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

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.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.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.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.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.In one embodiment according to the first aspect, the set of diagnostic processes is a subset of the one or more diagnostic processes.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.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.In one embodiment according to the first aspect, the defect report is obtained from a control arrangement of the remotely located vehicle.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.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 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.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.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”.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 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.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.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”.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.The server 110 then transmits a second message to the remotely located vehicle 123 comprising the one or more diagnostic actions.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 110.The server 110 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.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”.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.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.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.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.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”.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.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 110. 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 an EPROM (Erasable PROM), a Flash memory, an EEPROM (Electrically Erasable PROM), or a hard disk drive.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.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.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.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.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, Wi-Fi, GSM, UMTS, LTE or LTE advanced communications network or any other wired or wireless communication network known in the art.The control arrangement, CA, described herein may comprise al or a selection of the features described in relation to Fig. 3.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.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.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 (Wi-MAX) and ultrasonic communication, etc., but is not limited thereto.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.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.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

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 the server (110) comprising defect information related to the vehicle (123),receiving a second message (M2) comprising one or more diagnostic actions,generating diagnostic data by performing the one or more diagnostic actions,transmitting 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.