METHOD FOR PRESENTING DATA RELATING TO ROAD CONDITIONS

Description

[0001] The invention relates to a method for the cartographic representation of data concerning the state of a road. The invention notably, but not exclusively, implements a method for detecting the state of the road and of the tire fitted to a vehicle moving on this road, from the noise generated by the tire when it comes into contact with the ground.

[0002] It is indeed useful to know the state of the road or the tire at any time, to interact with the driver or with the driver assistance systems, so as to inform them in real time of the evolution of the driving conditions, and more generally, of the possible modification of the grip conditions of the tire and the road holding of the vehicle. These methods are therefore intended to highlight the evolution of weather conditions such as the transition between driving conditions on dry, damp, wet, snowy or icy ground.

[0003] In order for this data to be usable, it is useful for it to be displayed in a readable and understandable manner for a user such as a vehicle driver.

[0004] We know of maps that allow us to know the weather on a certain route in real time, for example from data provided by meteorological services. However, although they allow us to have an initial idea of ​​the driving conditions on a route, they do not allow us to know precisely the state of a road at a given moment, information that could allow a driver, or the safety system of a vehicle, to adapt the driving conditions, and therefore reduce the risk of accidents.

[0005] Document US 2015 / 106299 A1 discloses a method and system for monitoring and maintaining roads. Dedicated vehicles travel the roads, recording their positions and associated camera images, and send their data to a server that centralizes and processes the received data. The GPS tracks of the vehicles are displayed on an interactive map that also displays the acquired images of the road associated with the GPS positions. The condition of the road (snow, rain, ice, potholes) can thus be viewed on the map, and maintenance vehicles can be sent when necessary (snow removal, salting, repairs, etc.).

[0006] Document US 2013 / 155061 A1 discloses a method and system for monitoring and evaluating the condition of a road surface by recording an image by a camera mounted on a vehicle traveling on the road, thereby making it possible to evaluate the condition of the surface texture and identify defects and damage.

[0007] The paper "Demo: Mobile Crowdsensing of Road Surface Roughness" by Borriello Gaetano et al. (Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '15, Florence, Italy, May 18 - 22, 2015, May 18, 2015, pages 439-439, XP093162803) discloses the display on a map of the condition of a road surface recorded by vehicle-mounted sensors, in which a color code superimposed on the road representation indicates the surface roughness.

[0008] The paper "Implementation of a multi-modal mobile sensor system for surface and subsurface assessment of roadways" by Wang Ming et al. (PROCEEDINGS OF SPIE, IEEE, US, vol. 9436, March 27, 2015, pages 943607-943607, XP060052485) discloses the display on a map of the condition of a road surface recorded by vehicle-mounted sensors, in which a color code superimposed on the representation of the roads indicates an equivalent pavement condition index.

[0009] The paper titled "Road condition and texture estimation by fusing GPS, accelerometer and camera data" by Deepak Rajamohan (September 1, 2014, XP093162922) discloses the display on a map of the condition of a road surface recorded by sensors mounted on a vehicle, in which a color code overlaid on the vehicle's GPS track indicates the type of surface.

[0010] Thus the invention aims to remedy this shortcoming by proposing a method for cartographic representation of data concerning the state of a road according to claim 1.

[0011] In a preferred embodiment, the weather condition is included in the group comprising: a dry condition, a humid condition and a wet condition or a winter condition such as a snow or ice condition. A snow condition may, in a preferred embodiment, be included in the group comprising a fresh snow condition, a compacted snow condition, a granular snow condition and a slush condition.

[0012] A wet state is characterized by a water level that is flush with the natural roughness of the road surface; this wet state corresponds, for example, to the state obtained by a light rain or a road that dries out after a heavy rain. A wet state, on the other hand, is characterized by a water level that exceeds the level of the natural roughness of the road surface. In practice, the wet state corresponds to a water level generally between 0.5 millimeters and 1 millimeter.

[0013] According to the invention, during the recording step, information concerning the macro-texture of the coating is also recorded, and in this case the display step also comprises the representation of this macro-texture. This macro-texture of the coating is, preferably, included in the group comprising a closed state, a medium state and an open state.

[0014] A surface is considered a closed surface when it takes on a smooth appearance without roughness, such as bitumen that has oozed after being subjected to high heat. A surface will be considered open when the roughness is significant, for example, such as that of a worn surface or that of a country road quickly repaired using a surface coating made by spraying pebbles onto bitumen. A medium surface describes all surfaces in an intermediate state between the two previous states and more specifically describes new surfaces.

[0015] The macro-texture of a coating affects its water drainage properties as well as its acoustic properties. This texture is characterized by a "Mean Texture Depth", also called "MDT". This MDT is measured by the height to true sand, also noted HSv, well known to those skilled in the art.

[0016] The different macro-textures can be categorized as follows: a closed macro-texture coating has a PMT of between 0 and 0.4 millimeters. A medium macro-texture coating has a PMT of between 0.4 and 1.1 millimeters, and an open macro-texture coating has a PMT greater than 1.1 millimeters.

[0017] It is specified here that the display of the road weather condition and the display of the macro-texture of the surface can be carried out independently of each other on two different maps, or mentioned on the same map.

[0018] In yet another preferred embodiment, during the recording step, information is also recorded concerning a specific event affecting the state of the road, and in this case, the display step also comprises the representation of this event.

[0019] These events, also called "road hazards", correspond to obstacles or occasional changes to the road surface, such as the presence of gravel, earth or sand on the road, or even deterioration such as potholes. All of these events can have an influence on the driving conditions of a vehicle on the road, and it is therefore useful, in one embodiment, to be able to know its position on a route.

[0020] As mentioned above, the invention finds an advantageous application in driving assistance for drivers or for safety systems installed on vehicles. In order for this assistance to be relevant, and taking into account the punctual and changing nature of the various elements influencing grip, it is useful for the maps to be updated in real time. To this end, in a preferred embodiment, the various information concerning the state of the road is determined in real time by the vehicle, for example at a frequency of three determinations per second, and is immediately transmitted to a remote database. This transmission is carried out by telecommunications means, for example by GSM.

[0021] In order to further improve the quality of the information provided on the map, in an advantageous embodiment of the invention, information and GPS positions from several different vehicles are recorded. Indeed, such a feature makes it possible to obtain a greater number of data concerning the state of the road on a given route, and thus to reinforce the “real-time” aspect of the invention. This data is then grouped together to be displayed in the form of a single map for all the vehicles.

[0022] In order to allow a correct update of the data transmitted by different vehicles, it is useful, in an advantageous embodiment, for a method according to the invention to comprise the step of recording, in addition to the GPS position of the vehicle, the date and time of the recording. Thus, it becomes possible to update the display if recorded data concerns a road segment for which data has already been displayed, provided that the recorded data has a date and time later than the data previously displayed. It should be noted that this update is particularly advantageous for data concerning the weather condition of the road or specific events. Indeed, the macro-texture of the surface, for its part, does not change very quickly over time.

[0023] Concerning one-off events, it is possible, in one embodiment of the invention, to wait to receive matching information from several vehicles before displaying the presence of an event, in order to robustify the displayed data.

[0024] A method according to the invention can find several advantageous applications. Thus, in one example, such a method makes it possible to estimate, based on the different recordings made and their GPS position, the grip characteristics of the road, and this method further comprises the step of displaying these characteristics on a map of the route. By "grip characteristic" is meant, for example, the drainability of the road, which can be estimated in view of the macro-texture of the surface. It is also possible to envisage combining the different data to obtain an indication of a level of grip of the road. However, this data does not take into account the tires or the vehicles, and therefore cannot precisely determine the grip coefficient for a vehicle traveling on a road.

[0025] In another advantageous application, a method according to the invention makes it possible to estimate, based on the various recordings made and their GPS position, the acoustic characteristics of the road surface on a route, and further comprises the step of displaying these characteristics on a map. These characteristics are useful to know because they give an indication of a potential noise level generated by a vehicle traveling on the route. Indeed, it is known that the noise generated by a vehicle traveling on a road surface depends on the characteristics of this surface. Thus, it is known, for example, that the more closed a road surface is, the more it will excite the tread pattern of a tire and generate noise. The noise is higher when the tire tread pattern is new. In the same way, an open road excites the vibration modes of the tire, which also generate noise outside the vehicle, regardless of the condition of the tire.

[0026] However, as with the grip characteristics, it is not possible to establish a precise map of the noise generated, because this noise depends on each vehicle, as well as on the tires and their condition.

[0027] In another advantageous application, a method according to the invention comprises the step of determining, based on the various recordings made, areas of deterioration of the coating, and further comprises the step of displaying these areas on a map. Indeed, information such as the macro-texture of the coating and specific events are of interest to operators of road and motorway networks, and can make it possible to know in real time which areas of their networks require maintenance action.

[0028] In yet another advantageous application, a method according to the invention comprises the step of identifying, based on the various recordings made, areas in which the road weather condition is a winter condition, namely a snow condition or an ice condition. Indeed, such information allows road and motorway network operators to know the areas of their networks requiring action such as sanding to remedy winter traffic conditions.

[0029] In a preferred embodiment of the invention, the step of determining by the vehicle the weather condition of the road or the macro-texture of the surface comprises the following steps: a measurement of a sound signal produced by the tire rolling on a road surface is recorded during a given time frame, a power spectral density of the sound signal is determined over a given frequency interval, the frequency interval is segmented into a plurality of frequency bands of previously determined widths, and each frequency band is associated with data representative of an average sound power measured in said frequency band, the representative data from a measurement forming variables of a vector associated with said measurement, a state of the road and of the tire corresponding to the vector associated with the measurement carried out is determined using a discriminant analysis of the data based on a learning base formed from a set of vectors associated with previously recorded and carried out measurements, according to the same steps as above,under known driving conditions according to methods each representing a given state of the road and the tire, , characterized in that the representative data forming the variables of the vector associated with a measurement are obtained by calculating the ratio between the average sound power measured in a frequency band and a total sound power measured over the entire frequency interval.

[0030] The determination of the road condition is thus based on the observation that the sound frequency and intensity generated by the tire vary when the road condition changes, and analyzes sound recordings made by microphones placed near the tire and the road. From the recording of a frequency spectrum of sound powers, the meteorological condition of the road is determined using judiciously chosen ratios or by comparing this spectrum with pre-recorded data.

[0031] Sound recordings are made using a microphone carefully placed on the vehicle. The spectral density of the sound power is distributed over a given frequency interval. This spectrum varies depending on a set of modalities such as weather conditions, road conditions, tire wear, tire tread pattern, and to a lesser extent, inflation pressure, load, etc. One of the major modalities likely to modify this spectrum, all other conditions remaining equal, is the vehicle speed at the time the measurement is taken.

[0032] It has been shown that this variation can be practically neutralized if, instead of considering the power spectrum from the measurement, this spectrum is recalculated by "normalizing" the measured data using the total sound power recorded during the measurement time over the entire frequency interval. This aims to erase the speed effect and make the measurement substantially invariant to this modality.

[0033] The method is characterized in that the representative data forming the variables of a vector associated with a measurement are obtained by calculating the ratio between the average sound power measured in a frequency band and the total sound power measured over the entire frequency interval. In this way, unlike existing methods in the state of the art, it is no longer necessary to take speed into account, in order to obtain, from a single acoustic measurement and without it being necessary to introduce additional parameters to interpret the measurement, reliable information on the state of the road and, as will also be seen, on the state of the tire.

[0034] The method according to the invention may also comprise, in isolation or in combination, the following characteristics: The total sound power measured is equal to the sum of the average sound powers of all frequency bands in the frequency interval considered. The frequency bands are determined by dividing the frequency interval by third of an octave. The time frame of a measurement is less than or equal to 0.5 seconds, and preferably less than or equal to 0.25 seconds. The frequency interval is between 0Hz and 20KHz The frequency interval is between 200Hz and 20KHz. A "weather" modality class, formed by different road weather conditions, includes a dry state, a damp state and a wet state. A "surface macro-texture" modality class, formed by different road surface conditions, includes a closed state, a medium state and an open state. A "wear" modality class, formed by different tire wear conditions, includes a new state, a mid-worn state and a worn state.A "tread pattern" modality class, formed by different types of tire tread patterns, includes a summer tread pattern and a winter tread pattern. The discriminant analysis of the data provides for the steps in which: ∘ from the learning base, a reduced discriminant space is determined in which areas formed by each modality or combination of modalities are identified, ∘ the vector associated with a measurement is transformed into said reduced discriminant space and, depending on the location of said vector, a probability is associated with the measurement according to each of the modalities or combinations of modalities, ∘ the most probable modality is determined according to each of the modality classes. A modality is associated with the measurement according to the "macro-texture of the road surface", "wear" or "tread pattern" modality, after having previously determined that the measurement was carried out on a dry road.A probability is associated with the measurement according to each of the combinations of modalities containing this modality, and this measurement is assigned the modality of the class with the highest probability. A diagnosis of the condition of the tire is carried out according to the "wear" modality or the "tread" modality by combining the results of measurements carried out at different time intervals. The sound signal generated by the tire is measured using a microphone placed in the front part of a wheel arch located at the rear of the vehicle.

[0035] In a preferred embodiment, the sound signal generated by the tire (T) is measured using a microphone (1) placed in the vehicle. On the figure 1, the microphone is placed in the front part of a wheel arch located at the rear of the vehicle (C). However, other locations can be considered, such as, for example, the rear bumper. The choice of location for the microphone depends, for example, on the type of data to be estimated, the type of vehicle and the external constraints related to its installation, maintenance, and durability.

[0036] The invention will be better understood by reading the attached figures, which are provided as examples and are not limiting in nature, in which: There figure 1 represents a vehicle equipped with a device allowing the determination of road conditions. figures 2 , 3show different displays of information determined by a vehicle, within the scope of the present invention. The following figures will be useful for describing a method for determining the state of the road as implemented in a particular embodiment of the present invention: The figure 4 represents a non-"normalized" sound power spectrum for measurements made at different speeds. The Figure 5 represents the same power spectrum after "normalization". The figure 6 represents the normalized mean power spectra for different road weather conditions. The figure 7 represents a distribution of measurements in a reduced two-dimensional discriminant space according to the meteorological state of the road.

[0037] Vehicle C rolling on ground G, shown schematically in figure 1, includes front and rear wheel arches in which the wheels fitted with T tires are housed.

[0038] When vehicle C moves, tire T generates a noise whose amplitude and frequency depend on multiple factors. This sound pressure is in fact the superposition of noises of various origins such as the noises generated by the contact of the tread blocks with the ground G, by the movements of air between the tread elements, by the water particles raised by the tire, or by the air flows linked to the speed of the vehicle. Listening to these noises also overlaps with noises linked to the vehicle's environment such as the noise of the engine. All these noises are also dependent on the speed of the vehicle.

[0039] A listening means, such as a microphone 1 is installed in the vehicle. It is specified here that different positions can be envisaged for the microphone, only one being shown on the figure 1 , but not limiting the scope of the invention to this configuration. Thus, the microphone can be positioned on a wall of the rear bumper, for example, but not necessarily oriented so as to detect a sound signal coming from the rear of the vehicle.

[0040] Another option is to position the microphone on a wall of the front bumper of a vehicle. The microphone can also be positioned in a wheel arch to listen to rolling noise as close as possible to where it is generated. Ideally, installing a microphone in each wheel arch can be considered the best way to capture all rolling noise generated by the tires. However, to determine the condition of the road (weather and macro-texture of the road surface), a single microphone is sufficient. In the latter case, it is preferable to isolate it from aerodynamic and engine noise.

[0041] Of course, the usual precautions are taken to protect the microphone from external aggressions such as splashes of water, mud or gravel.

[0042] The vehicle also includes a computer 2, connected to the microphone, and configured to perform the operations making it possible to format and analyze, as will be described in detail later, the raw information coming from the microphone, and to estimate the state of the road or the tire based on a measurement of the sound power detected by the microphone.

[0043] As previously mentioned, it is specified here that by "state of the road" is meant one or more pieces of information from among: a weather state of the road, the macro-texture of the surface, or a specific event affecting the road. This information, determined in the vehicle by the computer 2, is transmitted, for example by GSM means installed in or connected to the computer, to an external database. It is accompanied by the GPS position of the vehicle at the time of capture of the data used to determine the information. It is also, in an exemplary embodiment, accompanied by time stamp information. It should be noted that this transmission can be carried out in real time, in order to allow an update of the display in real time, or with a small time lag.

[0044] Once this information has been transmitted, it is then possible, given the presence of GPS coordinates, to display the information on a map corresponding to the journey taken by the vehicle transmitting the data.

[0045] So, the figure 2 shows a road map on which a line has been superimposed, over a given route, whose color corresponds to the weather conditions of the road at that given location. Three colors were used for this display: a dark color corresponding to a wet condition, an intermediate color corresponding to a humid condition, and a light color corresponding to a dry condition.

[0046] According to the invention, the macro-texture of the coating is also displayed on a map. Such a map according to the present invention is shown in figure 3, on which a line in different shades is used to represent different states of the coating. In this figure, the dark shade corresponds to a closed macro-texture, the intermediate shade corresponds to a medium macro-texture, and the light shade corresponds to an open macro-texture.

[0047] The data displayed on these maps comes from a single vehicle that has completed the entire journey. However, it is possible, in an exemplary embodiment of the invention, to display information from different vehicles on the same map. This makes it possible, on the one hand, to cover a larger geographical area, and on the other hand, to have data updated more regularly, and therefore more relevant.

[0048] Such maps can be made available to different actors: Drivers, to know the state of the road before taking the wheel, Motorway management companies, or road maintenance services, to know the state of deterioration of the surfaces, or the need for salting or sanding actions during winter driving conditions, Public bodies to know, from this information, the “noisy” roads, and thus be able to implement means of combating noise pollution...

[0049] We will now return in detail, using the following figures, to a method for determining this different information on the state of the road from the sound readings taken by the microphone installed on the vehicle. It should be noted here that the invention is not limited exclusively to this method of determination.

[0050] There figure 4is a spectral representation of the sound power recorded by the microphone during a time frame. Here, a time frame is understood to be the time interval, generally short, during which a recording is made on the basis of which the data used as the basis for a measurement are established. This time frame is less than or equal to 0.5 seconds or ideally less than or equal to 0.25 seconds.

[0051] This spectral representation represents the received sound power (in dB) as a function of frequency, over a given frequency interval, typically here, the audible frequency interval, between 0 Hz and 20 KHz.

[0052] More specifically the spectral representation of the figure 4is obtained by decomposing the frequency interval into frequency bands of predetermined widths, and assigning to each frequency band a characteristic value equal to the average power measured in this frequency band. A division of the frequency interval into third-octave bands seems to be the most appropriate. Thus, each point of each of the curves of the figure 4 represents an average sound power for a given frequency band and measured during a time frame in driving conditions in which, all other things being equal, only the speed is varied (typically from 30kmh to 110kmh).

[0053] We then observe that the curves representing the spectral powers are shifted relative to each other, and that the total dissipated sound power increases as a function of speed. However, the general shape of the curves remains similar.

[0054] This observation is reproduced when one or more modalities of the other classes are changed and the curves obtained by varying only the speed parameter are compared.

[0055] The total sound power over the entire frequency range is then determined, which can be considered the area between the curve and the x-axis, and for each frequency band, the average power observed in that frequency band during a given time frame is divided by the total power recorded during that time frame over the entire frequency range. This amounts to "normalizing" the measurement, so to speak.

[0056] We then observe at the Figure 5 that the curves obtained previously overlap significantly and have very similar profiles, particularly in the highest frequency bands and those most representative of the sound phenomena described previously.

[0057] This "normalization" makes it possible to neutralize the effect linked to speed, without significantly modifying the analytical capacity that can be produced from a sound recording during a given time frame.

[0058] This advantage can prove decisive when one does not wish to connect the calculator 2 to means of evaluating the vehicle speed, and one wishes to obtain information on the state of the road or the vehicle independently.

[0059] For the sake of simplification and speed in carrying out the calculations, we can consider that the total power is equal to the sum of the average powers in each of the frequency bands of the frequency interval considered.

[0060] Each of the points on the curve of the Figure 5is a value representative of the average sound power in a given frequency band. All of these points can then constitute a vector in a vector space comprising as many dimensions as there are frequency bands. In the example used to support this description, a vector comprising 21 dimensions is obtained by considering a frequency interval segmented by third of an octave and included in the frequency interval between 200Hz and 20KHz. It will be observed in passing that the sum of the values ​​forming the coordinates of a vector is equal to 1.

[0061] The choice of the frequency interval can also be adapted depending on whether one wishes to completely eliminate the noise generated by the motor whose maximum amplitude is between 50Hz and 60Hz, in which case one will consider a frequency interval between, for example, 200Hz and 20KHz, or whether one wishes to retain the portion of relevant information contained in the frequency range below 200Hz, in which case one will take into account the spectrum over the entire interval between 0Hz and 20KHz.

[0062] Recording of sound power during a time frame can be done from high-frequency sampling (around 40 kHz) of the sound signal.

[0063] The implementation of the invention comprises a preliminary learning phase, during which a large number of measurements are carried out by varying in a known manner the methods described above, and describing the meteorological condition, the condition of the road, the state of wear or the type of tread pattern of the tire. To each of these measurements, a vector obtained under the conditions described above is assigned. In this way, a learning base specific to a vehicle is constituted.

[0064] The methods of statistical analysis and processing of data are known per se and are not the subject of this invention. The linear discriminant analysis method that was used made it possible to obtain reliable and robust results.

[0065] A first step in this method is to determine the principal factorial axes that allow the number of dimensions to be reduced to just the number necessary to describe the vectors assigned to each of the measurements along orthogonal axes. The transition from the vector space whose number of dimensions is equal to the number of frequency bands, typically equal to 21 dimensions, into the reduced discriminant space is done using a linear transformation.

[0066] A second step then consists, using the discriminant analysis itself, in searching, in this reduced discriminant space, the zones in which the measurements obtained during the learning phase are located according to a given single modality or according to a combination of modalities.

[0067] Here, a combination of modalities is understood to mean a state representative of a given measurement carried out according to a modality chosen in each of the classes. For example, a measurement carried out in the "wet" state, on a "closed" road with a "summer" and "worn" tire represents the combination of modality "wet-closed-summer-worn". The number of combined modalities is therefore equal to the product of the number of modalities of each of the classes.

[0068] Then, in this reduced discriminant space, we calculate the center of gravity of the zone in which the points representing a modality or a combination of modalities are located, as well as a confidence interval representative of the dispersion of the points of the same zone in relation to this center of gravity.

[0069] In the example used to support this description, the reduction in the number of dimensions between the initial discriminant space and the reduced discriminant space makes it possible to go from 21 dimensions to approximately 15 dimensions. This small reduction makes it possible to observe that it is the consideration of the total shape of the spectrum which is characteristic of the expression of the different modalities. And to suggest that the consideration of the powers of a reduced number of frequency bands does not make it possible to highlight a particular modality concerning the road or the tire, with the exception of the modalities linked to the meteorological state of the road.

[0070] There figure 6 , represents the spectral distribution of "normalized" sound power, by 1 / 3 octave frequency bands for three road weather conditions, all other classes being equal.

[0071] There figure 7allows you to visualize in a two-dimensional space the distribution of measurements according to one of the "dry", "humid", "wet" modalities of the "weather" class of the road.

[0072] A first observation shows that the measurements taken on dry ground do not overlap with the measurements taken on damp or wet ground. A second observation allows us to conclude that it is possible to determine the meteorological state of the road independently of the modalities of the other classes with good robustness.

[0073] The ellipses surrounding each of the point clouds are placed at one, two and three standard deviations, and make it possible to evaluate the dispersion of the measurements around the center of gravity, and above all to assess the rate of overlap of one zone compared to another which is representative of the risk of wrongly assigning to another modality a measurement carried out according to a different given modality.

[0074] From these data, it is also possible to determine the probability of a new measurement belonging to one of the three modalities of the road "weather" class by evaluating the distance from this point to the center of gravity of each of these modalities.

[0075] Table 1 gives the probabilities of classifying the road weather condition according to one of the three modalities "dry", "humid", "wet". Table 1 Pj / i J = Sec J=Wet J=Wet i=Sec 1 0 0 i=Humid 0 0,91 0,09 i=Wet 0 0,03 0,97

[0076] As can be seen, we obtain probabilities high enough to conclude that the vehicle is driving on a "dry", "wet" or "dry" road. And only the recognition of the "wet" modality can be wrongly attributed to the "wet" modality in 9% of cases.

[0077] Similarly, it would be possible to determine the macro-texture of the road surface, with less robustness than the weather condition of the road, without the need to know the condition of the tire beforehand. However, it is preferable to carry out this analysis when the road is dry. This observation suggests that certain acoustic phenomena related to macro-texture and ground reflection are independent of the nature of the tire.

[0078] On the other hand, by carrying out similar analyses, we note that the zones housing the vectors relating to the modalities linked to the state of the tire (wear or sculpture) are relatively dispersed and strongly interpenetrate (strong dispersion around the center of gravity, and small distance from the centers of gravity) which does not allow us to conclude on a precise modality without a high risk of wrong determination, in particular when the state of the road is "humid" or "wet".

[0079] Also, to ensure good robustness, the method provides for an initial analysis of the weather conditions of the road and, when it is observed that the vehicle is driving on "dry" ground, to carry out a second analysis to discern the conditions relating to the road surface, the state of wear of the tire and the type of tire tread.

[0080] For greater robustness, it then seems preferable to carry out the discriminant analysis based on the combined modalities of the three classes. We locate in the reduced discriminant space the point clouds representative of the vectors and measurements carried out according to a given combination of modalities chosen in each of the three modality classes "macro-texture of the coating", "wear", "sculpture".

[0081] The modalities related to the tire tread pattern are noted "A" for a "winter" tire and "P" for a "summer" tire, the modalities of the state of wear are noted "N" for a "new" tire, "M" for a "half-worn" tire and "U" for a "worn" tire, and finally, the modalities of the macro-texture of the coating are noted "f" for the "closed" modality, "m" for the "medium" modality and "o" for the "open" modality. The 18 combined modalities are then noted respectively: ANf, ANm, ANo, AMf, AMm, AMo, AUf, AUm, AUo, PNf, PNm, PNo, PMf, PMm, PMo, PUf, PUm, PUo.

[0082] Table 2 gives the probabilities obtained from the measurement results contained in the learning base, for each of the 18 combinations of modalities. The dispersion of the measurements, observed for the modalities alone, is then much lower for the combined modalities and allows classification to be carried out more efficiently. Table 2 ANf ANm ANo AMf AMm AMo AUf AUm AUo PNf PNm PNo PMf PMm PMo PUf PUm PUo ANf 0,9 0,02 0,03 0 ANm 0,99 0,01 ANo 0,96 0 AMf 0,95 0 0 AMm 0,01 0,95 0,01 0,01 0,03 AMo 0,03 0,96 AUf 1 AUm 0,01 0,97 0,01 0,01 AUo 0,91 0,1 PNf 0 1 0 PNm 0,01 0,97 0,02 PNo 1 PMf 1 0 PMm 0,01 0,02 0,01 0,96 PMo 1 PUf 0 1 PUm 0,06 0,91 PUo 0,03 0,1 0,03 0,9

[0083] The overall probability of detecting one of the modality combinations is of the order of 0.96.

[0084] The next step is to recognize, for a given new measurement, the modality of each of the classes "macro-texture of the coating", "wear" and "sculpture" in which the measurement was carried out.

[0085] Table 3 allows us to determine the probabilities of detecting the modality of one of the three classes based on the combinations of modalities.

[0086] This Table 3 indicates that, if a measurement is assigned to the class "AUf" (Winter, Worn, closed coating), we can have good confidence in the determination of the tread pattern (1), the state of wear (U) and the macro-texture of the coating (1). A relatively lower confidence is obtained in the class "AUo" (Winter, Worn, open) for which the prediction on the type of tire tread pattern is less good (0.91). Table 3 Class detection probability Class found Sculpture Wear Coating ANf 1 0,95 0,98 ANm 1 0,99 1 ANo 0,96 1 1 AMf 0,98 0,95 1 AMm 0,97 0,96 1 AMo 0,99 0,96 0,99 AUf 1 1 1 AUm 0,98 0,98 1 AUo 0,91 1 1 PNf 0,98 0,95 1 PNm 0,99 0,98 1 PNo 1 1 1 PMf 1 0,97 1 PMm 0,97 0,98 1 PMo 1 1 1 PUf 1 0,97 1 PUm 0,94 0,97 1 PUo 0,87 0,97 1

[0087] From the learning base, we locate the zones in which the combined modalities are located in the reduced discriminant space, as well as their center of gravity and their dispersion. Typically, in the case of the present description, we locate in the reduced discriminant space, the 18 zones of the 18 combined modalities considered.

[0088] Then, from the location of the vector associated with each new measurement and transformed into the reduced vector space, we determine for each of the modalities of a class, a probability for each of the combinations of modality containing this modality, and we attribute to this measurement the modality of the class having the highest probability.

[0089] Thus, if the weather class found is "dry", the 21 variables of the vector resulting from the measurement allow, based on the discriminant analysis based on the learning base, to determine a probability of belonging to one of the combined modalities, according to the "macro-texture of the coating" class, the "wear" class or the "sculpture" class, that is, typically, in the case used as support for the present description, the probability of belonging to one of the 18 classes of combined modalities: ANf, ANm, ANo, AMf, AMm, AMo, AUf, AUm, AUo, PNf, PNm, PNo, PMf, PMm, PMo, PUf, PUm, PUo. This probability is calculated for example by evaluating a distance from the center of gravity of the combined modality class considered.

[0090] The probability of the measurement belonging to one of the modalities of a particular class, other than the weather class, is then carried out using a second probability calculation called the "sculpture+wear+covering on dry ground" model in the following manner.

[0091] The probability of the macro-texture modality of the "closed" coating is deduced from the relation p(macro-texture= "closed") = p ("closed") = p(ANf) + p(AMf) + p(AUf) + p(PNf) + p(PMf) + p(PUf)

[0092] Similarly, we deduce:

[0093] We then look for the one of the three probabilities which is maximum and which gives the modality of the macro-texture of the detected coating and the associated probability: p(macro-texture) = max [p("closed"), p("medium"), p("open")].

[0094] Similarly, p(sculpture = max [p("Winter"), p("Summer")] with:

[0095] Finally, the wear is given by p(wear = max [p("new"), p("half-worn"), p("worn")] with: p usure = " neuf " = p " neuf " = p ANf + p ANm + p ANo + p PNf + p PNm + p PNo , p usure = " usé " = p " usé " = p AUf + p AUm + p AUo + p PUf + p PUm + p PUo .

[0096] The probability of assignment to a modality from a given measurement is then compared to a specific threshold to decide on the validity of the result found and its transmission to a display or driver assistance system. For example, all detections whose classification probability is not at least equal to 0.75 are rejected. And if this probability is between 0.75 and 0.95, the result from the measurement must be confirmed by one or more subsequent measurements.

[0097] It will be observed here that, unlike the meteorological state or the macro-texture of the road surface which can change suddenly and require rapid decision-making, the evolution of wear or the type of tire tread pattern are factors that are much more stable over time, typically on time scales corresponding to distances traveled of 100 kilometers or even 1000 kilometers. However, since the detection of these tire parameters depends on the state of the road, we arrive at the paradox that they must be able to be detected almost as quickly as the state of the road.

[0098] The probability of an incorrect determination on these two criteria can be greatly reduced by combining observations obtained using several consecutive measurements, before deciding on the actual state of wear or the type of tire tread fitted to the vehicle.

[0099] The embodiments of the invention serving as a basis for the present description are not limiting, and may be the subject of implementation variants, in particular in the choice of methods for digital analysis of the data, provided that they make it possible to obtain the technical effects as described and remain within the scope of the present invention defined by the claims.

Claims

1. Method for mapping data relating to the conditions on a road, the method comprising the following steps: - A step for recording, in a database, information relating to the weather conditions on a road and information relating the macro-texture of the road surface, determined by a vehicle travelling on the said road, the macro-texture of the road surface being characterized by the mean texture depht, - A step for recording the GPS position of the vehicle corresponding to the recordings made, - A step for displaying, on a map showing the route followed by the vehicle, the weather conditions and the macro-texture of the road surface as a function of the GPS position, wherein the the macro-texture of the road surface is represented by superposing, over a given route, a line whose colours correspond to various states of the road surface.

2. Mapping method according to Claim 1, in which the weather conditions are included within the group comprising: a dry condition, a damp condition, a wet condition, a wintery condition, such as a snow-covered or icy condition.

3. Mapping method according to either of Claims 1 and 2, in which, during the recording step, information is also recorded relating to the macro-texture of the road surface, and in which the display step also comprises the representation of this macro-texture.

4. Mapping method according to one of the preceding claims, in which the macro-texture of the road surface is included within the group comprising a closed state, a medium state and an open state.

5. Mapping method according to one of the preceding claims in which, during the recording steps, information and GPS positions coming from several different vehicles are recorded, the method then comprising a step for organizing the data in order for it to be displayed in the form of a single map for all of the vehicles.

6. Mapping method according to one of the preceding claims comprising the step for recording, aside from the GPS position of the vehicle, the date and the time of the recording.

7. Mapping method according to Claim 6 comprising the step for updating the display if the recorded data relates to a segment of road for which data has already been displayed, as long as the recorded data has a date and a time that is later than the data having been previously displayed.