Method and system for vehicle analysis

1. A method for analyzing vehicle diagnosis data generated by a vehicle, the method comprising:

receiving a first collection of vehicle diagnosis data, the vehicle diagnosis data comprising a plurality of different sets of digital data representing a plurality of different vehicle components comprised with the vehicle, wherein the different sets of digital data representing the plurality of different vehicle components are generated from a CAN bus information flow;

performing a modeling of the first collection of vehicle diagnosis data by correlating at least a selected portion of the different sets of digital data comprised with the first collection of vehicle diagnosis data, wherein the modeling comprises applying a machine learning process to the selected portion of the different sets of digital data and wherein the selected portion of the different sets of digital data is identified from the first collection of vehicle diagnosis data by utilizing pattern recognition;

forming, based on the performed modeling, an indication of a status for a single one of the vehicle components comprised with the vehicle based on an identified pattern provided as an outcome of the pattern recognition, wherein the indication of the status for the single one of the vehicle components relates to an error relating to that vehicle component;

determining a validity of the status for the single one of the vehicle components; and

outputting the indication in combination with the validity of the status, wherein the validity comprises information stating if the indication is to be treated as reliable or unreliable.

2. The method according to claim 1 , wherein the validity of the status for the single one vehicle component is determined to be above a predetermined threshold.

3. The method according to claim 1 , wherein the indication of the status for the single one vehicle component relates to at least one of a short-circuit, a broken connection, and a corrosion in a connector.

4. The method according to claim 1 , wherein the machine learning process is an unsupervised machine learning process or a supervised machine learning process.

5. The method according to claim 1 , wherein the performed modeling comprises:

accessing a digital storage unit comprising a previously stored collection of training vehicle diagnosis data; and

comparing the collection of training vehicle diagnosis data with the first collection of vehicle diagnosis data.

6. The method according to claim 5 , wherein the training vehicle diagnosis data comprises at least one of:

a second collection of vehicle diagnosis data generated by a second vehicle, the second vehicle being different from the vehicle; and

an expected collection of vehicle diagnosis data.

7. The method according to claim 6 , wherein the expected collection of vehicle diagnosis data is generated from a behavioral simulation of a vehicle.

8. The method according to claim 1 , wherein the machine learning process is based on a convolutional neural network (CNN) or a recurrent neural network (RNN).

9. A vehicle analysis system for analyzing vehicle diagnosis data generated by a vehicle, the system comprising one or more processors configured to:

receive a first collection of vehicle diagnosis data, the vehicle diagnosis data comprising a plurality of different sets of digital data representing a plurality of different vehicle components comprised with the vehicle, wherein the different sets of digital data representing the plurality of different vehicle components are generated from a CAN bus information flow;

perform a modeling of the first collection of vehicle diagnosis data by correlating at least a selected portion of the different sets of data comprised with the first collection of vehicle diagnosis data, wherein the modeling comprises applying a machine learning process to the selected portion of the different sets of digital data and wherein the selected portion of the different sets of digital data is identified from the first collection of vehicle diagnosis data by utilizing pattern recognition;

form, based on the performed modeling, an indication of a status for a single one of the vehicle components comprised with the vehicle based on an identified pattern provided as an outcome of the pattern recognition, wherein the indication of the status for the single one of the vehicle components relates to an error relating to that vehicle component;

determine a validity of the status for the single one of the vehicle components; and

output the indication in combination with the validity of the status, wherein the validity comprises information stating if the indication is to be treated as reliable or unreliable.

10. The vehicle analysis system according to claim 9 , further comprising an arrangement for interfacing with and collecting the vehicle diagnosis data.

11. The vehicle analysis system according to claim 9 , further comprising a display unit comprising a graphical user interface (GUI), wherein the GUI is configured for presenting the indication of the status for the single one of the vehicle components comprised with the vehicle.

12. A computer program product comprising a non-transitory computer readable medium having stored thereon computer program means for controlling a vehicle analysis system, wherein the computer program product comprises:

code for receiving a first collection of vehicle diagnosis data, the vehicle diagnosis data comprising a plurality of different sets of digital data representing a plurality of different vehicle components comprised with the vehicle, wherein the different sets of digital data representing the plurality of different vehicle components are generated from a CAN bus information flow;

code for performing a modeling of the first collection of vehicle diagnosis data by correlating at least a selected portion of the different sets of data comprised with the first collection of vehicle diagnosis data, wherein the modeling comprises applying a machine learning process to the selected portion of the different sets of digital data and wherein the selected portion of the different sets of digital data is identified from the first collection of vehicle diagnosis data by utilizing pattern recognition;

code for forming, based on the performed modeling, an indication of a status for a single one of the vehicle components comprised with the vehicle based on an identified pattern provided as an outcome of the pattern recognition, wherein the indication of the status for the single one of the vehicle components relates to an error relating to that vehicle component;

code for determining a validity of the status for the single one of the vehicle components; and

code for outputting the indication in combination with the validity of the status, wherein the validity comprises information stating if the indication is to be treated as reliable or unreliable.

13. An arrangement for collection of vehicle diagnosis data from a vehicle, including:

at least one processor;

an interface configured for providing an electrical connection between the vehicle and the at least one processor; and

a memory for storing vehicle diagnosis data collected from the vehicle, wherein the interface is arranged to collect a plurality of analog signals from the vehicle and the arrangement further comprises an analogue-to-digital converter to allow for parallel digitizing of the plurality of analogue signals, and the arrangement is comprised with the vehicle analysis system according to claim 9 .

14. The arrangement according to claim 13 , further comprising a serializer configured to convert the plurality of digitized signals to a serial stream of data, and the serializer is a low-voltage differential signaling (LVDS) serializer.

15. The arrangement according to claim 13 , further comprising a further communication interface for providing the stored vehicle diagnosis data to a remotely arranged computing device.

16. The arrangement according to claim 13 , wherein the plurality of components comprised with the arrangement are arranged in a housing, wherein the arrangement is further adapted for use in a vehicle workshop.

17. The arrangement according to claim 13 , wherein the plurality of components comprised with the arrangement are comprised with the vehicle as an on-board arrangement.