Remote diagnosis of a vehicle

Abstract

The disclosure relates to a method comprising: receiving a first message (M1) from the vehicle (123) comprising defect information related to the vehicle (123), performing a first identification of one or more diagnostic processes, performing a second identification of one or more diagnostic actions to be performed at the remotely located vehicle (123), transmitting a second message (M2) to the remotely located vehicle (123) comprising the one or more diagnostic actions, receiving a third message (M3) from the remotely located vehicle (123) comprising diagnostic data resulting from performing the one or more diagnostic actions at the remotely located vehicle (123), selecting a set of diagnostic processes from the one or more diagnostic processes, transmitting a fourth message to a workshop, wherein the fourth message comprises at least the selected set of diagnostic processes.

Description

[0001] REMOTE DIAGNOSIS OF A VEHICLE

[0002] TECHNICAL FIELD

[0003] The present invention relates to a method configured to assist in diagnosing a remotely located vehicle. The invention further relates to a control arrangement, a server, a computer program product, and a computer-readable storage medium.

[0004] BACKGROUND

[0005] In logistics networks, e.g., transporting different types of cargo, vehicles are an essential part of such networks. The availability and timely arrival of those vehicles are of outmost importance to all actors in the logistics network.

[0006] Traditionally, unwanted failure or breakdown of those vehicles are addressed by periodic maintenance which can be planned in advance, thereby minimizing impact of vehicle downtime. Periods when the vehicle is in operation may be referred to as vehicle uptime, and periods when spent recovering, diagnosing, and repairing the vehicle may be referred to as vehicle downtime.

[0007] In cases where there is an unexpected failure or breakdown of those vehicles, the vehicle is transported to a workshop where diagnosis of the vehicle is started. Patterns or processes of successful historical repairs together with other data such as historical vehicle data, vehicle operational variables or vehicle specification may be used, e.g., by a service coordinator, to diagnose problems and generate repair recommendations to technicians. In other words, the vehicle downtime typically includes recovery of the vehicle, initial diagnosis of the vehicle and iterative repair actions and finally transport of the vehicle from the workshop to its point of operation. It is not uncommon that time consuming troubleshooting / diagnosing of the vehicle must be performed by the technician in the workshop without any historical data at hand.

[0008] Further conventional solutions introduce predictive maintenance, which aim to optimize timing of periodic maintenance. However, this still does not address the delay introduced when there is an unexpected failure or breakdown of those vehicles, and thus, do not address the disadvantage to introduce delay before the vehicle can be returned to an operational state.

[0009] To further complicate matters, availability of workshops are typically bottlenecks in the process of getting vehicles back in an operational state. Thus, there is a need for a method to further improve vehicle uptime.

[0010] OBJECTS OF THE INVENTION

[0011] An objective of embodiments of the present invention is to provide a solution which mitigates or solves the drawbacks described above.

[0012] SUMMARY OF THE INVENTION

[0013] The above and further objectives are achieved by the subject matter described herein. Further advantageous implementation forms of the invention are described herein. The invention is set out in the appended claims. The scope of the invention is defined by the claims, which are incorporated into this section by reference.

[0014] According to a first aspect of the invention, the above mentioned objective is achieved by a method performed by a server configured to assist in diagnosing a remotely located vehicle, the method comprising: receiving a first message from the remotely located vehicle comprising defect information related to the remotely located vehicle, performing a first identification of one or more diagnostic processes using the defect information, wherein the first identification is performed using a first trained model, performing a second identification of one or more diagnostic actions to be performed at the remotely located vehicle, wherein the second identification is performed using a second trained model, transmitting a second message to the remotely located vehicle comprising the one or more diagnostic actions, receiving a third message from the remotely located vehicle comprising diagnostic data resulting from performing the one or more diagnostic actions at the remotely located vehicle, selecting a set of diagnostic processes from the one or more diagnostic processes, wherein the set of diagnostic processes is selected using a third trained model and the received diagnostic data, transmitting a fourth message to a workshop, wherein the fourth message comprises at least the selected set of diagnostic processes.

[0015] An advantage of the present disclosure is that delay before the vehicle can be returned to an operational state can be reduced by advancing diagnosis of the vehicle as far as possible when the vehicle is in a remote location relative to the workshop.

[0016] In one embodiment according to the first aspect, the defect information comprises a selection of any of a fault code and / or a detected anomaly condition of the vehicle.

[0017] In one embodiment according to the first aspect, the first trained model takes defect information as input parameters and provides the one or more diagnostic process as output. In one embodiment according to the first aspect, the first trained model is trained using first training data indicative of historic vehicle defects and related diagnostic processes followed to correct the defect, wherein the diagnostic processes comprise at least a sequence of diagnostic actions.

[0018] In one embodiment according to the first aspect, the second trained model takes the diagnostic processes as input parameters and provides diagnostic actions to be performed at the remotely located vehicle as output.

[0019] This embodiment has the advantage to further reducing delay before the vehicle can be returned to an operational state by taking a selection of diagnostic actions with vehicle is in a remote location relative to the workshop.

[0020] In one embodiment according to the first aspect, the second trained model is trained using second training data indicative of historic selections of diagnostic processes and related diagnostic actions taken to correct a defect.

[0021] In one embodiment according to the first aspect, the third trained model takes one or more diagnostic processes and related diagnostic data as input parameters and a set of diagnostic processes, selected from the one or more diagnostic processes, as output.

[0022] In one embodiment according to the first aspect, the set of diagnostic processes is a subset of the one or more diagnostic processes.

[0023] In one embodiment according to the first aspect, the third message further comprises a selection of any of a proposed solution, list of parts numbers, a list of further diagnostic actions, and an estimated time to implement the solution.

[0024] In one embodiment according to the first aspect, wherein the one or more diagnostic actions comprises a selection of any of obtaining sensor data, controlling the vehicle, and prompting a user to perform manual diagnostic actions.

[0025] In one embodiment according to the first aspect, the defect report is obtained from a control arrangement of the remotely located vehicle.

[0026] According to a second aspect of the invention, the above-mentioned objective is achieved by a server, the server comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said server is operative to perform the method according to the first aspect.

[0027] According to a third aspect of the invention, the above mentioned objective is achieved by a vehicle communicatively coupled to the server according to the second aspect, the vehicle comprising: one or more sensors configured to generate sensor data, a user interface configured to interact with a driver of the vehicle, and a control arrangement, the control arrangement comprising a processor, and a memory, said memory containing instructions executable by said processor, whereby said control arrangement is operative to: transmit a first message to the server comprising defect information related to the vehicle, receiving a second message comprising one or more diagnostic actions, generating diagnostic data by performing the one or more diagnostic actions, transmitting a third message comprising diagnostic data resulting from performing the one or more diagnostic actions.

[0028] In one embodiment according to the third aspect, the control arrangement is operative to generate the diagnostic data by obtaining sensor data from the one or more sensors and processing the sensor data to diagnostic data.

[0029] In one embodiment according to the third aspect, the control arrangement is operative to generate the diagnostic data by presenting one or more diagnostic actions to be performed by the driver via the user interface and receiving diagnostic data from the driver via the user interface.

[0030] In one embodiment according to the third aspect, the control arrangement is ay one of an Electronic Control Unit, ECU communicatively coupled to the vehicle, a smartphone communicatively coupled to the vehicle, or a tablet computer communicatively coupled to the vehicle.

[0031] According to a fourth aspect of the invention, a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to the first aspect.

[0032] According to a fifth aspect of the invention, a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to according to the first aspect.

[0033] Reference will be made to the appended sheets of drawings that will first be described briefly. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the drawings.

[0034] BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Fig. 1 shows a system configured to assist in diagnosing a remotely located vehicle according to one or more embodiments of the present disclosure. Fig. 2 shows an example scenario according to one or more embodiments of the present disclosure.

[0036] Fig. 3 shows a computer according to one or more embodiments of the present disclosure.

[0037] Fig. 4 shows a flowchart of a method according to one or more embodiments of the present disclosure.

[0038] Fig. 5 shows a flowchart of a method according to one or more embodiments of the present disclosure.

[0039] A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments.

[0040] DETAILED DESCRIPTION

[0041] An “or” in this description and the corresponding claims is to be understood as a mathematical OR which covers ’’and” and “or”, and is not to be understand as an XOR (exclusive OR). The indefinite article “a” in this disclosure and claims is not limited to “one” and can also be understood as “one or more”, i.e., plural.

[0042] In the present disclosure, the expression “computer” and / or “control arrangement” and / or “device” and / or “system” denotes a unit comprising processor, and a memory, said memory containing instructions executable by said processor, wherein said unit is configured to perform any of the methods described herein. The control arrangement is typically capable of receiving input data that is comprised in control signals, and to control other units by sending commands comprised in control signals. In one example, the control arrangement is a general-purpose computer or an Electric Control Unit, ECU.

[0043] In the present disclosure, the expression “diagnosing” denotes finding and resolving defects of a vehicle, typically applied to repair failed parts or modules of a vehicle. Diagnosing typically involves systematic search for the source of a problem, faulty part, or faulty module of a vehicle in order to solve the problem and bring the vehicle to an operational state again. In other words, performing a sequence of diagnostic actions.

[0044] In the present disclosure, the expression “diagnostic process” denotes a plan, scheme or workflow followed to resolve an indicated defect of a vehicle. In other words, the diagnostic process typically describes a sequence of diagnostic actions. In the present disclosure, the expression “diagnostic action” denotes actions taken to resolve an indicated defect of a vehicle. Examples of diagnostic actions may be measuring characteristics of the vehicle, documenting status of the vehicle or components of the vehicle, replacing parts or components of the vehicle, adjusting parts or components of the vehicle, calibrating parts, or components of the vehicle.

[0045] In the present disclosure, the expression “remotely located vehicle” denotes the vehicle being remotely located from a workshop, typically out on the road performing tasks.

[0046] In the present disclosure, the expression “defect information” denotes information indicative of a state of the vehicle deviating from a normal state. Defect information may in one example be generated by a control arrangement of the vehicle by obtaining sensor data from one or more sensors and processing the sensor data to defect information related to the vehicle, e.g., a fault code indicating “Engine temperature too low.” Defect information may in one further example be generated by a control arrangement of the vehicle by obtaining sensor data from one or more sensors and processing the sensor data via a trained model to a detect an anomaly condition of the vehicle. Defect information may in one further example be generated by a control arrangement of the vehicle by obtaining diagnostic information from the driver via the user interface, e.g., voice or text input of a phrase “loud noise heard from the back of the vehicle”.

[0047] In the present disclosure, the expression “trained model” or “training of a model” signifies the step of producing a model from a training set of data by using machine learning. In one example this involves obtaining a training data set, having predetermined input values and / or desired output values. This may include natural language description of experienced defects / problems in a vehicle, and the resulting parts of the vehicle being replaced / repaired to solve the defects / problems. The model may then be trained using machine learning, e.g., unsupervised, or supervised learning, by repeatedly providing the input values to candidate models and registering model output values and calculating performance measure for each candidate model based on the training output values and model output values. The candidate model producing the highest performance measure is then selected as the trained model. The trained model may e.g., be configured to identify one or more parts of the vehicle to be repaired. The model may in one example be trained by selecting from any number of candidate models forming a hypothesis space, based on a performance measure. The performance measure may indicate how well the model output corresponds to the desired output in the training set for the model. The candidate model producing the highest performance measure may then be selected as the trained model. This may also be seen as tuning parameters of the model. Examples of machine learning training methods / techniques are supervised classification methods such as neural networks, Support Vector Machines, Naive Bayes, and k-Nearest Neighbor.

[0048] Fig. 1 shows a system 100 configured to assist in diagnosing a remotely located vehicle 123 according to one or more embodiments of the present disclosure. The system comprises at least one set of a processor, and a memory, said memory containing instructions executable by said processor, wherein said system is configured to perform any of the method steps described herein.

[0049] The system may optionally comprise one or more user interfaces and / or one or more displays configured to display indications to a user and / or receive input from a user.

[0050] The system may further comprise databases and resources for training and maintaining one or more trained models. Examples of databases may be databases for historic defect symptoms / information, main groups, parts database with associated article numbers and estimated service times of diagnostic actions. The system may further optionally comprise one or more communications interfaces, e.g., capable of receiving input from a driver, error codes from the vehicle, data from external databases etc.

[0051] The system may optionally comprise a server 1 10. The server 110 may e.g., be a cloud server. The server 1 10 may typically comprise a processor, memory, the databases, and the resources for training / generating and maintaining trained models.

[0052] The system may optionally further comprise a vehicle device 120, e.g., a control arrangement, configured to interact with a user or driver via a user interface and / or obtaining sensor data from one or more sensors, e.g., a fault code. E.g., receiving a defect information via a user interface from a driver of the vehicle 123 or provide indications to the driver of the vehicle 123 via the user interface. The server 110 may typically comprise a processor and a memory.

[0053] The system may optionally further comprise a coordinator device 130 configured to configured to interact with a supervisor via a user interface. In one or more examples, the supervisor is a workshop coordinator, that performs an initial diagnosis and / or assigns technician resources to repair a defect vehicle. The coordinator device 130 may typically comprise a processor and a memory.

[0054] The system may optionally further comprise a technician device 140 configured to interact with the technician, e.g., to display a work order. The work order may e.g., include information such as a proposed solution, a parts number, one or more diagnostic actions, and an estimated time to implement the proposed solution and or diagnostic actions. In one or more examples, the technician is a mechanic, that studies a work order on a display. After successfully repairing the vehicle, the technician may provide feedback on diagnostic actions taken and the implemented solution. The feedback is fed back to the system to update databases and / or to act as training data when re-training / improving trained models. The feedback may optionally be provided to other devices 1 10, 120, 130 for presentation to users 221 , 231 . The technician device 140 may typically comprise a processor and a memory.

[0055] The system may optionally further comprise a communications network 150, interconnecting any of the mentioned devices and any other nodes, such as external database servers or cloud servers.

[0056] Fig. 2 shows an example scenario according to one or more embodiments of the present disclosure.

[0057] In the example below a certain number of devices are listed for easier understanding of the concept. It is understood that the system 100 may comprise any number of devices, and the method steps described herein may be freely distributed amongst those devices without deviating from the present disclosure.

[0058] In this example, defect information is generated by a control arrangement, CA, of the vehicle 123 by obtaining sensor data from one or more sensors and processing the sensor data to defect information related to the vehicle 123, e.g., a fault code indicating “Engine temperature too low”. Additionally, or alternatively, defect information may be obtained from the driver via a user interface.

[0059] A message M1 , comprising the defect information, is sent over a communications network 150 to the server 1 10. The server 110 then use the defect information as input.

[0060] The server 110 performs a first identification of one or more diagnostic processes using the defect information. The first identification is performed using a first trained model. In one example, the identified diagnostic processes may be indicative of a first diagnostic process “Replace thermostat” and an optional associated reliability measure, e.g., 90% together with a second diagnostic process “Take no further action” with an optional associated reliability measure, e.g., 80%.

[0061] The first trained model is typically trained using first training data indicative of historic vehicle defects and related diagnostic processes followed to correct the defect. The diagnostic processes typically comprise at least a sequence of diagnostic actions. In other words, patterns or processes of successful historical repairs starting with defect information are used to train the model. The server 110 then performs a second identification of one or more diagnostic actions to be performed at the remotely located vehicle 123. The one or more diagnostic actions are typically performed to further increase the reliability measure of the diagnostic processes.

[0062] In one example, if defect information indicates that a battery of the vehicle is not charging. The first model may then provide two processes “Replace alternator” and “Replace alternator belt.” By following a diagnostic process and taking corresponding diagnostic actions, and as a result obtaining diagnostic data indicative of output voltage from the alternator, it may be determined that the alternator belt should be replaced.

[0063] The second identification is performed using a second trained model. In one example, the diagnostic process comprises diagnostic actions “measure ambient temperature” and “measure fuel consumption”.

[0064] The second trained model typically takes diagnostic processes as input parameters and provides diagnostic actions to be performed at the remotely located vehicle 123 as output. The second trained model also assesses whether, or not, additional diagnostic data would be helpful in selecting the set of diagnostic processes. The second trained model is trained using second training data indicative of historic selections of diagnostic processes and related diagnostic actions taken to correct a defect. In particular, diagnostic actions that assist in narrowing down the potentially one or more diagnostic processes to address a defect.

[0065] The server 110 then transmits a second message to the remotely located vehicle 123 comprising the one or more diagnostic actions.

[0066] A control arrangement in the remotely located vehicle 123 then performs the one or more diagnostic actions, i.e., measure ambient temperature and measures fuel consumption and sends a third message to the server 1 10.

[0067] The server 1 10 then receives the third message M3 from the remotely located vehicle 123 comprising diagnostic data resulting from performing the one or more diagnostic actions at the remotely located vehicle 123.

[0068] In a first example, the diagnostic data is indicative of ambient temperature around the vehicle to be -30 degrees Celsius and fuel consumption classified as “low”.

[0069] In a second example, the diagnostic data is indicative of ambient temperature around the vehicle to be +20 degrees Celsius and fuel consumption classified as “high”. The server 110 then selects a set of diagnostic processes from the one or more diagnostic processes, wherein the set of diagnostic processes is selected using a third trained model and the received diagnostic data.

[0070] The third trained model typically takes one or more diagnostic processes and related diagnostic data as input parameters and a provides set of diagnostic processes, selected from the one or more diagnostic processes, as output.

[0071] In the first example above, the diagnostic data is indicative of ambient temperature around the vehicle to be -30 degrees Celsius and fuel consumption classified as “low”, the diagnostic process “Take no further action” is selected to the set. In other words, the diagnostic process “Replacing thermostat” can be eliminated from the identified one or more diagnostic processes.

[0072] In the second example above, the diagnostic data is indicative of ambient temperature around the vehicle to be +20 degrees Celsius and fuel consumption classified as “high”, the diagnostic process The diagnostic process “Replacing thermostat” is selected to the set. In other words, the diagnostic process “Take no further action” can be eliminated from the identified one or more diagnostic processes.

[0073] The server 110 then transmits a fourth message M4 to a workshop, e.g., to the coordinator device 130 and / or to the technician device 140. The fourth message M4 comprises at least the selected set of diagnostic processes. In this example , “Replacing thermostat” or “Take no further action”.

[0074] Optionally, the workshop, e.g., via the coordinator device 130 and / or to the technician device 140, sends appointment information to the remotely located vehicle 123 in a fifth message M5, e.g., time and geographical location for service of the vehicle.

[0075] In yet an example scenario, the defect information is indicative of “coolant fluid level dropping”. The first trained model identifies diagnostic processes “Replacing coolant hose” and “Replacing head gasket”, optionally with corresponding reliability measures. A second identification of diagnostic actions is performed as the diagnostic actions “Check coolant fluid level in reservoir”, “Check for indication of fluid leaks under the vehicle” and “Check exhaust for white smoke”. The driver and / or CA / sensors of the vehicle performs the diagnostic actions, e.g., capturing images and / or obtaining sensor data and sends the resulting diagnostic data to the server. The diagnostic process “Replacing coolant hose” is selected to the set by the server 1 10. The server 110 then transmits a fourth message M4 to the workshop, e.g., to the coordinator device 130 and / or to the technician device 140. The fourth message M4 comprises at least the selected diagnostic process “Replacing coolant hose” and optionally diagnostic data, such as captured images and / or parts numbers of required parts. In this example , “Replacing coolant hose” and images and / or parts number of the corresponding hose. Optionally, the workshop, e.g., via the coordinator device 130 and / or to the technician device 140, sends appointment information to the remotely located vehicle 123 in a fifth message M5.

[0076] In yet an example scenario, the defect information is indicative of “Windshield wipers not working”. The first trained model identifies diagnostic processes “Replacing cable harness” and “Replacing wiper motor”, optionally with corresponding reliability measures. A second identification of diagnostic actions is performed as the diagnostic actions “Activate wiper motor from control arrangement”, “Detect motion of wiper arms”. The driver and / or CA / sensors of the vehicle performs the diagnostic actions, e.g., capturing video and / or obtaining sensor data and sends the resulting diagnostic data to the server in a third message M3. For this particular example, diagnostic data indicates that motion of the wiper arms is detected. The diagnostic process “Replacing cable harness” is selected to the set by the server 110. The server 1 10 then transmits a fourth message M4 to the workshop, e.g., to the coordinator device 130 and / or to the technician device 140. The fourth message M4 comprises at least the selected diagnostic process “Replacing cable harness” and optionally diagnostic data, such as captured images and / or parts numbers of required parts. In this example , “Replacing cable harness” and optionally images and / or parts number of the corresponding cable harness. Optionally, the workshop, e.g., via the coordinator device 130 and / or to the technician device 140, sends appointment information to the remotely located vehicle 123 in a fifth message M5.

[0077] Fig. 3 shows a computer 110, 120, 130, 140 according to one or more embodiments of the present disclosure. The computer may e.g., be in the form of an Electronic Control Unit, a server, an on-board computer, a control arrangement, a vehicle mounted computer system or a navigation device. The computer may comprise a processor or processing means 312 communicatively coupled to a transceiver 304 configured for wired or wireless communication. Further, the computer may further comprise at least one optional antenna (not shown in figure). The antenna may be coupled to the transceiver 304 and is configured to transmit and / or emit and / or receive wireless signals in a wireless communication system, e.g., wireless signals comprising diagnostic actions or diagnostic data. In one example, the processor 312 may be any of a selection of processing circuitry and / or a central processing unit and / or processor modules and / or multiple processors configured to cooperate with each-other. Further, the computer may further comprise a memory 315. The memory 315 may contain instructions executable by the processor to perform any of the methods described herein. The memory and / or computer-readable storage medium referred to herein may comprise of essentially any memory, such as a ROM (Read-Only Memory), a PROM (Programmable Read-Only Memory), an EPROM (Erasable PROM), a Flash memory, an EEPROM (Electrically Erasable PROM), or a hard disk drive.

[0078] In a further embodiment, the computer may further comprise and / or be coupled to one or more sensors configured to e.g., receive and / or obtain and / or measure physical properties pertaining to the system 100 or vehicle 123 and send one or more sensor signals indicative of the physical properties to the processing means 312.

[0079] In one or more embodiments the computer may further comprise an input device 317, configured to receive input or indications from a user and send a user-input signal indicative of the user input or indications to the processor or processing means 312.

[0080] In one or more embodiments the computer may further comprise a display 318 configured to receive a display signal indicative of rendered objects, such as text or graphical user input objects, from the processor or processing means 312 and to display the received signal as objects, such as text or graphical user input objects.

[0081] In one embodiment the display 318 is integrated with the user input device 317 and is configured to receive a display signal indicative of rendered objects, such as text or graphical user input objects, from the processing means 312 and to display the received signal as objects, such as text or graphical user input objects, and / or configured to receive input or indications from a user and send a user-input signal indicative of the user input or indications to the processing means 312.

[0082] In embodiments, the processing means 312 is communicatively coupled to a selection of any of the memory 315 and / or the communications interface and / or transceiver and / or the input device 317 and / or the display 318 and / or the one or more sensors. In embodiments, the transceiver 304 communicates using wired and / or wireless communication techniques. The wired or wireless communication techniques may comprise any of a CAN bus, Bluetooth, WiFi, GSM, UMTS, LTE or LTE advanced communications network or any other wired or wireless communication network known in the art.

[0083] The control arrangement, CA, described herein may comprise al or a selection of the features described in relation to Fig. 3.

[0084] In one embodiment, a CA 120 is provided, the CA comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said CA is operative to perform any of the methods described herein. The server 110 described herein may comprise al or a selection of the features described in relation to Fig. 3.

[0085] In one embodiment, a server 110 is provided, the server comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said server is operative to perform any of the methods described herein.

[0086] Fig. 4 shows a flowchart of a method 400 according to one or more embodiments of the present disclosure. In embodiments, the method is a computer implemented method and may be performed by the server 110 configured to assist in diagnosing a remotely located vehicle 123. The term remotely located in this context is to be interpreted as the vehicle 123 having a different geographical location than the server 110 and / or a repair workshop. The method comprises:

[0087] Step 410: receiving a first message M1 from the remotely located vehicle 123 comprising defect information related to the vehicle 123.

[0088] The defect information comprises a selection of any of a fault code and / or a detected anomaly condition of the vehicle and / or a report from the driver of the vehicle 123. An anomaly condition may be detected by providing defect information, e.g., sensor data of the vehicle, to a trained detection model. The trained detection model may be trained using defect information recorded when known defects has occurred at the vehicle 123.

[0089] In one embodiment, the first message M1 is received from a control arrangement, CA, 120 of the remotely located vehicle 123.

[0090] In one example, the defect information comprises a selection of “Engine temperature too low”, “coolant fluid level dropping”, Windshield wipers not working. Any information related to a defect of the vehicle may be provided without departing from the present disclosure.

[0091] Step 420: performing a first identification of one or more diagnostic processes using the defect information, wherein the first identification is performed using a first trained model.

[0092] In one embodiment, the first trained model takes defect information as input parameters and provides the one or more diagnostic process as output. Additionally, or alternatively, the first trained model is trained using first training data. The first training data is indicative of historic vehicle defects, related defect information and related diagnostic processes followed to correct the defect. Additionally, or alternatively, the diagnostic processes comprise at least sequences of diagnostic actions. Step 430: performing a second identification of one or more diagnostic actions to be performed at the remotely located vehicle 123. The second identification is performed using a second trained model.

[0093] Additionally, or alternatively, the one or more diagnostic actions comprises a selection of any of obtaining sensor data, controlling the vehicle, and prompting a user to perform manual diagnostic actions and / or obtaining user input.

[0094] Additionally, or alternatively, the second trained model takes the diagnostic processes as input parameters and provides diagnostic actions to be performed at the remotely located vehicle 123 as output.

[0095] Examples of diagnostic actions are further provided in relation to Fig. 2.

[0096] Additionally, or alternatively, the second trained model is trained using second training data indicative of historic selections of diagnostic processes and related diagnostic actions taken to narrow down the different diagnostic processes.

[0097] Examples of diagnostic processes and related diagnostic actions are further provided in relation to Fig. 2.

[0098] Step 440: transmitting a second message M2 to the remotely located vehicle 123 comprising the one or more diagnostic actions.

[0099] Step 450: receiving a third message M3 from the remotely located vehicle 123 comprising diagnostic data resulting from performing the one or more diagnostic actions at the remotely located vehicle 123.

[0100] In one embodiment, the third message M3 further comprises a selection of any of a proposed solution, list of parts numbers, a list of further diagnostic actions, and an estimated time to implement the solution.

[0101] Diagnostic data resulting from performing the one or more diagnostic actions is further described in relation to Fig. 2.

[0102] Step 460: selecting a set of diagnostic processes from the one or more diagnostic processes, wherein the set of diagnostic processes is selected using a third trained model and the received diagnostic data.

[0103] In one embodiment, the third trained model takes one or more diagnostic processes and optionally related diagnostic data as input parameters and a set of diagnostic processes, selected from the one or more diagnostic processes, as output.

[0104] Selecting the set of diagnostic processes is further described in relation to Fig. 2. Step 470: transmitting a fourth message to a workshop, wherein the fourth message comprises at least the selected set of diagnostic processes. Additionally, or alternatively, the fourth message further comprises a selection of the defect information and / or the diagnostic data.

[0105] In one embodiment, the defect information comprises a selection of any of a fault code and / or a detected anomaly condition of the vehicle.

[0106] In one embodiment, the set of diagnostic processes is a subset of the one or more diagnostic processes.

[0107] Fig. 5 shows a flowchart of a method 500 according to one or more embodiments of the present disclosure. The method being performed by a control arrangement ,CA. The CA is communicatively coupled to the server 1 10, the CA comprises a processor, and a memory, said memory containing instructions executable by said processor, whereby said CA is operative to:

[0108] Step 510: transmit a first message M1 to the server 110 comprising defect information related to the vehicle 123.

[0109] Defect information is further provided in relation to Fig. 2.

[0110] Step 520: receive a second message M2 comprising one or more diagnostic actions.

[0111] Defect actions is further provided in relation to Fig. 2.

[0112] Step 530: generate diagnostic data by performing or assisting in performing the one or more diagnostic actions.

[0113] Step 540: transmit a third message M3 comprising diagnostic data resulting from performing the one or more diagnostic actions.

[0114] In one embodiment, the CA is operative to: generate the diagnostic data by obtaining sensor data from the one or more sensors and processing the sensor data to diagnostic data.

[0115] In one embodiment, the CA is operative to: generate the diagnostic data by presenting one or more diagnostic actions to be performed by the driver via the user interface and receiving diagnostic data from the driver via the user interface.

[0116] In one embodiment, the control arrangement CA is a smartphone communicatively coupled to the vehicle, a tablet computer communicatively coupled to the vehicle or an Electronic Control Unit, ECU.

[0117] In a further aspect of the disclosure, a vehicle 123 is provided and is communicatively coupled to the server 110. The vehicle comprises: one or more sensors configured to generate sensor data, a user interface configured to interact with a driver of the vehicle, and the CA described in relation to Fig. 3 and Fig. 5.

[0118] In embodiments, the communications network 150 communicate using wired or wireless communication techniques that may include at least one of a Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), Universal Mobile Telecommunications System, Long term evolution, High Speed Downlink Packet Access (HSDPA), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth®, Zigbee®, Wi-Fi, Voice over Internet Protocol (VoIP), LTE Advanced, IEEE802.16m, Wireless MAN-Advanced, Evolved High-Speed Packet Access (HSPA+), 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), Ultra Mobile Broadband (UMB) (formerly Evolution-Data Optimized (EV-DO) Rev. C), Fast Low-latency Access with Seamless Handoff Orthogonal Frequency Division Multiplexing (Flash-OFDM), High Capacity Spatial Division Multiple Access (iBurst®) and Mobile Broadband Wireless Access (MBWA) (IEEE 802.20) systems, High Performance Radio Metropolitan Area Network (HIPERMAN), Beam-Division Multiple Access (BDMA), World Interoperability for Microwave Access (WiMAX) and ultrasonic communication, etc., but is not limited thereto.

[0119] Moreover, it is realized by the skilled person that the system and / or devices 110, CA / 120, 130, 140 may comprise the necessary communication capabilities in the form of e.g., functions, means, units, elements, etc., for performing the present solution. Examples of other such means, units, elements and functions are: processors, memory, buffers, control logic, encoders, decoders, rate matchers, de-rate matchers, mapping units, multipliers, decision units, selecting units, switches, interleavers, de-interleavers, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, MSDs, encoder, decoder, power supply units, power feeders, communication interfaces, communication protocols, etc. which are suitably arranged together for performing the present solution.

[0120] Especially, the processor and / or processing means of the present disclosure may comprise one or more instances of processing circuitry, processor modules and multiple processors configured to cooperate with each-other, Central Processing Unit (CPU), a processing unit, a processing circuit, a processor, an Application Specific Integrated Circuit (ASIC), a microprocessor, a Field-Programmable Gate Array (FPGA) or other processing logic that may interpret and execute instructions. The expression “processor” and / or “processing means” may thus represent a processing circuitry comprising a plurality of processing circuits, such as, e.g., any, some or all of the ones mentioned above. The processing means may further perform data processing functions for inputting, outputting, and processing of data comprising data buffering and device control functions, such as call processing control, user interface control, or the like.

[0121] Finally, it should be understood that the invention is not limited to the embodiments described above, but also relates to and incorporates all embodiments within the scope of the appended independent claims.

Claims

CLAIMS1 . A method performed by a server (110) configured to assist in diagnosing a remotely located vehicle (123), the method comprising: receiving a first message (M1 ) from the remotely located vehicle (123) comprising defect information related to the remotely located vehicle (123), performing a first identification of one or more diagnostic processes using the defect information, wherein the first identification is performed using a first trained model, performing a second identification of one or more diagnostic actions to be performed at the remotely located vehicle (123), wherein the second identification is performed using a second trained model, wherein the second trained model takes the one or more diagnostic processes as input parameters and provides the one or more diagnostic actions as output, transmitting a second message (M2) to the remotely located vehicle (123) comprising the one or more diagnostic actions, receiving a third message (M3) from the remotely located vehicle (123) comprising diagnostic data resulting from performing the one or more diagnostic actions at the remotely located vehicle (123), selecting a set of diagnostic processes from the one or more diagnostic processes, wherein the set of diagnostic processes is selected using a third trained model and the received diagnostic data, transmitting a fourth message (M4) to a workshop, wherein the fourth message comprises at least the selected set of diagnostic processes.

2. The method according to claim 1 , wherein the defect information comprises a selection of any of a fault code and / or a detected anomaly condition of the vehicle.

3. The method according to any of the preceding claims, wherein the first trained model takes defect information as input parameters and provides the one or more diagnostic process as output.

4. The method according to claim 3, wherein the first trained model is trained using first training data indicative of historic vehicle defects and related diagnostic processes followed tocorrect the defect, wherein the diagnostic processes comprise at least a sequence of diagnostic actions.

5. The method according to any of the preceding claims, wherein the second trained model takes the diagnostic processes as input parameters and provides diagnostic actions to be performed at the remotely located vehicle (123) as output.

6. The method according to claim 5, wherein the second trained model is trained using second training data indicative of historic selections of diagnostic processes and related diagnostic actions taken to correct a defect.

7. The method according to any of the preceding claims, wherein the third trained model takes one or more diagnostic processes and related diagnostic data as input parameters and a set of diagnostic processes, selected from the one or more diagnostic processes, as output.

8. The method according to claim 7, wherein the set of diagnostic processes is a subset of the one or more diagnostic processes.

9. The method according claim 1 , wherein the third message further comprises a selection of any of a proposed solution, list of parts numbers, a list of further diagnostic actions, and an estimated time to implement the solution.

10. The method according to any of the preceding claims, wherein the one or more diagnostic actions comprises a selection of any of obtaining sensor data, controlling the vehicle, and prompting a user to perform manual diagnostic actions.1 1 . The method according to any of the preceding claims, wherein the defect report is obtained from a control arrangement (120) of the remotely located vehicle (123).

12. A server, the server comprising: a processor, and a memory, said memory containing instructions executable by said processor, whereby said server is operative to perform the method according to any of claims 1 - 11.

13. A vehicle (123), the vehicle comprising: one or more sensors configured to generate sensor data,a user interface configured to interact with a driver of the vehicle, and a control arrangement (120), the control arrangement (120) comprising a processor, and a memory, said memory containing instructions executable by said processor, whereby said control arrangement (120) is operative to: transmit a first message (M1 ) to a server (1 10) according to claim 12, the first message (M1 ) comprising defect information related to the vehicle (123), receiving, from the server (110), a second message (M2) comprising one or more diagnostic actions, generating diagnostic data by performing the one or more diagnostic actions, transmitting, to the server (110), a third message (M3) comprising diagnostic data resulting from performing the one or more diagnostic actions.

14. The vehicle according to claim 13, wherein the control arrangement (120) is operative to generate the diagnostic data by obtaining sensor data from the one or more sensors and processing the sensor data to diagnostic data.

15. The vehicle according to claim 13, wherein the control arrangement (120) is operative to generate the diagnostic data by presenting one or more diagnostic actions to be performed by the driver via the user interface and receiving diagnostic data from the driver via the user interface.

16. The vehicle according to any of claims 13-16, wherein the control arrangement (120) is ay one of an Electronic Control Unit, ECU communicatively coupled to the vehicle, a smartphone communicatively coupled to the vehicle, or a tablet computer communicatively coupled to the vehicle.

17. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of the claims 1 to 11 .

18. A computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to according to any of claims 1 to 11 .

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