualization where applicable, in the control of vehicle 1. Alternatively, it is possible to assign to each control data a confidence or correlation threshold value, the control data having different safety criticalities.Some control data may then have a lower confidence requirement because they are not safety critical, and other data may have a higher confidence requirement because they are safety critical. Figure 3 schematically illustrates a vehicle 1 moving from left to right along a road represented by a first axis 36, and the acquisition of map data and the determination of the corresponding representative quantity.

[0160] 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.

[0161] 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.

[0162] Vehicle 1 acquires at position Pvn a road adhesion coefficient value geolocated to a map position Pdn located in front of it.

[0163] 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.

[0164] Similarly, a new road friction 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.

[0165] 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. They 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.

[0166] 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.

[0167] 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.

[0168] 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+L

[0169] An example of a process for contextualizing cartographic data is shown in [Fig.4].

[0170] In the example considered, the map 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 map data that can be measured directly or indirectly by one or more sensors 3 of the vehicle 1.

[0171] 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.

[0172] In [Fig.4], 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.

[0173] 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.

[0174] The road roughness coefficient measurements are carried out at different times according to the same principle as in the case of the evaluation of the cartographic data described above.

[0175] The process then includes a step 44 of determining differentials between each value of acquired roughness coefficient ki k2, ... ku and the corresponding representative quantities vb v2, ...vu.

[0176] 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.

[0177] The determination of the maximum correlation index Ir and the associated correlation model can, for example, be carried out by the least squares method.

[0178] 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\

[0179] c' being a proportionality coefficient and b' being a constant representing a fixed difference.

[0180] The correlation index Ir is compared to a threshold correlation index Irs in step 48.

[0181] In a first scenario, the determined correlation index Ir is greater than the threshold correlation index Irs. The map data 50 representing the road roughness coefficient can then be contextualized with the determined polynomial correlation equation.

[0182] 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.

[0183] 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.

[0184] The method may include recording or sending contextualized data to a remote server when the confidence level Ic and the correlation level Ir are respectively greater than a threshold confidence value Ics and a threshold correlation value Irs. The method may also include recording contextual data from data within vehicle 1 or from outside vehicle 1, for example, temperature, humidity, wind, rain, or ice. This data may be transmitted with the acquired map data, particularly after contextualization where appropriate, to record the acquisition context of said data.

[0185] 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.

[0186] The method may include, 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 by The method is based on a difference between the acquired map data, particularly after contextualization where applicable, based on the polynomial correlation equation, and the corresponding determined characteristic quantity. The method may involve discarding map data whose correlation index falls below a predetermined threshold and / or for which a less safe command in the control of vehicle 1 is executed if it is taken into account. The method may also involve determining a new polynomial correlation equation when a predetermined quantity of successive map data is uncorrelated with the determined equation, i.e., exhibits a characteristic index of decorrelation, particularly below a predetermined threshold.

[0187] The invention is applicable to any technical field and any system. It can be generalized to the validation of future mapping data from data measured in the present, or to the confirmation of measured data using mapping data. Thus, it is possible to perform a secure evaluation of measured data from corresponding mapping data considered reliable and acquired securely.

[0188] 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.

Claims

1. Demands A method for the secure evaluation of map information or data acquired by a sensor (3) 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 (Tb ... Tn), of a cartographic data (ab... an) from among the cartographic information with the cartographic position (Pdi, ... Pdn) associated with said cartographic data (ai,... an), the cartographic positions (Pdi, ... Pdn) associated with said acquired cartographic data (ab... an) being geographically different from each other, the cartographic positions (PdB ... Pdn) associated with said acquired cartographic data (ai,... an) being at a distance (rb ... rn) and / or a travel time relative to the vehicle that is not zero at the corresponding acquisition time (Tb ... Tn), - the determination of the distance along the most probable path between the vehicle (1) at the time of acquisition (Tb ... Tn) and the map position (Pdb ... Pdn) associated with said map data acquired (ai,... an) at said time of acquisition (Tb ... Tn), - the determination (20), for each acquired cartographic data, of a representative quantity (mb ... mn) of the cartographic data 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 cartographic position (Pdb ... Pdn) associated with said acquired cartographic data (ab... an), - the determination (24), for each acquired cartographic data (ai,... an), of a differential (dB ...dn) between the acquired cartographic data (ai,... an) and the corresponding representative quantities (mb ... mn), - the determination of a confidence index Ic (26) by comparison of several of the determined differentials (db ...dn), - the generation (50) of at least one vehicle control data (1) at least as a function of the determined confidence index Ic.

2. A method according to claim 1, wherein the control data is generated based on at least one of the acquired map information if the confidence index Ic is greater than a threshold confidence value Ics and on at least one quantity representative of at least one of the acquired map information if the confidence index Ic is less than the threshold confidence value Ics.

3. A method according to claim 2, wherein the control data is sent to a system of the vehicle (1), in particular an actuator of the vehicle (1), said actuator being configured to control, alone or in combination with other actuators, the dynamic behavior of said vehicle (1), in particular by influencing the brakes, engine, steering and / or wheel speeds, depending at least on the input data.

4. A method according to any one of the preceding claims, wherein the map data is 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 coefficient of friction of the road comprising a value of the local friction coefficient of the road, the coefficient of roughness of the road comprising a value of the 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.

5. A method according to any one of the preceding claims, wherein the map information is acquired by the navigation system (7) at a predefined acquisition frequency, the map data acquisitions being sampled over time or based on the vehicle's (1) positioning along its route at a predetermined sampling frequency that is the same as, or different from, the acquisition frequency of the map information, in particular at a sampling frequency corresponding to a acquisition of map data every N acquisitions of map information by the navigation system (7), N being an integer.

6. A method according to any one of the preceding claims, wherein the distance between the acquired map positions (Pdi, ... Pdn) and the vehicle locations (Pvb ... Pvn) at the corresponding acquisition time (Tb ... Tn) is substantially constant.

7. A method according to any one of the preceding claims, wherein the determination of the representative quantity (mb ... mn) for each acquired map data (ai,... an) involves determining, as a function of the speed of the vehicle at the time of acquisition (Tb ... Tn), the measurement time (Tf, ... Tn') so that the location of the vehicle at the measurement time (Pvi', ... Pvn') is substantially equal to the map position (Pdb ... Pdn) associated with the map data (ai,... an), the measurement time (Tf, ... Tn') being determined in particular at least from the distance on the most probable path between the vehicle (1) at the time of acquisition (Ti, ... Tn) and the map position (Pdi, ... Pdn) associated with the map data acquired at the time of acquisition and the speed of the vehicle at the time of acquisition.

8. A method according to any one of the preceding claims, wherein the determination of the confidence index Ic (26) is carried out at an evaluation time by comparing at least some of the differentials (db ...dn), better all the differentials, corresponding to at least some of the map data acquired (a^... an) upstream of the evaluation time over a time window of a predetermined acquisition duration ending at the evaluation time or corresponding to a predetermined number of successive map data acquisitions going back in time from the evaluation time.

9. A method according to any one of the preceding claims, wherein the comparison (24) of the differentials (db ... dn) comprises the determination (26), from the acquired cartographic data (ai ,... an) which enabled the determination of the confidence index Icet of the corresponding representative quantities (mb ... mn), of a polynomial correlation equation between the cartographic data and the representative quantity of the cartographic data of a rank less than a predefined rank, in particular less than or equal to rank 1, and maximum correlation index Ir with the acquired cartographic data (ai,... an) and the corresponding representative quantities (m^ ... mn) used to determine the polynomial correlation equation and includes the assignment of the confidence index Icen as a function of the maximum correlation index Ir, in particular the assignment of a confidence index Ic greater than a threshold confidence value Icsou equal to a discrete confidence value when the maximum correlation index Ir is greater than a threshold correlation value Irs.

10. A method according to the preceding claim, comprising determining a new polynomial correlation equation if the correlation index Irest is less than the correlation threshold value Irssur for a period exceeding a predetermined period, in particular the acquisition period, or over a number of successive acquisitions exceeding a predetermined number, or if correlation indices of map data acquired over a period exceeding the predetermined period, in particular the acquisition period, or over a number of successive acquisitions exceeding the predetermined number are all less than a threshold index value,the correlation indices 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.

11. A method according to any one of claims 9 and 10, wherein the comparison (24) of the differentials (dB ...dn) involves discarding a manifestly erroneous data point in the determination of the confidence index (26) and / or in the search for the polynomial correlation equation (26) having a maximum correlation index Ir with the representative data and quantities taken to search for the equation.

12. A method according to any one of the preceding claims, wherein, when an acquired map data (ai,... an) exhibits a difference between the acquired map data (ai,... an), particularly after contextualization where appropriate, and the magnitude representative (mb ... mn) less than a threshold value and when taking into account in the generation of the control data the value of an acquired cartographic data (ab... an), in particular after contextualization where appropriate, is less secure than taking into account the value of the representative quantity (mb ... mn), the confidence index Ic is determined to be less than the threshold confidence value Ics temporarily at least for a predetermined period, in particular at least until the next acquisition of the cartographic data.

13. A method according to any one of the preceding claims, comprising the determination by the navigation system (7) of the most probable path of the vehicle (1) based on user input data, information acquired from one or more sensors (3) of the vehicle (1) and / or the location of the vehicle (1) determined by the navigation system (7).

14. A method according to any one of the preceding claims, comprising the generation of control data (30) as a function of a security level of the control data among a plurality of different security levels and of the confidence index Ic, a range of values ​​of the confidence index Ic being assigned to each security level to determine whether or not to take into account one or more map information in the generation of the control data (30).

15. A vehicle equipped with at least one sensor (3) and a navigation system (7) enabling the acquisition, at an acquisition time, of map information with its associated map position and the location of the vehicle (1) at the acquisition time, said vehicle (1) further comprising a computer program product configured to, when the vehicle (1) is in motion: - locate the vehicle (1) and acquire map information, - perform a plurality of acquisitions (20, 40) by the navigation system (7), at different acquisition times (Tb ... Tn), of a map data item (ai,... an) from among the map information and of the map position (Pdb ... Pdn) associated with said map data item (ab... an), the positions Given that the acquired cartographic data (Pdi, ... Pdn) associated with said acquired cartographic data (ai,... an) are geographically different from each other, and that the cartographic positions (Pdb ... Pdn) associated with said acquired cartographic data (ai,... an) are at a distance (rb .. .rn) and / or a non-zero travel time relative to the vehicle at the acquisition time (Tb ... Tn), determine the distance along the most probable path between the vehicle (1) at the acquisition time (Tb ... Tn) and the cartographic position (Pdb ... Pdn) associated with said acquired cartographic data (ab... an) at said acquisition time (Tb ... Tn). - determine (20), for each acquired map data, a quantity representative of the map data from at least one representative measurement (mb ... mn) by at least one sensor (3) at a location of the vehicle (1) (Pvi', ... Pvn') calculated to be substantially equal to the map position (Pdb ... Pdn ) associated with which said map data is acquired (ab... an), - determine (24), for each acquired cartographic data (ai ,... an), a differential (db ...dn) between the acquired cartographic data (ab... an) and the corresponding quantities (mb ... mn), - determine a confidence index Ic (26) as a function of the comparison of several of the differentials (24) of the cartographic data. - generate an order data (30) based on the comparison (28) of the confidence index Ic to a predetermined threshold confidence value Ics.

Images