Method and device for processing data from vehicles connected to a wireless communication network according to the seasons

Abstract

The present invention relates to a method and device for processing data from vehicles connected to a wireless communication network. To this end, a group (11) of connected vehicles is selected from a set of connected vehicles, and driving data is collected. Seasonal information representative of the season in which the driving data was obtained is associated with the driving data. A sample (111 to 114) is selected, for each season, from the group (11) of connected vehicles based on a score calculated for connected vehicles that have completed at least one drive during that season. Figure 1 (for the abstract)

Description

Title of the invention: Method and device for processing data from vehicles connected to a wireless communication network according to the seasons technical field

[0001] The invention relates to methods and devices for processing vehicle data, particularly from automobiles, connected to a wireless communication network. The invention relates to methods and devices for communicating vehicle data within a wireless communication network. In particular, the invention relates to a method and device for calculating a mission profile from data obtained from samples of connected vehicles. Technological background

[0002] Modern vehicles are made up of a large number of components or parts that are susceptible to failure. To reduce the number of failures and to properly size these components and parts, it is important for vehicle manufacturers and their subcontractors to know precisely the stresses and strains experienced by these components and parts during vehicle use.

[0003] To this end, it is known to obtain data relating to vehicle use according to predetermined mission profiles from the manufacturer's customers, for example by sending these customers one or more questionnaires relating to their use of the vehicles. It is also known to obtain such data by conducting test drives under conditions determined according to the mission profiles, in particular on race tracks.

[0004] Data collection using known methods presents several problems. The cost associated with collecting this data is high, the volume of data obtained is limited, and the representativeness of vehicles and driving conditions is also limited due to the generally small number of vehicles considered. Furthermore, since data collection campaigns are complex and costly to implement, changes in customer vehicle usage are generally unknown and therefore not taken into account when developing components or parts.

[0005] Gaining a better understanding of vehicle use in real-world conditions is an important issue for automotive manufacturers, for example to improve mission profile calculations to better size components according to their actual use by customers or to learn from predictive models of breakdowns based on learning data reflecting actual vehicle usage. Summary of the present invention

[0006] One object of the present invention is to solve at least one of the problems of the technological background described above.

[0007] Another object of the present invention is, for example, to improve the collection of vehicle usage data.

[0008] According to a first aspect, the present invention relates to a method for processing data from vehicles connected to a wireless communication network, said method comprising the following steps: - reception, from each connected vehicle of a set of vehicles connected to the wireless communication network via a wireless connection, of driving data representative of the use of each connected vehicle for a set of driving trips carried out over a determined time period, the driving data including, for each driving trip in the set of driving trips, seasonal information representative of a season of a plurality of seasons in which each driving trip is carried out; - determination, for each connected vehicle in the set of connected vehicles and for each drive, of a set of usage parameters from the drive data; - selection of a group of connected vehicles from the set of connected vehicles based on the set of usage parameters; - for each season out of the plurality of seasons: • selection of journeys made by each connected vehicle in the group of connected vehicles during each season based on seasonal information to obtain a set of selected journeys; • for each usage parameter and for each connected vehicle, calculation of a number of distinct values ​​for each usage parameter from a set of rounded values ​​taken by each usage parameter in the set of selected driving conditions; • determination of a score for each connected vehicle based on the number of distinct values ​​determined for the set of usage parameters; • selection of a sample of connected vehicles from the group of connected vehicles based on scores.

[0009] Selecting a sample (i.e., a subgroup) from a group of connected vehicles for each season of a plurality of seasons (e.g., for each season of the year) based on scores obtained from vehicle usage parameters in the group allows us to obtain the most representative sample. representative of the group of connected vehicles, and this for each season. The collection of usage data from the connected vehicles forming this sample makes it possible to obtain data representative of the use of the group of vehicles in real conditions, for each season, the data being obtained directly from the vehicles in the sample via the wireless connection linking these connected vehicles to a wireless network, for example a terrestrial cellular network.

[0010] The collection of data representative of the actual use of vehicles is facilitated in that this data can be obtained at any time, via the wireless connection, from a small set of vehicles connected to the wireless network and representative of a larger group of connected vehicles.

[0011] The data thus obtained are used for example to establish various mission profile calculations necessary for the dimensioning of components or organs on board vehicles of the same type as the vehicles forming the group of connected vehicles for example, taking into account the criterion of the season and the constraints associated with each season.

[0012] According to one variant, the score for each connected vehicle corresponds to a weighted sum of the numbers of distinct values ​​determined for the set of usage parameters, a determined weighting coefficient being applied to each usage parameter of the set of usage parameters.

[0013] According to another variant, the selected sample comprises an integer number N of connected vehicles having the highest score values ​​in the group of connected vehicles.

[0014] According to yet another variant, the method further includes an identification of the driving data associated with each selected sample of connected vehicles.

[0015] According to a further variant, the selection of the connected vehicle group within the connected vehicle set comprises the following steps: - for each usage parameter of the set of usage parameters, aggregation of the values ​​taken by each usage parameter in the set of runs to obtain a set of aggregated usage parameters; - comparisons of the set of aggregated usage parameters to a set of threshold values, the group of connected vehicles being selected from the set of connected vehicles based on comparison results.

[0016] According to yet another variant, the set of threshold values ​​is a function of a type of mission profile to be carried out according to the driving data associated with each selected sample of connected vehicles.

[0017] According to another variant, the set of usage parameters includes at least one parameter from among: - a first parameter representing a distance traveled during a drive; - a second parameter representing the duration of the ride; - a third parameter representing the state of charge of a traction battery before and / or after driving; - a fourth parameter representing the number of traction battery recharges before driving; and - a fifth parameter representing a maximum speed during driving.

[0018] According to a second aspect, the present invention relates to a device for processing data from vehicles connected to a wireless communication network, the device comprising a memory associated with a processor configured for implementing the steps of the process according to the first aspect of the present invention.

[0019] According to a third aspect, the present invention relates to a system comprising a device as described above according to the second aspect of the present invention and a group of vehicles connected to the device via a wireless connection.

[0020] According to a fourth aspect, the present invention relates to a computer program which includes instructions adapted for carrying out the steps of the process according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0021] Such a computer program may use any programming language, and be in the form of source code, object code, or an intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0022] According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the process according to the first aspect of the present invention.

[0023] On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means or a hard disk drive.

[0024] On the other hand, this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be transmitted via an electrical or optical cable, by conventional or radio frequency, by self-directing laser beam, or by other means. The computer program according to the present invention can, in particular, be downloaded from an Internet-type network.

[0025] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the process in question. Brief description of the figures

[0026] Other features and advantages of the present invention will become apparent from the description of the specific and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 6, in which:

[0027] [Fig. 1] schematically illustrates a communication environment for connected vehicles, according to a particular embodiment of the present invention;

[0028] [Fig.2] schematically illustrates a data processing procedure for data obtained of at least some of the connected vehicles of [Fig.1], according to a particular and non-limiting embodiment of the present invention;

[0029] [Fig.3] schematically illustrates a data processing implemented in the process of [Fig.2], according to a particular and non-limiting example of the present invention;

[0030] [Fig.4] schematically illustrates a selection of connected vehicle samples implemented in the process of [Fig. 2], according to a particular and non-limiting embodiment of the present invention

[0031] [Fig.5] illustrates a device configured for processing at least part of the connected vehicles of the [Fig.1], according to a particular and non-limiting embodiment of the present invention.

[0032] [Fig.6] illustrates a flowchart of the different stages of a treatment process of at least some of the connected vehicles of [Fig.1], according to a particular and non-limiting embodiment of the present invention. Description of examples of achievements

[0033] A method and a control device for a vehicle data processing system connected to a wireless communication network will now be described in what follows with joint reference to Figures 1 to 6. The same elements are identified with the same reference signs throughout the following description.

[0034] The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to allow for the identification and distinction of different elements (such as operations, means, etc.) implemented in the embodiments described below. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0035] According to a particular and non-limiting embodiment of the present invention, the processing of vehicle data obtained from a sample of vehicles connected to a wireless communication network is, for example, implemented by a server-type data processing device or computer connected wirelessly to a group of connected vehicles including the sample. In the following, a connected vehicle corresponds to a vehicle configured to communicate data to the data processing device via a wireless communication network to which the data processing device is connected.

[0036] To this end, driving data is received from each connected vehicle in a set of connected vehicles via the wireless connection. Such driving data is representative of the use of each connected vehicle for a set of drives carried out over a defined time period (for example, over a period covering several seasons (spring, summer, autumn, and / or winter)). The driving data includes information on each drive carried out, for example, data acquired by on-board sensors of each connected vehicle relating to the route traveled, as well as seasonal information representative of the season in which each drive is carried out.

[0037] A set of usage parameters is determined from the driving data for each connected vehicle and for each trip, for example, the distance traveled, the driving time, the average speed and / or the maximum speed during the trip. A group of connected vehicles is then selected from the set of connected vehicles based on the usage parameters to select the most representative connected vehicles in the set, for example, according to the needs determined for a particular study or a particular mission profile.

[0038] A sample comprising connected vehicles from the connected vehicle group is selected from the group for each season of a plurality of seasons, for example, for each season of the year. A sample of connected vehicles corresponds to a subgroup of the connected vehicle group. Thus, for each season of the year, trips taken are selected for each connected vehicle in the connected vehicle group based on seasonal information to obtain a set of selected trips for each season. The usage parameters associated with the set of selected trips are used to calculate, for each usage parameter and for each connected vehicle, the number of distinct values ​​taken by that usage parameter after rounding the values ​​taken by that usage parameter for the set of selected trips.A score is then calculated for each connected vehicle based on the number of distinct values ​​obtained for all the usage parameters of the connected vehicle in question. Finally, the sample of connected vehicles is formed. based on the scores obtained for the connected vehicles in the connected vehicle group.

[0039] Such a method makes it possible to select the most representative sample of vehicles from a group of vehicles for each season, for example to then identify and store the driving data obtained for these vehicles, which driving data are representative of the use of one or more of the components of the vehicles in the selected sample.

[0040] This allows for representativeness of the driving data collected for each season. Indeed, vehicle use can vary according to the seasons, for example, with more high-speed driving on highways in summer and less in winter and autumn (for example, due to trips to go on vacation), while in winter and autumn, short trips at reduced speeds may be more frequent, for example, due to more commuting, more city driving, and more challenging weather conditions. The stresses on vehicle components are therefore different from one season to another, which impacts, for example, the lifespan of these components.

[0041] Fig. 1 schematically illustrates a communication environment 1 of a group 11 of vehicles connected to a wireless communication network, according to a particular and non-limiting embodiment of the present invention.

[0042] Each vehicle in group 11 of connected vehicles corresponds, for example, to a vehicle with an internal combustion engine, with electric motor(s), or a hybrid vehicle with an internal combustion engine and one or more electric motors. Each vehicle thus corresponds, for example, to a land vehicle, such as a car, a truck, a bus, or a motorcycle.

[0043] Group 11 of connected vehicles corresponds to a subset of a larger set of connected vehicles, the connected vehicles being selected from the set of connected vehicles according to a set of usage parameters collected for this set of connected vehicles, as described below with regard to Figures 2 and 3 according to particular embodiment examples.

[0044] Each vehicle in group 11 (and in the set of vehicles from which group 11 is selected) is said to be connected in that it carries a communication system configured to communicate with one or more remote devices 101 via a wireless communication network infrastructure. The remote device 101 advantageously corresponds to a device configured to process data, for example, data stored in the memory of the remote device 101 and / or data received from the connected vehicles forming group 11 of connected vehicles. The remote device 101 corresponds, for example, to a server or a computer in the "cloud" 100.

[0045] The communication system of a connected vehicle includes, for example, one or more communication antennas connected to a telematic control unit (TCU), which is itself connected to one or more computers of the connected vehicle's embedded system. The antenna(s), the TCU, and the computer(s) form, for example, a multiplexed architecture for providing various services useful for the proper functioning of the connected vehicle and for assisting the driver and / or passengers of the connected vehicle in controlling the vehicle and / or for diagnosing the operation of one or more components of the connected vehicle.The computer(s) and the TCU communicate and exchange data with each other via one or more computer buses, for example a CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (according to ISO 17458) or Ethernet (according to ISO / IEC 802-3) type communication bus.

[0046] The mobile communication infrastructure enabling wireless data communication between a connected vehicle of group 11 and the remote device 101 includes, for example, one or more communication devices 102 of the type relay antenna (cellular network) or roadside unit, known as UBR. In a communication mode using such a network architecture, the data is, for example, transmitted by the connected vehicle to the remote device 101 from the "cloud" 100 via a relay antenna 102 (the antenna 102 being, for example, connected to the "cloud" 100 via a wired link and the remote device 101 being itself connected to the network infrastructure of the "cloud" 100 via a wired and / or wireless network).

[0047] The wireless communication system enabling data exchange between the connected vehicle and the remote device 101 corresponds, for example, to: - a vehicle-to-infrastructure (V2I) communication system, for example based on the 3GPP LTE-V or IEEE 802.1 lp standards of ITS G5; or - a cellular network communication system, for example an LTE (Long-Term Evolution) network, LTE-Advanced (also called LTE 3G, 4G or 5G); or - a Wifi type communication system according to IEEE 802.11, for example according to IEEE 802.1 In or IEEE 802.1 lac.

[0048] A communication and / or data processing process for vehicles connected to a wireless communication network, including embodiments particulars are described in relation to [Fig.2], is advantageously implemented by a data processing device such as the remote device 101.

[0049] During the execution of the process, a set of samples 111, 112, 113, 114 of connected vehicles are identified within the group 11 of connected vehicles, the set of samples comprising, for example, one sample of connected vehicles per season. Each sample comprises several connected vehicles, each sample forming a subgroup of the group 11 of connected vehicles.

[0050] In a first operation 21 of the process, data representing a need are received, the need corresponding for example to a design project for a new vehicle component or organ, a design project for a new vehicle, a project to improve an existing component or organ, etc. Such a need is for example defined in the form of a mission profile.

[0051] In a second operation 22 of the process, a population of connected vehicles corresponding to the need expressed in the first operation is identified. For example, the expressed need concerns a type of vehicle (e.g., a hybrid vehicle or an electric vehicle) of a particular category circulating in a particular region (e.g., in Europe or the United States of America). Identifying the population of connected vehicles makes it possible, for example, to select the set of connected vehicles for which driving data must be collected and then to identify a sample of connected vehicles within this set of connected vehicles, for each season of a plurality of seasons, for example, for each season of the year, or for part of the seasons (e.g., for summer and for winter).

[0052] The set of selection criteria or characteristics is determined, for example, according to the population of vehicles on which a study or analysis is to be carried out, for example a study relating to the actual use of vehicles in the field.

[0053] The target vehicle population is identified, for example, via one or more characteristics such as: - intrinsic characteristics of the vehicle such as, for example, the vehicle model, the vehicle's powertrain (e.g., electric, internal combustion, or hybrid), the vehicle's engine power, the vehicle's mass, the power-to-weight ratio, the date of first registration, the equipment level, the vehicle type (commercial vehicle, van, car, station wagon, sedan, SUV, etc.), the vehicle's dimensions, the ADAS (Advanced Driver-Assistance System) functions and / or systems fitted to the vehicle, the vehicle's level of autonomy, the serial number of all or part of the vehicle's components, etc.; and / or - extrinsic characteristics of the vehicle such as, for example, the geographical area (region, country, continent) of sale or circulation of the vehicle, the number of times the vehicle has changed ownership, the type of owner (individual, company, vehicle rental agency, shared vehicle), etc.

[0054] Driving data is collected for each connected vehicle in the set of connected vehicles selected based on one or more of the characteristics defined above. This driving data is received from each connected vehicle via a wireless connection. The driving data thus received is stored in a database controlled by the remote device 101. This driving data is collected over a predetermined period covering, for example, several seasons, or even several years, to obtain driving data for each season of the year (spring, summer, autumn, and winter).

[0055] The driving data received from a connected vehicle is thus representative of the use made of that connected vehicle during a series of drives (or journeys) over a specified period. The driving data is acquired by a set of sensors or systems embedded in the connected vehicle and transmitted via the wireless connection to the remote device 101. The driving data is transmitted, for example, as it is acquired. In another example, the driving data is transmitted at the end of each drive (for example, when the connected vehicle is brought to a stop at the end of the journey), the driving data acquired during that drive being stored in the connected vehicle's memory as it is acquired, until transmission.

[0056] Seasonal information is associated with the driving data for each driving trip, this seasonal information indicating the season in which the driving trip was carried out. This seasonal information is, for example, added by the remote device to the received driving data, the seasonal information being determined, for example, based on the date on which the driving data was acquired or received.

[0057] In a third operation 23 of the process, a set of usage parameters is determined from the driving data for each connected vehicle in the set of connected vehicles and for each driving trip.

[0058] The set of usage parameters includes one or more of the following usage parameters, in any possible combination: - a first parameter representing a distance traveled during a drive; - a second parameter representing the duration of the ride; - a third parameter representing the state of charge of a traction battery before and / or after driving; - a fourth parameter representing the number of traction battery recharges before driving; and - a fifth parameter representing a maximum speed during driving.

[0059] The above list is provided for illustrative purposes only and is not exhaustive, the parameters determined from the driving data depending, for example, on the type of connected vehicle from which the driving data is received.

[0060] The group 11 of connected vehicles is then selected from the set of connected vehicles according to the set of usage parameters determined from the driving data.

[0061] Fig. 3 illustrates a particular implementation of the third operation 23 of the process, the third operation 23 comprising a set of operations 311 to 317 according to this particular embodiment.

[0062] In an operation 311, the set of usage parameters is determined from the driving data.

[0063] Taking as an example connected vehicles identified A, B, C, D and E with journeys identified AR1, AR2 for connected vehicle A, BRI, BR2 and BR3 for connected vehicle B, CRI, CR2 and CR3 for connected vehicle C, ER1 for connected vehicle E, with the distance traveled (in m) D as an example of usage parameters, the following table 1 is obtained for example.

[0064] [Tables 1] Vehicle Driving Distance Parameter XA AR1 20 A AR2 15000000 B BRI 6842 B BR2 2.6 B BR3 6842 C CRI 800 C CR2 6057 D DR1 362 D DR2 514 D DR3 514 E ER1 15763

[0065] In operation 312, the usage parameters obtained in operation 311 are aggregated per vehicle, for example by summing the usage parameters for the entire driving sequence performed by each connected vehicle. According to a Alternatively, aggregation corresponds to another operation, for example, an average for a usage parameter corresponding to a speed. The result of the aggregation is illustrated in Table 2 below.

[0066] [Tables2] Vehicle Aggregate Distance Parameter X Aggregate A 15000020 B 13686.6 C 6857 D 1390 E 15763

[0067] Thus, for each vehicle connected A to E, the values ​​taken by each usage parameter in the set of journeys are aggregated to obtain a set of aggregated usage parameters.

[0068] In an operation 313, the set of aggregated usage parameters is compared to a set of threshold values, that is, each aggregated parameter is compared to a determined threshold value from the set of threshold values.

[0069] Threshold values ​​are, for example, defined according to quality rules based on the need expressed in the first operation 21 (for example, distance traveled less than 503). The set of threshold values ​​is, for example, a function of a type of mission profile to be carried out based on the driving data associated with each selected sample of connected vehicles.

[0070] The results of the comparisons provide a first list of connected vehicles, obtained in operation 314, respecting all the quality rules defined (or respecting all the expected results as selection criteria) and a second list of connected vehicles, obtained in operation 315, not respecting all the quality rules defined (or not respecting all the expected results as selection criteria).

[0071] For example, if the selection criterion concerning distance is that the aggregate distance travelled must be less than 503, then vehicle A belongs to the second list and vehicles B to E belong to the first list.

[0072] The connected vehicles in the first list thus form group 11 of connected vehicles, group 11 of connected vehicles being selected from the set of connected vehicles according to the results of the comparisons.

[0073] In operation 316, the data from table 1, including the usage parameters for each connected vehicle in the set of connected vehicles, are filtered to retain only the usage parameters for trips made with connected vehicles from the first list, i.e., the connected vehicles of the selected group 11. Table 3 below, which includes usage parameters only for connected vehicles in group 11, is thus obtained.

[0074] [Tables3] Vehicle Driving Distance Parameter XB BRI 6842 B BR2 2.6 B BR3 6842 C CRI 800 C CR2 6057 D DR1 362 D DR2 514 D DR3 514 E ER1 15763

[0075] In operation 317, the values ​​of the parameters used in Table 3 are rounded according to predetermined rounding rules, for example, rounding to the nearest thousand for the distance traveled D (i.e., to the nearest kilometer), which allows us to obtain the following Table 4. For a parameter corresponding to travel time, the rounding is, for example, to the nearest minute.

[0076] [Tables4] Vehicle Driving Distance Parameter XB BRI 7000 B BR2 0 B BR3 7000 C CRI 1000 C CR2 6000 D DR1 0 D DR2 1000 D DR3 1000 E ER1 16000

[0077] In a fourth operation 24 of the process, a sample of connected vehicles is selected from group 11 for each season of a plurality of seasons, for example, for each season of the year. According to the example illustrated in [Fig. 1], sample 111 corresponds to the sample associated with spring, sample 112 corresponds to the sample associated with summer, sample 113 corresponds to the sample associated with autumn, and sample 114 corresponds to the sample associated with winter. Some connected vehicles in group 11 belong, for example, to several samples, and some connected vehicles in group 11 do not belong to any sample.

[0078] Figure 4 illustrates a particular implementation of the fourth operation 24 of the process, the fourth operation 24 comprising a set of operations 411 to 415 according to this particular embodiment.

[0079] Operations 411 to 415 are implemented for each season of the plurality of seasons to obtain the sample of connected vehicles associated with each season.

[0080] In operation 411, the trips made by each connected vehicle in group 11 of connected vehicles during each season are selected based on the seasonal information associated with each trip to obtain a set of selected trips. The set of selected trips for a season (e.g., summer) is included in Table 5 below.

[0081] [Tables5] Vehicle Driving Distance Parameter XB BR2 0 B BR3 7000 D DR2 1000 E ER1 16000

[0082] According to this example, only routes BR2, BR3, DR2, and ER1 had the associated seasonal information corresponding to summer. The set of selected routes thus includes routes BR2, BR3, DR2, and ER1 of connected vehicles B, D, and E.

[0083] In an operation 412, for each usage parameter and for each connected vehicle having at least one ride in the set of selected rides (i.e. connected vehicles B, D and E according to the example in Table 5), a number of distinct values ​​is calculated for each usage parameter from among the set of rounded values ​​taken by each usage parameter in the set of selected rides.

[0084] The following table 6 is obtained based on the example of the distance traveled.

[0085] [Tableauxô] Vehicle Number of unique values ​​on the distance parameter Number of unique values ​​on the XB parameter 2 D 1 E 1

[0086] Such an operation calculates the number of occurrences of different values ​​taken by the different values ​​of each parameter, per connected vehicle having at least one drive during the season considered. Thus, the same value taken by the same parameter is counted only once.

[0087] In an optional operation 413, a weighting is applied to the result of the calculation of operation 412 with a weighting factor determined for each usage parameter, which weighting factor depends for example on the need expressed in the first operation 21.

[0088] For example, the weighting factor associated with the distance parameter is equal to 10, a weighting factor associated with a maximum speed parameter is equal to 5, a weighting factor associated with a number of recharges parameter is equal to 5, etc.

[0089] In a 414 operation, a score is determined for each connected vehicle based on the distinct value numbers determined for the set of usage parameters.

[0090] According to a particular example, the score for each connected vehicle corresponds to a sum of the numbers of distinct values ​​determined for the set of usage parameters.

[0091] According to another particular example, the score for each connected vehicle corresponds to a weighted sum of the numbers of distinct values ​​determined for the set of usage parameters, a determined weighting coefficient being applied to each usage parameter of the set of usage parameters, as described in operation 413.

[0092] Each connected vehicle having at least one run in the season in question receives a score as shown for example in the following table 7:

[0093] [Tables7] Vehicle score B 25 D 55 E 25

[0094] In operation 415, the sample of connected vehicles is selected from the group of connected vehicles based on the scores obtained in operation 414.

[0095] For example, the selected sample comprises an integer number N of connected vehicles having the highest score values ​​in the group of connected vehicles having at least one drive in the season considered.

[0096] A threshold is associated, for example, with each season to select only connected vehicles with a score above that threshold. N varies, for example, from one season to another.

[0097] Such a process makes it possible to obtain a selection of the connected vehicles in a group 11 of the most representative, depending on the season.

[0098] In a fifth operation 25 of the process, the connected vehicles selected for each season are identified to obtain the overall list of samples representing all seasons. The identifier of each connected vehicle and the associated season are, for example, stored in the memory of the remote device 101.

[0099] The driving data associated with each selected sample of connected vehicles is, for example, identified via the identification of the connected vehicles selected for each season, which makes it possible to use this driving data for specific projects or mission profiles taking into account the specificity of the seasons in the use of the vehicles, for example for the sizing and development of vehicle components, systems or parts, and more particularly for carrying out mission profile calculations necessary for the sizing of components.

[0100] Figure 5 schematically illustrates a device 5 configured for communication and / or data processing of vehicles connected to a wireless communication network, according to a particular and non-limiting embodiment of the present invention. The device 5 corresponds, for example, to a computing device or a data processing device such as the remote device 101, for example a computer or a server.

[0101] According to one embodiment, the device 5 corresponds to a device embedded in the vehicle 10, for example a computer or a telematic control unit, called TCU (from the English "Telematic Control Unit").

[0102] Device 5 is, for example, configured to carry out the operations described opposite Figures 1 to 4 and / or the steps of the process described opposite [Fig. 6]. Examples of such a device 5 include, but are not limited to, a computer, a laptop computer, a server, embedded electronic equipment such as a vehicle's on-board computer, an electronic control unit such as an ECU (Electronic Control Unit), a smartphone, and a tablet. The elements of the device 5, individually or in combination, can be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. The device 5 can be implemented as electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules.

[0103] The device 5 comprises one (or more) processor(s) 50 configured to execute instructions for carrying out the steps of the process and / or for executing instructions from the software embedded in the device 5. The processor 50 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 5 further comprises at least one memory 51, for example, volatile and / or non-volatile memory, and / or includes a memory storage device that may include volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk, or optical disk.

[0104] The computer code of the embedded software(s) including the instructions to be loaded and executed by the processor is for example stored on memory 51.

[0105] According to various particular and non-limiting embodiments, the device 5 is coupled in communication with other similar devices or systems and / or with communication devices, for example a TCU (Telematic Control Unit), for example via a communication bus or through dedicated input / output ports.

[0106] According to a particular and non-limiting embodiment, the device 5 includes a block 52 of interface elements for communicating with external devices. The interface elements of the block 52 include one or more of the following interfaces: - radio frequency RF interface, for example of the Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or of the Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or of the Sigfox type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced; - USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus" in French); - HDMI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - LIN interface (from the English "Local Interconnect Network", or in French "Réseau interconnecté local").

[0107] According to another particular and non-limiting embodiment, the device 5 includes a communication interface 53 which enables communication with other devices (such as other computers in the embedded system) via a communication channel 530. The communication interface 53 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via the communication channel 530. The communication interface 53 corresponds, for example, to a wired Ethernet network (standardized by ISO / IEC 802-3).

[0108] According to a particular and non-limiting embodiment, the device 5 can provide output signals to one or more external devices, such as a display screen 540, touch or not, one or more loudspeakers 550 and / or other peripherals 560 (projection system) via output interfaces 54, 55 and 56 respectively. According to a variant, one or more of the external devices is integrated into the device 5.

[0109] Figure 6 illustrates a flowchart of the various stages of a method for processing data from vehicles connected to a wireless communication network, according to a particular and non-limiting embodiment of the present invention. The method is, for example, implemented by a computing device or a data processing device such as the remote device 101, for example a computer or a server, or by the device 5 of Figure 5.

[0110] In a first step 61, driving data representative of the use of each connected vehicle for a set of driving trips carried out over a determined time period are received from each connected vehicle of a set of vehicles connected to the wireless communication network via a wireless connection, the driving data comprising, for each driving trip in the set of driving trips, seasonal information representative of a season of a plurality of seasons in which each driving trip is carried out.

[0111] In a second step 62, a set of usage parameters is determined from the driving data for each connected vehicle in the set of connected vehicles and for each drive.

[0112] In a third step 63, a group of connected vehicles is selected from the set of connected vehicles according to the set of usage parameters.

[0113] In a fourth step 64, journeys made by each connected vehicle in the group of connected vehicles during each season are selected according to seasonal information to obtain a set of selected journeys.

[0114] In a fifth step 65, a number of distinct values ​​is calculated for each usage parameter from a set of rounded values ​​taken by each usage parameter in the set of selected rides, for each usage parameter and for each connected vehicle.

[0115] In a sixth step 66, a score for each connected vehicle is determined based on the numbers of distinct values ​​determined for the set of usage parameters.

[0116] In a seventh step 67, a sample of connected vehicles from the group of connected vehicles is selected based on scores.

[0117] Operations 64 to 67 are repeated for each season of a plurality of seasons.

[0118] According to one variant, the variants and examples of the operations described in relation to one of Figures 1 to 4 apply to the steps of the process in [Fig.6].

Claims

Demands

1. A method for processing data from vehicles connected to a wireless communication network, said method comprising the following steps: - receiving (61), from each connected vehicle in a set of vehicles connected to said wireless communication network via a wireless connection, driving data representative of the use of each connected vehicle for a set of drives carried out over a determined time period, said driving data comprising, for each drive in said set of drives, seasonal information representative of a season in a plurality of seasons in which said each drive is carried out; - determining (62), for each connected vehicle in said set of connected vehicles and for said each drive, a set of usage parameters from said driving data;- selection (63) of a group (11) of connected vehicles from said set of connected vehicles according to said set of usage parameters; - for each season of said plurality of seasons: • selection (64) of the journeys made by each connected vehicle from said group of connected vehicles during said each season according to seasonal information to obtain a set of selected journeys; • for each usage parameter and for said each connected vehicle, calculation (65) of a number of distinct values ​​for said each usage parameter from a set of rounded values ​​taken by said each usage parameter in the set of selected journeys; • determination (66) of a score for said each connected vehicle according to the number of distinct values ​​determined for said set of usage parameters;• selection (67) of a sample (111 to 114) of connected vehicles from said group (11) of connected vehicles according to said scores.;

2. A method according to claim 1, wherein said score for said each connected vehicle corresponds to a weighted sum of number of distinct values ​​determined for said set of usage parameters, a determined weighting coefficient being applied to each usage parameter of said set of usage parameters.

3. A method according to claim 1 or 2, wherein the selected sample (111 to 114) comprises an integer number N of connected vehicles having the highest score values ​​in said group of connected vehicles.

4. A method according to any one of claims 1 to 3, further comprising an identification of the driving data associated with each selected sample (111 to 114) of the connected vehicles.

5. A method according to any one of claims 1 to 4, wherein said selection (63) of the group (11) of connected vehicles in said set of connected vehicles comprises the following steps: - for each usage parameter of said set of usage parameters, aggregation of the values ​​taken by said each usage parameter in the set of driving to obtain a set of aggregated usage parameters; - comparisons of said set of aggregated usage parameters to a set of threshold values, said group (11) of connected vehicles being selected in said set of connected vehicles according to the results of the comparisons.

6. A method according to claim 5, wherein the set of threshold values ​​is a function of a type of mission profile to be carried out based on the driving data associated with each selected sample of connected vehicles.

7. A method according to any one of claims 1 to 6, wherein said set of operating parameters comprises at least one parameter among: - a first parameter representing a distance traveled during a drive; - a second parameter representing a duration of the drive; - a third parameter representing a state of charge of a traction battery before and / or after the drive; - a fourth parameter representing a number of recharges of the traction battery before the drive; - a fifth parameter representing a maximum speed during driving.

8. A computer program comprising instructions for carrying out the method according to any one of the preceding claims, when such instructions are executed by a processor.

9. Device (5) for processing data from vehicles connected to a wireless communication network, said device (5) comprising a memory (51) associated with at least one processor (30) configured for carrying out the steps of the method according to any one of claims 1 to 7.

10. System comprising the device (5) according to claim 9 and a group (11) of vehicles connected to said device (5) via a wireless connection.

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