METHOD AND SYSTEM FOR RETRIEVING VEHICULAR PARAMETERS FROM A VEHICLE DATA BUS

• US 20150371457A1
• Filed: 12/23/2013
• Published: 12/24/2015
• Est. Priority Date: 12/23/2012
• Status: Active Grant

• Current Assignee: Orpak Systems Ltd. (Vontier Corp.)
• Original Assignee: Orpak Systems Ltd. (Vontier Corp.)
• Inventors: BAKFAN, Shay, MANOS, Hezi, PERI, Itay, NEULANDER, Osnat
• Granted Patent: US 9,767,624 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0841 Registering performance data recording measured values G01D; information storage G11B

Abstract

The present invention relates to a device, system and a method for obtaining and monitoring vehicular parameters and in particular, to such a device, system and method in which vehicular parameters are sniffed and automatically ascertained from a vehicle controller data bus.

Claims

1. A method for identifying required parameters of a vehicle by sniffing for vehicle bus messages transferred via an OBD port of the vehicle, the method comprising:

a. sniffing to OBD messages transferred over the OBD port;

b. obtaining a plurality of the transferred OBD messages;

c. sorting the obtained OBD messages into a plurality of groups according to the message identification (MID);

d. dividing each group of the sorted OBD messages to one or more suspected vectors wherein each suspected vector (SV) is associated with a segment of an OBD message wherein the values of the segment changes in time;

e. analyzing the changes in time of each of the SVs in order to learn the time behavior of each of the SV;

f. comparing the time behavior of the SVs to expected time behavior of at least one required parameter in order to identify an SV that represents the at least one required parameter;

g. defining a parameter identification information (PID) for the at least one identified required parameter; and

h. storing at a memory device the defined PID.

2. The method of claim 1, wherein the parameters of the vehicle are fleet-management parameters.

3. The method of claim 1, wherein the time behavior of an SV reflects changes in time of the values of that SV.

4. The method of claim 1, wherein the OBD port is an OBD connector.

5. The method of claim 1, wherein a SV comprises the entire payload of the OBD message.

6. The method of claim 1, wherein the PID of an SV comprises:

a. the MID of the OBD message that comprises the identified segment;

b. an offset from the beginning of the OBD message in which the identified segment locates; and

c. a number of bits of that segment.

7. The method of claim 2, wherein the required fleet-management parameters comprises information on the odometer value of the vehicle.

8. The method of claim 2, wherein the required fleet-management parameters comprises information on the road speed of the vehicle.

9. The method of claim 2, wherein the required fleet-management parameters comprises information on the volume of the fuel in the fuel tank.

10. The method of claim 1, wherein the action of obtaining a plurality of the transferred OBD messages further comprising adding a timestamp to each obtained OBD message.

11. The method of claim 1, wherein the action of obtaining a plurality of the transferred OBD messages further comprising organizing the obtained messages based on the time of obtaining the message.

12. The method of claim 1, wherein the action of analyzing the changes in time of each of the SVs further comprising determining whether the time behavior of that SV represents a monotonically increasing in time pattern.

13. The method of claim 1, wherein the action of analyzing the changes in time of each of the SVs further comprising determining whether the time behavior of the values of that SV represents a non-monotonic in time pattern.

14. The method of claim 1, wherein the action of analyzing the changes in time of each of the SVs further comprising determining whether the time behavior of the values of that SV represents a sawtooth pattern in time.

15. The method of claim 1, wherein the action of defining the PID for the at least one identified required parameter further comprises checking the correlation between the time behavior of an SV of the at least one identified required parameter with the time behavior of an SV of another identified required parameter.

16. The method of claim 15, wherein the at least one identified required parameter represents the odometer and the another identified required parameter represent the road speed.

17. The method of claim 1, wherein the action of defining the PID for the at least one identified required parameter further comprising checking the correlation between the time behavior of an SV of the at least one identified required parameter with an event.

18. The method of claim 17, wherein the at least one identified required parameter represents the volume of the fuel in the fuel tank and the event is located in a fuel station premises.

19. The method of claim 1, wherein the action of comparing the time behavior of the SVs further comprising repeating in a loop of few cycles from the action ‘

a’

to action ‘

f’ and

the action of defining the PID is based on evaluating the results of the comparing in each of the cycles.

49. A system for determining and analyzing vehicular parameters from a vehicle data bus characterized in that the system does note interrogate the vehicle processing unit, the system comprising a data management system (80) in communication with a vehicle bus reading (VBR) device (60) installed in a vehicle (50) wherein said VBR (60) is associated with said vehicle about said vehicle data bus (52), and wherein said VBR (60) provides for seamlessly ascertaining vehicular parameters from data communicated over said vehicle data bus (52) without interrogating the vehicle processor (54) by implementing the processor mediated method of any of claim 1 or 29.

50. The system of claim 49 wherein said data management system is a fleet management system.

51. The system of claim 49 wherein said data management system (80) communicates with said VBR (60) to define vehicular parameters of interest.

52. The system of claim 49 wherein said data management system (80) provide a-priori data that models the behavior of said vehicular parameters.

53. The system of claim 49 wherein said VBR (60) communicates to said data management system (80) any vehicular data ascertained from said vehicle data bus (52).

20. An vehicle data bus reader (VBR), comprising:

a. a common interface;

b. a shared memory device;

c. a transmitting/receiving (Tx/Rx) module;

d. an vehicle data bus interface module (VBIF) associated with an OBD port of a vehicle and is configured to sniff OBD messages transferred over an OBD port, to obtain the transferred messages, process the obtained message and deliver the processed messages to the common interface;

e. a sampling module that obtains a group of a plurality of processed messages from the common interface, sorts the obtained messages into a plurality of sub-groups according to the message identification (MID) and stores the plurality of sub-groups in the shared memory;

f. a message detector and verifier module (MDVM) that obtains a sub-group of stored messages from the shared memory, divides the obtained sub-group of the sorted messages to one or more suspected vectors wherein each suspected vector (SV) is associated with a segment of a stored message wherein the values of the segment changes in time;

analyzes the changes in time of each of the SVs in order to learn the time behavior of each of the SV;

compares the time behavior of each of the SVs to expected changes in time of at least one required parameter in order to identify an SV that represents the at least one required parameter;

defines an identification information (PID) for the at least one identified required parameter; and

stores the defined PID in a PID table;

g. a managing processor capable of creating reports based on the stored defined PID and transferring the report via the Tx/Rx.

21. The device of claim 20, wherein the parameters of the vehicle are fleet-management parameters.

22. The device of claim 20, wherein the VBIF is associated with the vehicle OBD port wires.

23. The device of claim 20, wherein the VBIF is associated with OBD port wires of the vehicle by induction that is created from the electrical current over the OBD port wires.

24. The device of claim 20, wherein the VBIF is configured to add a timestamp to each processed OBD message.

25. The device of claim 20, wherein the MDVM is configured to define the PID for the at least one identified required parameter by checking the correlation between the time behavior of an SV of the at least one identified required parameter with the time behavior of an SV of another identified required parameter.

26. The device of claim 20, wherein the MDVM is configured to define the PID for the at least one identified required parameter by checking the correlation between the time behavior of an SV of the at least one identified required parameter with an event.

27. A vehicle fleet management system comprising:

a. a fleet management server;

b. a plurality of vehicle data bus readers (VBR), each VBR is associated with a vehicle and is configured to obtain onboard diagnostic (OBD) messages by sniffing to an onboard diagnostic (OBD) port of the vehicle, automatically learn to identify one or more required parameters of the vehicles that are carried by the OBD messages, create one or more reports that are based on the values of the identified one or more required parameters, and transfer the one or more reports; and

c. one or more fuel station gateways that is configured to receive the created one or more reports from the VBR of a vehicle while the vehicle is fuelling in the fuel station, add information regarding the fuel process to the received report to create a station report and transfer the station report to the fleet management server.

28. The vehicle fleet management system of claim 27, wherein VBR automatically learn to identify one or more required parameters by comparing changes in time of values of certain segment of certain messages to expected time behavior of at least one required parameter.

29. A method for identifying vehicular parameters of a vehicle by sniffing for data communicated over a vehicle data bus (52) characterized in that said vehicular parameters are identified without interrogating the vehicle'"'"'s central processor (54), the method comprising:

a. sniffing to the vehicle data bus and recording data communicated thereon;

b. parse said recorded vehicle bus data to form a plurality of vehicular parameter candidates;

c. perform a plurality of statistical measurements for each of said candidates to define the candidates'"'"' statistical behavior;

d. filter the candidates with at least one data behavioral modeling filter, wherein said data behavioral model is modeled to reflect the statistical behavior of known vehicular parameters;

to associate each candidates with a vehicular parameter;

e. group and sort candidates according to behavioral model therein matching candidates into groups representative of vehicular parameters;

f. remove any candidate that are not matched and/or grouped to a vehicular parameter representative group;

g. perform a correlation analysis between candidates grouped into a single vehicular parameter representative group to determine a correlation coefficient;

h. score and sort candidates according to correlation analysis; and

i. select winning candidates according to said score to determine the vehicular parameter.

30. The method of claim 29 wherein said correlation analysis score is a function of the correlation coefficient.

31. The method of claim 30 wherein said winning candidate is selected based on a score wherein the threshold correlation coefficient having an absolute value of at least 0.75.

32. The method of claim 29 further comprising the step of determining the scale of said candidates based on available a-priori data of any vehicular parameter.

33. The method of claim 32 wherein said a-priori data is selected from the group consisting of initial odometer reading.

34. The method of claim 29 wherein said statistical measure is selected from the group consisting of minimum, maximum, mean, median, average, standard deviation, variance, distribution analysis, Gaussian distribution, any combination thereof.

35. The method of claim 29 wherein parsing said recorded vehicle bus data comprises applying a data structure Filters provided to extract available data structure details selected from MSB position, LSB position, reading frames, BigEndian, LittleEndian, MiddleEndian, data prefixes, bit length, number of bytes, encryption codes, data sequence order, or any combination thereof.

36. The method of claim 29 wherein said recorded vehicle bus data message is provided in the form of a message (10) having a message header (12) and payload (14), and wherein said message header comprises unique identifier in the form of a message ID (MID)

37. The method of claim 36 wherein said candidates statistical analysis is performed on aid payload.

38. The method of claim 36 wherein said candidates are associated with said unique identifier message ID.

39. The method of claim 29 further comprising a cross correlation between at least two or more candidates from different vehicular parameter groups wherein said vehicular parameter are correlated and having a known correlation function, the method comprising

j. Forming a cross correlation data set including at least two or more candidates;

k. Perform a cross-correlation analysis between said at least two or more candidates;

l. Determine a scaling factor for said at least two or more candidates based on said cross-correlation analysis.

40. The method of claim 39 further comprising obtaining a-priori data of any vehicular parameter.

41. The method of claim 40 wherein said a-priori data is selected from the group consisting of initial odometer reading.

42. The method of claim 29 wherein said winning candidate is utilized to determine the scale of a parameter by comparison to a-priori data.

43. The method of claim 29 wherein said parsing is performed on the payload portion of said recorded vehicle bus data.

44. The method of claim 29 wherein said candidates are formed from the payload portion of said recorded vehicle bus data.

45. The method of claims 29 wherein said method is preformed substantially simultaneously as data communicated over the vehicle data is made available.

46. The method of claim 29 wherein said method is performed substantially without recording intermediate data.

47. A device adapted associated directly and/or indirectly with a vehicle data bus provided to implement the processor mediated method of claim 29, the device comprising, a processor module, a communication module, data capture module, and a data modeling module, characterized in that said data modelling module provides for statistically modeling vehicular parameter candidates based on known vehicular data historical data.

48. The device of claim 47 wherein said data modeling module comprises a message modeling module provided for modeling the data structure of data sniffed from the vehicle data bus.