Method and device for providing information about an operating anomaly affecting a vehicle

Abstract

The invention relates to a method and device for providing information about an operating anomaly affecting a vehicle, the method comprising the following steps: - receiving (210) a generated text; - determining (220) a list of labels based on the received text and on a first predetermined model; - receiving (230) first data; - receiving (240) second data; - determining (250) a first list of words based on the list of labels, on the first and second data and on a predetermined knowledge graph; - determining (260) a second list of words based on the first list of words, on the second data and on a second predetermined model; - determining (270) a list of sentences based on the second list of words and on a third predetermined model; - transmitting (280) the list of sentences to the audio system in order to provide information about the operating anomaly affecting the vehicle through voice synthesis.

Description

DESCRIPTION TITLE: Method and device for providing information on a vehicle operating fault Technical field of the invention

[0001] The present invention claims priority from French application 2310059 filed on 09 / 22 / 2023, the content of which (text, drawings and claims) is incorporated herein by reference. The invention is in the field of autonomous vehicle driving assistance systems. In particular, the invention relates to a method and a device for providing information on an operating anomaly of a vehicle having an audio voice recognition and synthesis system, to inform an occupant of said vehicle, said occupant issuing a verbal request. State of the art

[0002] A "vehicle" means any type of vehicle such as a motor vehicle, a moped, a motorcycle, a storage robot in a warehouse, etc. "Autonomous driving" of a "vehicle" means any process capable of assisting the driving of the vehicle; the "vehicle" is then also called an "autonomous vehicle". The process may therefore consist of partially or totally steering the vehicle or providing any type of assistance to a natural person driving the vehicle. The process thus covers all autonomous driving, from level 0 to level 5 in the OICA scale, for International Organization of Motor Vehicle Manufacturers.

[0003] Vehicles sold today include a large number of features and are becoming increasingly complex to meet regulatory requirements such as emissions constraints. Unexpected noises or operating conditions may lead the driver to believe that there is a fault with their vehicle. To verify the fault, the driver must make an appointment with the after-sales service and have their vehicle diagnosed. Some of these faults can be resolved or explained by remote communication (telephone, message sent via the internet, etc.) with the after-sales service.

[0004] After-sales service includes many diagnostic assistance or diagnostic documents, these documents are called Technical Documents. These documents may include repair methods, methods of using tools, methods of resolving identified anomalies (TSB from the English "Technical Service Bulletin"), diagnostic methods, diagrams, fault trees, .... From this information, the after-sales service is able to answer their customers' questions. A "diagnostic guide" is understood to be a set of diagnostic assistance or diagnostic documents.

[0005] However, some anomalies appear only furtively or intermittently in well-defined life situations. During an appointment with the After-sales service, the conditions under which the anomaly occurred are no longer reproduced. It is often very difficult to reproduce the anomaly and therefore to understand what is happening and to explain the cause to the driver of the vehicle and to be able to repair, if necessary, to correct the anomaly. Also, sometimes, what a driver thinks is an anomaly is normal operation.

[0006] Artificial intelligence, or AI, refers to a machine or computer device designed from large amounts of data and capable of providing decisions or queries based on an expressed need. It is known that the design of these devices is based on learning a model from expressed needs, also called inputs, and expected decisions, also called outputs. The inputs and outputs of artificial intelligence are digital and can be interpreted by computers. We are talking about a machine learning model, and the models are pre-trained (learning phase).

[0007] Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. For example, NLP relies on machine learning techniques and models, such as neural networks, language models, classification and clustering algorithms, statistical methods, etc. These techniques allow NLP systems to process and understand the structures, meanings, and nuances of natural language.

[0008] For example, version 3.5 of the artificial intelligence known as "ChatGPT®" is an NLP whose training data size contains 175 billion parameters to be trained. The training dataset contains 300 billion words, or approximately 570 gigabytes of text data.

[0009] Currently, NLP models are much too large (memory) and consume computing resources (processor CPU time, processor power, etc.). These models cannot be embedded in a computer (or ECU from the English "Electronic Control Unit") in a vehicle while delivering services in near real time, the computer being compatible (temperature variation, humidity, etc.) for use in a "general public" vehicle (sold in several tens of thousands of units).

[0010] It is known to use a "smartphone", a multifunctional cell phone or mobile phone, connected to the "cloud" (remote computer server, or server). This smartphone may include an application connected, by an electromagnetic link (4G, 5G, ...) or similar, to this cloud to an AI of the ChatGPT® type. However, on the one hand, this requires a good connection quality and generally a paid subscription, and, on the other hand, these AIs are not suitable for providing vehicle operating information from a voice request, in particular providing contextualized information based on a vehicle make and / or model. In addition, this implies that voice requests transit on the cloud and are recorded by the AI. A user may wish not to have his conversations, made in the car, recorded by a third party, outside the car.

[0011] Audio speech recognition systems are also known, which allow human speech to be converted into written text. These systems work using signal processing algorithms that analyze the audio and identify the different components of language, such as phonemes, words, intonations, etc.

[0012] Some of these voice recognition systems are simpler and can be implemented in a vehicle. There is no conversion of human speech into written text. From the identification of intonations or a predetermined sound sequence, action requests such as "turn off" or "turn on" a car radio are recognized. These are simpler systems but they are limited to a very limited number of language component identifications. The expressed need is limited to a very limited number (about ten) of functions. These systems are very sensitive to ambient noise, intonation, accents, etc., and therefore do not allow them to provide a wide variety of operating information. Summary of the invention

[0013] An object of the present invention is to remedy the aforementioned problem, in particular to provide, to an occupant of a vehicle, information on an operating anomaly of a vehicle without having to contact the after-sales service and at a time when the anomaly is present and detectable on the vehicle.

[0014] To this end, a first aspect of the invention relates to a method for providing information on an operating anomaly of a vehicle having an audio voice recognition and synthesis system, to inform an occupant of said vehicle, said occupant issuing a verbal request, said method being implemented by a processor (103) and comprising the steps of: • Receiving a text generated from said audio system processing said verbal request; • Determination of a list of labels from said received text and a first predetermined model, o said first model being a machine learning model from the natural language processing domain, o said first model being trained from ■ a predetermined set of texts representative of a verbal request for the provision of information on an operating anomaly of at least one vehicle, and ■ of a predetermined set of said labels, where said labels are a group of characters, at least one word, used to identify and describe sets of words from said predetermined set of texts representing a verbal request for the provision of information on an operating anomaly of at least one vehicle; • Reception of first data, called vehicle data, said first data relating to said vehicle, said data comprising at least one identifier making it possible to determine a make and / or a model of said vehicle, and comprising at least one item of information on a state of said vehicle such as a speed, a temperature, etc.; • Reception of second data, called diagnostic data, said second data relating to diagnostic data of said vehicle such as a fault code, a list of fault codes, etc.; • Determination of a first list of words from said list of labels, said first and second received data and a predetermined knowledge graph, where said graph comprises ■ nodes, said nodes being representative of entities, the entities being predetermined from • of said labels, and • a set of at least one word, said at least one word characterizing a predetermined set of the first and second data received; ■ arcs, said arcs being representative of a functional relationship between said entities, said arcs being described by at least one word; • Determination of a second list of words from the first list of words, said second received data and a second predetermined model, o said second model being a machine learning model from the natural language processing domain, o said second model being trained from ■ a predetermined set of initial word lists, and ■ of a predetermined set of second data; • Determining a list of sentences from said second list of words and a third predetermined model, said third model being a machine learning model from the natural language processing domain, where said third model is at least trained from a set of second lists of words; • Transmission of said list of phrases to said audio system to provide information on said malfunction in said vehicle by voice synthesis.

[0015] Thus, the first, second and third models are determined to the strict minimum, and therefore of minimum size, and are then suitable for being executed on a ECU, calculator, automobile (vehicle marketed to the "general public"). The memory size of these models is then limited, this allows a download in reasonable times, not mobilizing the vehicle for several hours. These models can then be updated in a reasonable time, more quickly than models not determined to the strict minimum. The voice request remains internal to the vehicle's computer system, it is not transmitted to a remote server. The confidentiality of the "conversations" is thus preserved. Also, relevant information on the anomaly is provided at the right time when the anomaly occurs.

[0016] Advantageously, said list of sentences is further determined from said vehicle data and diagnostic data, said second model being further trained from metadata relating to a predetermined set of vehicle data and diagnostic data.

[0017] Thus, the sentence list is contextualized according to the vehicle and the needs. The said second model is even more optimized.

[0018] Advantageously, said vehicle data also includes data relating to said occupant.

[0019] Thus, the list of phrases is also contextualized according to the occupant. The information provided to the occupant is personalized, resulting in improved perceived vehicle quality. The information provided can also be more accurate.

[0020] Advantageously, said first model and said third model are neural network type models.

[0021] Advantageously, said second model is a model of the type, from the English, “encoder / decoder with head classification”.

[0022] Thus, the model is particularly suitable for input data of different categories (data, vehicle, first list of words, diagnostic data, etc.).

[0023] Advantageously, the first, second and third models are further trained from text extractions of diagnostic guides.

[0024] This makes the models easier to predetermine and more optimized for the problem. The information is also more accurate.

[0025] A second aspect of the invention relates to a device comprising an audio system for voice recognition and synthesis, a memory associated with at least one processor configured to implement the method according to the first aspect of the invention.

[0026] The invention also relates to a vehicle comprising the device.

[0027] The invention also relates to a computer program comprising instructions which, when the program is executed by the device according to the second aspect of the invention, cause the latter to implement the method according to the first aspect of the invention. Brief description of the figures

[0028] Other characteristics and advantages of the invention will emerge from the description of the non-limiting embodiments of the invention below, with reference to the appended figures, in which:

[0029] [Fig. 1] schematically illustrates a device, according to a particular exemplary embodiment of the present invention.

[0030] [Fig. 2] schematically illustrates a method of providing information on an operating anomaly of a vehicle, according to a particular exemplary embodiment of the present invention. Detailed description of the invention

[0031] The invention is described below in its non-limiting application to the case of an autonomous motor vehicle traveling on a road or on a traffic lane. Other applications such as a robot in a storage warehouse or a motorcycle on a country road are also conceivable.

[0032] Figure 1 represents an example of a device 101 included in the vehicle, in a network (“cloud”) or in a server. This device 101 can be used as a centralized device in charge of at least certain steps of the method described below with reference to Figure 2. In one embodiment, it corresponds to an autonomous driving computer.

[0033] In the present invention, the device 101 is included in the vehicle.

[0034] This device 101 can take the form of a box comprising printed circuits, any type of computer or even a mobile telephone (“smartphone”).

[0035] The device 101 comprises a random access memory 102 for storing instructions for the implementation by a processor 103 of at least one step of the method as described below. The device also comprises a mass memory 104 for storing data intended to be retained after the implementation of the method.

[0036] The device 101 may further comprise a digital signal processor (DSP) 105. This DSP 105 receives data to format, demodulate and amplify, in a manner known per se, this data.

[0037] The device 101 also comprises an input interface 106 for receiving the data implemented by the method according to the invention and an output interface 107 for transmitting the data implemented by the method according to the invention.

[0038] For example, the input interface 106 can receive the following data: position or geographical location of the vehicle, speed and / or acceleration of the vehicle, set or predetermined positions / speeds / accelerations, engine speed, position and / or travel of the clutch, brake and / or acceleration pedal, detection of other vehicles or objects, position or geographical location of the other vehicles or objects detected, speed and / or acceleration of other vehicles or objects detected, operating states of sensors, confidence index of data from or processed by sensors and / or devices similar to device 101. For example, the sensors capable of providing data are: GPS associated or not with mapping, tachometers, accelerometers, RADAR, LIDAR, lasers, ultrasound, camera, etc.

[0039] The interface 106 can also receive: a text or an alphanumeric sequence of characters, data relating to the vehicle such as a make, a model, an identifier making it possible to determine a make and / or a model of the vehicle, data relating to a state of said vehicle such as a speed, a temperature, or at least one piece of data from the preceding paragraph, data relating to diagnostic data of said vehicle such as a fault code, a list of fault codes, ..., data relating to an occupant, ...

[0040] For example, the output interface 107 can transmit data similar to the data received by the input interface 106. For example, the output interface 107 can also transmit: a list of labels, a first list of words, a second list of words, a list of sentences, metadata relating to a predetermined set of received data, ...

[0041] [Fig. 2] schematically illustrates a method of providing information on an operating anomaly of a vehicle, according to a particular exemplary embodiment of the present invention.

[0042] The method 200 is a method for providing information on an operating anomaly of a vehicle having an audio voice recognition and synthesis system, to inform an occupant of said vehicle, said occupant issuing a verbal request, said method being implemented by the processor 103 and comprising several steps.

[0043] Audio speech recognition and synthesis systems are known. They are capable of converting a verbal request from an occupant into text. Text is understood to mean a succession of characters, for example alphanumeric, that can be interpreted by a computer. For example, these characters are coded, encoded, in ASCII, UTF-8, UTF-18 or other encodings known to those skilled in the art of character encoding.

[0044] The occupant can issue different verbal requests. For example, a verbal request might be the occupant's verbal expression "What does the flashing red light on the dashboard mean?", when the occupant wants to understand the meaning of an alert.

[0045] Step 210, RxTxt, is a step of receiving a text generated from said audio system processing said verbal query. Following the above verbal expression, said audio system is able to transmit to the device 101 a text such as “what does the flashing red light on the dashboard mean”. In one operating mode, the audio system is able to transmit metadata, such as metadata relating to a “Question” by an analysis of the intonation.

[0046] Step 220, LList, is a step of determining a list of labels from said received text and a first predetermined model. Said first model is a machine learning model from the field of natural language processing, NLP, such as neural networks, language models, classification and clustering algorithms, statistical methods, etc. In one operating mode, said first model is a neural network type model whose design method (number of nodes, number of layers, choice of activation functions, etc.) and training method (parameterization of activation functions, etc.) are well known, the remaining difficulty is to manage to choose inputs and outputs of the models relevant to the problem that must be solved.

[0047] Said first model is trained from a predetermined set of texts representative of a verbal request for the provision of information on an operating anomaly of at least one vehicle, and from a predetermined set of said labels. Said labels are a group of characters, at least one word, used to identify and describe sets of words from said predetermined set of texts representative of a verbal request for the provision of information on an operating anomaly of at least one vehicle.

[0048] For example, a predetermined set of texts representative of a verbal request for information on a vehicle malfunction may consist of several subsets. To illustrate, a subset may be linked to an expression introducing a question such as the set {“what”, “how”, “why”, “mean”, “signify”, ...}. In another example, a subset may be linked to a part or device of the vehicle such as the set {“engine”, “dashboard”, “light”, “flashing”, “red”, ...}.

[0049] For example, to illustrate, one label might be the phrase "question" to identify an expression introducing a question. Another label might be the phrase "seer" to identify a "flashing" element, a "light," etc. Thus, words or phrases with similar meanings or that appear in similar contexts will have the same label.

[0050] Advantageously, the construction of the labels is carried out from verbatim reports from customers reporting an anomaly and / or from text extracts from diagnostic guides.

[0051] The first model being predetermined, the automatic learning is carried out offline, before implementation in the RAM 102 or mass 104 memory of the device 101. All the labels and lists of labels are available. All or part of the set can be used for the model learning described below or for the construction of the knowledge graph described below.

[0052] Step 230, RxDataV, is a step of receiving first data, called vehicle data, said first data relating to said vehicle, said data comprising at least one identifier making it possible to determine a make and / or a model of said vehicle, and comprising at least one item of information on a state of said vehicle such as a speed, a temperature, etc.

[0053] It is known that a vehicle is identified by its vehicle identification number or VIN, from the English "Vehicle Identification Number", a sequence alphanumeric character set. The first characters make it possible to identify a brand or manufacturer such as a Peugeot® vehicle, a Cirtoën® vehicle, a car model such as 208®, C3®, ..., a type of engine, a year of manufacture of the model, ... Also, the calculators, ECU or device similar to the device 101, of a vehicle have other data making it possible to identify the brand and model of the vehicle. These calculators are able to transmit to the device 101 data relating to said vehicle

[0054] It is known that the vehicle comprises numerous devices controlled, activated, deactivated, controlled, etc. from sensors and actuators. These devices are capable of providing information on a state of the vehicle. A state corresponds, for example, to data received by the input interface 106, described previously.

[0055] Advantageously, said vehicle data also includes data relating to said occupant. For example, by means of driver or passenger recognition devices, it is possible to receive the first and last names of the occupants and / or the driver, and a driver's driving style,

[0056] For a given set of vehicles, for example vehicles of the same make and model, it is possible to construct, offline, a set of initial data that can be received. This set can be used for the model training described below or for the construction of the knowledge graph described below.

[0057] Step 240, RxDataD, is a step of receiving second data, called diagnostic data, said second data relating to diagnostic data of said vehicle such as a fault code, a list of fault codes, etc.

[0058] It is known that the vehicle has many devices controlled, activated, deactivated, controlled, etc. from sensors and actuators. These devices are able to provide information on their operation. Typically, in the event of a detected anomaly, a fault code, or DTC from the English "Diagnostic Trouble Code", is issued. Generally, a computer archives all the DTCs with a predetermined list of vehicle status. The input interface 106 is able to receive these DTCs, fault code lists, predetermined list of vehicle status.

[0059] For a given set of vehicles, for example vehicles of the same make and model, it is possible to construct, offline, a set of second data that can be received. This set can be used for the model training described below or for the construction of the knowledge graph described below.

[0060] Step 250, WListl, is a step of determining a first list of words from said list of labels, said first and second received data and a predetermined knowledge graph, said graph comprising nodes, said nodes being representative of entities, the entities being predetermined from said labels, and a set of at least one word, said at least one word characterizing a predetermined set of the first and second received data. Said graph also includes arcs, said arcs being representative of a functional relationship between said entities, said arcs being described by at least one word.

[0061] Knowledge graphs, KG acronym for "Knowledge Graph", are known. A KG is a data structure that organizes knowledge in the form of graphs by connecting entities and relationships. The difficulty is to have a list of entities. In the present invention, this list is determined from the set of possible labels of the first model and from the set of said at least one word, said at least one word characterizing a predetermined set of the first and second data received. Said at least one word can be considered similarly to other labels or additional labels that may have as a value, for example, "Vehicle", "Peugeot®", "Citroën®", "208®", "C3®", "engine temperature 200°C", "engine speed 18000 rpm", "flashing light n°12 activated", ...

[0062] Once the list of entities has been obtained, a person skilled in the art knows how to construct the KG and determine the relationships between these entities. The KG is predetermined, it is therefore carried out “offline”. Only the model is implemented in the RAM 102 or mass memory 104. The model can be interpreted by computer in a known manner, making it possible to capture complex relationships between the entities and to provide semantics to the data. Thus, at the output of this KG model, from the list of labels determined in step 220, and from the data from steps 230 and 240, we have a list of words. For example, this list of words can be a set of possible explanations for the state of a warning light such as “engine overheating”, “oil too hot”, “insufficient quantity of engine oil”, “insufficient fuel injection”, “incomplete combustion”, etc.

[0063] The use of a KG makes it possible to simplify all the models, in particular the second model described below. The KG makes it possible to identify different possible explanations for an anomaly poorly described by the occupant. The KG is like a filter on the set of possible explanations for all possible anomalies. The second model makes it possible to identify and diagnose the most probable explanation in relation to a given vehicle operating life situation.

[0064] For a given set of vehicles, for example vehicles of the same make and model, it is possible to construct, offline, a set of initial word lists. This set can be used for the model training described below.

[0065] Step 260, WList2, is a step of determining a second list of words from the first list of words, from said second received data and from a second predetermined model, said second model being a machine learning model from the natural language processing domain, said second model being trained from a predetermined set of first lists of words, and from a predetermined set of second data.

[0066] The said second model is a machine learning model from the field of natural language processing, NLP, such as neural networks, language models, classification and clustering algorithms, statistical methods, etc. Advantageously, said second model is an "encoder / decoder with head classification" type model. This allows different types of input data to be taken into account, such as a list of words and diagnostic data. The construction and parameterization of "encoder / decoder with head classification" type models are known.

[0067] In the case of the invention, we have found that the "encoder / decoder with head classification" type model architecture makes it possible to isolate and identify the anomaly on a list of possible anomalies. The classification head is an additional layer added to the encoder / decoder architecture. The classification head makes it possible to classify the data as inputs to the second model. It makes it possible to provide relationships between a list of words and diagnostic data.

[0068] An encoder and a decoder can each be based on a neural network model, for example recursive, or on a transformer. The outputs of the encoder are the inputs of the decoder. This architecture makes it possible to understand and represent the input data, and to generate a list of words interpretable by the third model.

[0069] For example, at the output of the second model, we have the following second list of words: "Pierre", "Citroën C3 car", "red light meaning", "high engine temperature detection", "low engine oil fault", "check engine oil level"

[0070] For a given set of vehicles, for example vehicles of the same make and model, it is possible to construct, offline, a set of second word lists. This set can be used for learning the model described below.

[0071] Step 270, SList, is a step of determining a list of sentences from said second list of words and a third predetermined model, said third model being a machine learning model from the natural language processing domain, said third model being at least trained from a set of second lists of words.

[0072] The said third model is a machine learning model from the field of natural language processing, NLP, such as neural networks, language models, classification and clustering algorithms, statistical methods, etc. In one operating mode, the said third model is a neural network type model whose design method (number of nodes, number of layers, choice of activation functions, etc.) and training method (parameterization of activation functions, etc.) are well known, the remaining difficulty is to manage to choose inputs and outputs of the models relevant to the problem that must be solved.

[0073] Said third model being at least trained from a set of second word lists. In order to train said third model, it is possible to extract, all or part, of the set of second word lists from the second data model. For example, this extraction can be carried out by simulation by requesting said second model by a set of first word lists and a set of received data, including the second data.

[0074] Advantageously, said list of phrases is further determined from said vehicle data and diagnostic data, said second model being further trained from metadata relating to a predetermined set of vehicle data and diagnostic data. Thus, the list of phrases is contextualized according to the make and / or model of the vehicle. Said third model is even more optimized. For example, a metadata is “Citroën C3®”. The third model is only trained on known operating anomalies for a Citroën C3® vehicle. It does not contain information on other vehicle manufacturers or models. The third model implemented will only include information related to said vehicle.

[0075] Advantageously, the third models are further trained from text extractions of diagnostic guides. Thus, the third model can generate a list of sentences that are more relevant and complete with respect to a detected anomaly. This simplifies the training and said third model, the list of sentences being adapted to respond to a request for the provision of information on an operating anomaly of the vehicle. If the first and second models are further trained from text extractions of diagnostic guides, the determination of the list of sentences is more robust to variations in verbal queries for the same operating request.

[0076] In one operating mode, if the said received data also includes data relating to the said occupant, the list of sentences can be contextualized and personalized in relation to the occupant.

[0077] For example, at the output of the third model, we have the following list of sentences: "Dear Pierre.", "for your Citroën C3 car," "Here is the meaning of the flashing red light.", "We have detected an abnormally high engine temperature.", "It seems that the engine oil level is insufficient.", "Please check the engine oil level with the engine off.", "If oil needs to be added, we remind you that the recommended oil type is 15W40.".

[0078] There are NLGs, from the English "Natural Language Generation", NLP sub-models specially designed to generate sentences from a list of instructions. Advantageously, said third model is obtained from an existing NLG after specialization, additional training and simplifications, from a set of first list of words and, if necessary, from text extractions from diagnostic guides and / or from metadata relating to a predetermined set of vehicle data and diagnostic data.

[0079] Step 280, TxSList, is a step of transmitting said list of sentences to said audio system to provide information about said operating anomaly of said vehicle by voice synthesis. The audio system is then able, by its voice synthesis system, to transmit the operating information to help and guide the occupant.

[0080] The present invention is not limited to the embodiments described above as examples: it extends to other variants.

[0081] For example, the process has been described according to a sequence of steps. Some steps can be carried out in parallel or according to another sequence.

[0082] Thus, an example embodiment has been described above in which words, expressions, groups of words, etc. are involved. An expression can also be a group of words. Advantageously, the words, expressions, and groups of words can be encoded to be easily interpretable by a computer system or by a device 101.

[0083] Advantageously, the training can be carried out on a particular make or model of vehicle. The device 101 will then include in the RAM 102 and mass memory 104 only models adapted and optimized in relation to the vehicle which includes the device 101.

Claims

CLAIMS 1. Method (200) for providing information on an operating anomaly of a vehicle having an audio voice recognition and synthesis system, to inform an occupant of said vehicle, said occupant issuing a verbal request, said method being implemented by a processor (103) and comprising the steps of: • Receiving (210) a text generated from said audio system processing said verbal request; • Determination (220) of a list of labels from said received text and a first predetermined model, o said first model being a machine learning model from the natural language processing domain, o said first model being trained from ■ a predetermined set of texts representative of a verbal request for the provision of information on an operating anomaly of at least one vehicle, and ■ of a predetermined set of said labels; o said labels being a group of characters, at least one word, used to identify and describe sets of words from said predetermined set of texts representative of a verbal request for the provision of information on an operating anomaly of at least one vehicle; • Reception (230) of first data, called vehicle data, said first data relating to said vehicle, said data comprising at least one identifier making it possible to determine a make and / or a model of said vehicle, and comprising at least one item of information on a state of said vehicle such as a speed, a temperature, etc.; • Reception (240) of second data, called diagnostic data, said second data relating to diagnostic data of said vehicle such as a fault code, a list of fault codes, etc.; • Determination (250) of a first list of words from said list of labels, said first and second received data and a predetermined knowledge graph, where said graph comprises ■ nodes, said nodes being representative of entities, the entities being predetermined from • of said labels, and • a set of at least one word, said at least one word characterizing a predetermined set of the first and second data received; arcs, said arcs being representative of a functional relationship between said entities, said arcs being described by at least one word; • Determination (260) of a second list of words from the first list of words, said second received data and a second predetermined model, o said second model being a machine learning model from the natural language processing domain, o said second model being trained from ■ a predetermined set of initial word lists, and ■ of a predetermined set of second data; • Determination (270) of a list of sentences from said second list of words and a third predetermined model, said third model being a machine learning model of the natural language processing domain, where said third model is at least trained from a set of second lists of words; • Transmission (280) of said list of sentences to said audio system to provide information on said operating anomaly of said vehicle by voice synthesis.

2. Method according to claim 1, wherein said list of sentences is further determined from said vehicle data and diagnostic data, said second model being further trained from metadata relating to a predetermined set of vehicle data and diagnostic data.

3. Method according to the preceding claim, said vehicle data further comprises data relating to said occupant.

4. Method according to one of the preceding claims, in which said first model and said third model are neural network type models.

5. Method according to one of the preceding claims, in which said second model is a model of the type, from the English, “encoder / decoder with head classification”.

6. Method according to one of the preceding claims, in which the first, second and third models are, in addition, trained from text extractions of diagnostic guides.

7. Device (101) comprising an audio voice recognition and synthesis system, a memory (102) associated with at least one processor (103) configured to implement the method according to one of the preceding claims.

8. Vehicle comprising the device according to the preceding claim.

9. Computer program comprising instructions which, when the program is executed by the device (101) according to claim 8, cause the latter to implement the method according to one of claims 1 to 7.

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