Electronic System for Dynamic, Quasi-Realtime Measuring and Identifying Driver Maneuvers Solely Based on Mobile Phone Telemetry, and a Corresponding Method Thereof

• US 20190102840A1
• Filed: 04/03/2018
• Published: 04/04/2019
• Est. Priority Date: 09/06/2017
• Status: Active Grant

• Current Assignee: Swiss Reinsurance Company
• Original Assignee: Swiss Reinsurance Company
• Inventors: Perl, Andri, Bongers, Sebastiaan, Bassis, Simone, Apolloni, Bruno
• Granted Patent: US 11,216,888 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 40/02 related to ambient conditions
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: G01C 21/12 executed aboard the object being navigated; Dead reckoning
• CPC Class Codes: G01S 19/52 Determining velocity
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 20/08 Payment architectures
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/02 Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0825 using optical means
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G07C 5/0858 wherein the data carrier is removable
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

An electronic, real-time system performs maneuver recognition of vehicles based on dynamically measured telematics data, particularly the sensory data of smartphone sensors, and more particularly data from the accelerometer sensor and the global positioning system (GPS) sensor and/or the gyroscope sensor of a smartphone. The axes of the smartphone may be moving independently relative to the axes of the vehicle and thus do not need to be aligned with the axes of the vehicle. Driver behaviors and operational parameters are automatically measured and discriminated, based on automatically individuated and measured driver maneuvers within various measured to vehicle trajectories, and an output signal is generated based upon derived risk measure parameters and/or crash attitude measure parameters. The system can use score-driven, especially risk-score driven, operations associated with motor vehicles or transportation modes for passengers or goods, and reliant on a dynamic, telematics-based data aggregation and dynamically measured driving maneuvers, respectively.

Claims

1. An electronic maneuver detection system in real time based on dynamically measured telematics data, wherein driver behaviors and operational parameters are measured and discriminated based on automatically individuated driver maneuvers within various measured vehicle trajectories, and wherein an output signal is generated performed based upon derived risk measure parameters and/or crash attitude measure parameters, the system comprising:

circuitry configured to

capture data from mobile telematics devices associated with a plurality of motor vehicles, the telematics devices comprising one or more wireless connections, wherein, for providing the wireless connection, each of the telematics devices acts as a wireless node within a corresponding data transmission network by antenna connections of the telematics device, wherein each of the telematics devices is an integral part of a mobile phone device and connected to a monitoring cellular mobile node application of the mobile phone device, and wherein each of the telematics devices comprises a sensor configured to capture the data as usage-based and/or user-based and/or operation-based telematics data of the motor vehicle and/or users, the telematics sensor, at a minimum, comprising an accelerometer sensor and a global positioning system (GPS) sensor and/or a gyroscope sensor;

implement a dynamic, functional aggregation-based telematics circuit to which the plurality of mobile telematics devices are connected, wherein a data link is set between the dynamic, functional aggregation-based telematics circuit transmitting at least the captured usage-based and/or user-based and/or operation-based telematics data from the mobile telematics devices to the dynamic, functional aggregation-based telematics circuit;

for driver maneuver identification, match sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data with searchable predefined measuring parameter sets of driving maneuvers by the dynamic, functional aggregation-based telematics circuit, wherein the dynamic, functional aggregation-based telematics circuit stores the searchable predefined driving maneuvers, wherein each driving maneuver is composed of a plurality of hierarchically structured operation-measuring parameter sets, wherein a specific operation or driving feature of a vehicle is separately identifiable and triggerable based on a corresponding operation parameter set, wherein each driving maneuver is composed of sequential patterns of operations or driving features by the hierarchical structure which are each extractable and measurable by the measuring telematics parameters exhibiting during the lifespan of a driving maneuver, and wherein sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data are matched with the searchable predefined measuring parameter sets of driving maneuvers by the dynamic, functional aggregation-based telematics circuit;

for pattern template identification, store searchable predefined measuring parameter pattern templates of driving maneuvers, wherein each single driving maneuver is composed of a plurality of prototypical parameter patterns, wherein an individuated time interval is measured spanning the duration of a captured maneuver pattern by the telematics circuit, wherein the measured individuated time interval is used as a time index for extracting the portions of features effectively corresponding to a selected maneuver, wherein, for each captured maneuver pattern and each operation or feature, a similarity measurement of the detected operation and/or feature profiles is performed ranging over possible trajectories used as a benchmark and highly correlated operations or features are selected; and

automatically individuate captured driver maneuvers within the various car trajectories provided by the matched searchable measuring parameter sets of driving maneuvers and the measured pattern templates of driving maneuvers, wherein, for two temporal sequences, within the captured usage-based and/or user-based and/or operation-based telematics data, a temporal-variation independent measuring parameter is measured, each time, by the dynamic, functional aggregation-based telematics circuit, wherein the dynamic, functional aggregation-based telematics circuit provides an optimal match between the two temporal sequences by varying the temporal sequences.

2. The electronic maneuver detection system in real time according to claim 1, wherein the dynamic, functional aggregation-based telematics circuit is implemented as a dynamic, time warping-based telematics circuit, wherein, for driver maneuver identification, the matching of the sequential patterns is implemented based on dynamic time warping over the searchable predefined measuring parameter sets of driving maneuvers, and wherein the dynamic, functional aggregation-based telematics circuit provides an optimal match between the two temporal sequences by non-linearly warping the temporal sequences in the time dimension, and wherein one of the two temporal sequences, which comprise two time series, is locally stretched or compressed while optimizing the measured similarity between the two temporal sequences of usage-based and/or user-based and/or operation-based telematics data.

3. The electronic maneuver detection system in real time according to claim 1, wherein the dynamic, functional aggregation- based telematics circuit is implemented as a dynamic, symbolic-aggregate-approximation-based telematics circuit or a dynamic, piecewise-aggregate-approximation-based telematics circuit, wherein, for driver maneuver identification, the matching of the sequential patterns is implemented based on dynamic, symbolic-aggregate-approximation or dynamic, piecewise-aggregate-approximation over the searchable predefined measuring parameter sets of the driving maneuvers.

4. The electronic maneuver detection system in real time according to claim 1, wherein the dynamic, functional aggregation-based telematics circuit is configured to perform machine learning based on a neural network, wherein the machine learning includes status observation and learning, wherein the status observation observes the status variables of the sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data, and wherein the learning performs a learning operation by linking at least one of the observed status variables of the sequential patterns to at least one of the searchable predefined measuring parameter sets of the driving maneuvers.

5. The electronic maneuver detection system in real time according to claim 4, wherein the learning includes reward computing and function updating, wherein the reward computing generates a reward based on at least one of the temporal sequences, wherein, for a temporal sequence within the captured usage-based and/or user-based and/or operation-based telematics data, as observed by the status observation, a temporal variation-independent reward parameter is measured, each time, by the dynamic, functional aggregation-based telematics circuit, and wherein the function updating updates a function for deciding, from the observed status variables of the sequential patterns at present, based on the reward generated by the reward computing, at least one of the searchable measuring parameter sets of driving maneuvers, and wherein the measured pattern templates of driving maneuvers provide an optimal match between the two temporal sequences.

6. The electronic maneuver detection system in real time according to claim 4, wherein the machine learning is based on a convolutional neural network or a recurrent neural network or a standard backpropagation neural network.

7. The electronic maneuver detection system in real time according to claim 1, wherein the telematics devices comprise one or more wireless or wired connections and a plurality of interfaces for connecting with at least one of a vehicle'"'"'s data transmission busses and/or a plurality of interfaces for connecting with sensors and/or measuring devices, wherein the telematics devices are connected to onboard diagnostics systems and/or in-car interactive devices and/or a monitoring cellular mobile node application.

8. The electronic maneuver detection system in real time according to claim 1, wherein the captured usage-based and/or user-based and/or operation-based telematics data are based solely on measuring data from the accelerometer sensor and the global positioning system (GPS) sensor and/or the gyroscope sensor.

9. The electronic maneuver detection system in real time according to claim 1, wherein the automatically individuated driver maneuvers, at a minimum, comprise maneuvers of harsh acceleration and/or harsh braking and/or fast lateral road entries and/or left and right overtaking actions.

10. The electronic maneuver detection system in real time according to claim 1, wherein the usage-based and/or user-based and/or operation-based telematics data are mainly captured by the telematics devices implemented as an integral part of a mobile cellular phone or OEM line-fitted telematics device (TCU) of the connected car and transmitted to the dynamic time warping based telematics circuit.

11. The electronic maneuver detection system in real time according to claim 1, wherein an accident risk of a measured driver'"'"'s behavior, based on the identified driving maneuvers, is measured, wherein historic risk causation data are captured building an accident statistics basis for measuring and/or weighting the linking of the identified driving maneuvers to a risk measure or score.

12. The electronic maneuver detection system in real time according to claim 1, wherein a driver'"'"'s risk potential and crash attitude are measured, based on the automatically individuated driver maneuvers within the measured car trajectories.

13. The electronic maneuver detection system in real time according to claim 1, wherein the risk measurements and/or crash attitude measurements are based at least on driver characteristics parameters extracted from the driver maneuvers identification of the sequential patterns of the captured telematics data.

14. The electronic maneuver detection system in real time according to claim 1, wherein the driver maneuver identification is further triggered by captured contextual measuring parameters.

15. The electronic maneuver detection system in real time according to claim 14, wherein the captured contextual measuring parameters comprise road condition measuring parameters and/or weather measuring parameters and/or daytime measuring parameters.

16. The electronic maneuver detection system in real time according to claim 1, wherein the risk measure parameters and/or crash attitude measure parameters comprise an optimum point within the contextual measuring parameter that varies among the captured contextual measuring parameters, while increasing on both sides of the point.

17. The electronic maneuver detection system in real time according to claim 1, wherein selected templates are filtered by providing template averaging by coupled filters, removing artifacts and individual singularities and establishing templates that are each specific to the single captured maneuver.

18. The electronic maneuver detection system in real time according to claim 17, wherein said coupled filters, at a minimum, comprise a Gaussian filter and/or moving average and/or median filter, wherein each template is made specific for a single maneuver by using the coupled filters.

19. The electronic maneuver detection system in real time according to claim 17, wherein the coupled filters are dynamically adapted by the system to a measured generalization threshold value, where all the different phenotypical representations of the selected reference maneuver from the stored driving maneuvers are captured.

20. The electronic maneuver detection system in real time according to claim 1, wherein captured driver maneuvers are automatically individuated within the various car trajectories provided by the matched searchable measuring parameter sets of the driving maneuvers and the measured pattern templates of driving maneuvers, wherein the system automatically discriminates between the various possible maneuvers by returning that portion of vehicle trajectory most accurately matching the same upon inputting a selected set of templates.

21. The electronic maneuver detection system in real time according to claim 1, wherein the circuitry is configured to provide measuring parameters for discriminating driver behaviors and risks measures by relating underlying crash or near-crash trajectories based on the individuated car trajectories.

22. An electronic accident causation and accident risk measuring system in real time based on dynamically measured telematics data, wherein driver behaviors and operational parameters are measured and discriminated by the system based on automatically individuated driver maneuvers within various measured vehicle trajectories, and wherein an output signal is generated based upon derived risk measure parameters and/or crash attitude measure parameters, the system comprising:

circuitry configured to

capture data from mobile telematics devices are associated with a plurality of motor vehicles, the telematics devices comprising one or more wireless or wired connections and a plurality of interfaces for connecting with at least one of a vehicle'"'"'s data transmission busses and/or a plurality of interfaces for connecting with sensors and/or measuring devices, wherein, for providing the wireless connection, the telematics device acts as a wireless node within a corresponding data transmission network by antenna connections of the telematics device, and wherein the telematics devices are connected to an onboard diagnostics system and/or an in-car interactive device and/or a monitoring cellular mobile node application, and wherein the telematics devices capture usage-based and/or user-based and/or operation-based telematics data of the motor vehicle and/or user,

implement a dynamic time warping-based telematics circuit to which the plurality of mobile telematics devices are connected, wherein a data link is set by the wireless connection between the dynamic time warping-based telematics circuit transmitting at least the captured usage-based and/or user-based and/or operation-based telematics data from the mobile telematics devices to the dynamic time warping-based telematics circuit,

for driver maneuver identification, match sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data are matched with searchable predefined measuring parameter sets of driving maneuvers by the dynamic time warping-based telematics circuit, wherein the dynamic time warping- based telematics circuit stores the searchable predefined driving maneuvers, wherein each driving maneuver is composed of a plurality of hierarchically structured operation measuring parameter sets, wherein a specific operation or driving feature of a vehicle is separately identifiable and triggerable based on a corresponding operation parameter set, wherein each driving maneuver is composed of sequential patterns of operations or driving features by the hierarchical structure, which are each extractable and measurable by the measuring telematics parameters exhibiting during a lifespan of a driving maneuver, and wherein sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data are matched with the searchable predefined measuring parameter sets of driving maneuvers by the dynamic time warping-based telematics circuit,

for pattern template identification, store searchable predefined measuring parameter pattern templates of driving maneuvers, wherein each single driver maneuver is composed of a plurality of prototypical parameter patterns, wherein a individuated time interval is measured spanning the duration of a captured maneuver pattern by the telematics circuit, the measured individuated time interval is used as a time index to extract the portions of features effectively corresponding to a selected maneuver, wherein, for each captured maneuver pattern and each operation or feature, a similarity measurement of the detected operation and/or feature profiles is performed ranging over possible trajectories used as a benchmark and highly correlated operations or features are selected, and

automatically individuate captured driver maneuvers within the various car trajectories provided by the matched searchable measuring parameter sets of driving maneuvers and the measured pattern templates of driving maneuvers, wherein, for two temporal sequences within the captured usage-based and/or user-based and/or operation-based telematics data, a temporal variation-independent measuring parameter is measured, each time, by the dynamic time warping-based telematics circuit, wherein the dynamic time warping-based telematics circuit provides an optimal match for the two temporal sequences by non-linearly warping the temporal sequences in the time dimension, and wherein one of the two temporal sequences, which comprise two time series, is locally stretched or compressed while optimizing the measured similarity between the two temporal sequences of usage-based and/or user-based and/or operation-based telematics data.

23. The accident causation and risk measuring system according to claim 22, wherein the automatically individuated driver maneuvers comprise a maneuver for harsh acceleration and/or harsh braking and/or fast lateral road entries and/or left and right overtaking action.

24. The accident causation and risk measuring system according to claim 22, wherein the usage-based and/or user-based and/or operation-based telematics data are mainly captured by the telematics devices implemented as an integral part of a mobile cellular phone or OEM line fitted telematics device (TCU) of the connected car and transmitted to the dynamic time warping-based telematics circuit.

25. The accident causation and risk measuring system according to claim 22, wherein an accident risk of a measured driver behavior based on the identified driving maneuvers is measured, wherein historic risk causation data are captured thereby building an accident statistics base for measuring and/or weighting the linking of the identified driving maneuvers to a risk measure or score.

26. The accident causation and risk measuring system according to claim 22, wherein the driver risk potential and crash are is measured based on the automatically individuated driver maneuvers within various measured car trajectories.

27. The accident causation and risk measuring system according to claim 22, wherein risk measurements and/or crash attitude measurements are based at least on driver characteristics parameters extracted from the driver maneuver identification of the sequential patterns of the captured telematics data.

28. The accident causation and risk measuring system according to claim 22, wherein driver maneuver identification is further triggered by captured contextual measuring parameters.

29. The accident causation and risk measuring system according to claim 28, wherein the captured contextual measuring parameters comprise road condition measuring parameters and/or weather measuring parameters and/or daytime measuring parameters.

30. The accident causation and risk measuring system according to claim 22, wherein the risk measure parameters and/or crash attitude measure parameters comprise a contextual optimum point of the measuring parameter that varies among the captured contextual measuring parameters, while increasing on both sides of the point.

31. The accident causation and risk measuring system according to claim 22, wherein selected templates are filtered by providing template averaging by coupled filters thus removing artifacts and individual singularities and establishing each template specific to the single captured maneuver.

32. The accident causation and risk measuring system according to claim 31, wherein said coupled filters comprise a Gaussian filter and/or moving average and/or median filter, wherein each template is made specific to a single maneuver by using the coupled filters.

33. The accident causation and risk measuring system according to claim 31, wherein the coupled filters are dynamically adapted by the system to a measured generalization threshold value, where all the different phenotypical representations of the selected reference maneuver from the stored reference maneuvers are captured.

34. The accident causation and risk measuring system according to claim 22, wherein captured driver maneuvers are automatically individuated within the various car trajectories provided by the matched searchable measuring parameter sets of driving maneuvers and the measured pattern templates of driving maneuvers, wherein the system automatically discriminates between the various possible maneuvers by returning those portions of vehicle trajectories that are most accurately matching the same upon inputting a selected set of templates.

35. The accident causation and risk measuring system according to claim 34, wherein the circuitry is configured to provide measuring parameters for discriminating driver behaviors and risks that are measured by relating underlying crash or near-crash trajectories based on the individuated car trajectories.

36. The accident causation and risk measuring system according to claim 22, wherein the system provides a first risk-transfer based on first risk transfer parameters from at least some of the motor vehicles, wherein the first risk transfer parameters are at least partially generated based upon the output signal generation, steered and/or adapted by the measured risk measure parameters and/or crash attitude measure parameters, wherein the first risk transfer systems comprise a plurality of payment transfer modules configured to receive and store first payment parameters associated with risk transfer of risk exposures of said motor vehicles for pooling their risks, and in that the automated telematics-based system provides a second risk transfer based on second risk transfer parameters from the first risk transfer to the second risk-transfer, wherein the system receives and stores second payment parameters for pooling the risks of the first risk transfer associated with risk exposures transferred in the first risk transfer,

wherein, by vehicle telematics-driven aggregation of the dynamic time warping-based telematics circuit associated with the second risk transfer system, risk-related usage-based and/or user-based telematics data are captured by the telematics devices and triggered and monitored by telematics data-based triggers in the data flow pathway of the mobile telematics devices,

wherein the dynamic time warping-based telematics circuit associated with the second risk transfer system further measures and/or generates a single or a compounded set of variable scoring parameters profiling the use and/or style and/or environmental conditions of the driving action during the operation of the motor vehicle, based upon the captured, triggered and monitored risk-related usage-based and/or user-based and/or operational telematics data,

wherein, by the dynamic time warping-based telematics circuit associated with the second risk transfer system, a shadow request is transmitted to at least one of the first risk transfer systems, connected in a decentralized manner to the dynamic time warping-based telematics circuit, over a data transmission network, wherein the shadow request comprises at least said single or a compounded set of variable scoring parameters and/or risk-relevant parameters based upon the captured, triggered and monitored risk-related usage-based and/or user-based and/or operational telematics data, and

wherein, in response to the emitted shadow request, individualized risk-transfer profiles based upon the dynamically collected single or compounded set of variable scoring parameters are transmitted from, at a minimum, one first risk transfer system to a corresponding motor vehicle and issued by a dashboard of the motor vehicle for selection by the driver of the motor vehicle, and wherein, in return for issuing an individualized risk transfer profile over said dashboard, payment-transfer parameters are transmitted from the first risk transfer system to the OEM of the OEM-linked, telematics-based system and platform.

37. The accident causation and risk measuring system according to claim 36, wherein the scores driver module of the dynamic time warping-based telematics circuit comprises a trigger for triggering and automatically selecting scores driver parameters based on a defined scores driver behavior pattern by comparing captured telematics data with the defined scores driver behavior pattern.

38. The accident causation and risk measuring system according to claim 36, wherein the scores driver module automatically captures scores risks according to the measured location or trip of the motor vehicles based on the captured telematics data (3) of the mobile telematics devices associated with the motor vehicles.

39. The accident causation and risk measuring system according to claim 36, wherein, by the dynamic time warping-based telematics circuit, the shadow request is transmitted to a corresponding first risk transfer system, if transmission of the shadow request is permitted by an access control unit implemented in the circuitry of the mobile telematics device, wherein the single or a compounded set of variable scoring parameters are generated by the second risk transfer system.

40. The accident causation and risk measuring system according to claim 39, wherein the access control unit of the mobile telematics device comprises a definable distribution table comprising a variables list with at least one first risk transfer system, wherein the transmission of the shadow request to a specific first risk transfer system depends on the definable distribution table of the access control unit of the mobile telematics device.

41. The accident causation and risk measuring system according to claim 22, wherein the mobile telematics devices are provided by a cellular mobile device.

42. The accident causation and risk measuring system according to claim 22, wherein the mobile telematics devices are provided by an OEM line fitted telematics device (TCU) of the connected car.

43. The accident causation and risk measuring system according to claim 22, wherein the mobile telematics devices are provided by a cellular mobile device and OEM line fitted telematics device (TCU) of the connected car, wherein the OEM line fitted telematics device (TCU) of the connected car communicates directly with the dynamic time warping-based telematics circuit or via a connection with the cellular mobile device.

44. The accident causation and risk measuring system according to claim 43, wherein the usage-based and/or user-based and/or operation-based telematics data of the motor vehicle and/or user are captured by the monitoring cellular mobile node application by mobile telematics devices of the cellular mobile device and/or the OEM line fitted telematics device (TCU) of the connected car.

45. The accident causation and risk measuring system according to claim 44, wherein an individualized risk transfer profile of a first risk transfer system is only issued by a display of the cellular mobile device of the motor vehicle for selection by the driver of the motor vehicle, if such issuance is permitted by an access control unit implemented in the circuitry of the accident causation and risk measuring system.

46. The accident causation and risk measuring system according to claim 22, wherein the single or compounded set of variable scoring parameters profiles the use and/or style and/or environmental condition of the driving action during operation of the motor vehicle and at least comprise scoring parameters measuring a driving score and/or a contextual score and/or a vehicle safety score.

47. The accident causation and risk measuring system according to claim 22, wherein the variable driving scoring parameter is, at a minimum, based upon a measure of driver behavior parameters comprising the automatically individuated and identified driver maneuvers.

48. The accident causation and risk measuring system according to claim 28, wherein the captured contextual measuring parameters are, at a minimum, based upon measured trip score parameters, reliant on the road condition and/or number of intersections and/or tunnels and/or elevations, and/or measured time of travel parameters, and/or measured weather parameters and/or measured location parameters, and/or measured distance driven parameters.

49. The accident causation and risk measuring system according to claim 36, wherein the first risk transfer is dynamically or partially dynamically provided based on the first risk transfer parameters from the motor vehicles by dynamic usage-based risk-measuring and risk-transfer, wherein the relevant driver maneuvers comprise measuring, at a minimum, trajectories for harsh acceleration and/or harsh braking and/or fast lateral road entries and/or left and right overtaking action and/or tailgating and/or speeding/reckless driving and/or running red lights and/or unsafe lane changes and/or wrong-way driving and/or distraction with or without connected car data and/or driving while drowsy.

50. An accident causation and accident risk measuring method in real time based on dynamically measured telematics data, wherein driver behaviors and operational parameters are measured and discriminated by an accident causation and risk measuring system based on automatically individuated driver maneuvers within various measured vehicle trajectories, and wherein an output signal is generated based upon derived risk measure parameters and/or crash attitude measure parameters, the method comprising:

associating, using circuitry, mobile telematics devices with a plurality of motor vehicles, with the telematics devices comprising one or more wireless or wired connections and a plurality of interfaces for connection with at least one of a vehicle'"'"'s data transmission buses and/or a plurality of interfaces for connection with sensors and/or measuring devices, wherein, for providing the wireless connection, the telematics device acts as a wireless node within a corresponding data transmission network by antenna connections of the telematics device, and wherein the telematics devices are connected to an onboard diagnostic system and/or an in-car interactive device and/or a monitoring cellular mobile node application, and wherein the telematics devices capture usage-based and/or user-based and/or operation-based telematics data of the motor vehicle and/or user,

connecting, using the circuitry, the plurality of mobile telematics devices associated with the motor vehicles to a dynamic time warping-based telematics circuit implemented in the circuitry, wherein a data link is created by the wireless connection between the dynamic time warping based telematics circuit transmitting at least the captured usage-based and/or user-based and/or operation-based telematics data from the mobile telematics devices to the dynamic time warping-based telematics circuit,

matching, using the circuitry, for driver maneuvers identification, sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data with searchable predefined measuring parameter sets of driving maneuvers by the dynamic time warping-based telematics circuit, wherein the dynamic time warping-based telematics circuit stores the searchable predefined driving maneuvers, wherein each driving maneuver is composed of a plurality of hierarchically structured operation measuring parameter sets, wherein a specific operation or driving feature of a vehicle is separately identifiable and triggerable based on a corresponding operation parameter set, wherein each driving maneuver is composed of sequential patterns of operations or driving features by the hierarchical structure, which are each extractable and measurable by the measuring telematics parameters exhibiting during the lifespan of a driving maneuver, and wherein sequential patterns of the captured usage-based and/or user-based and/or operation-based telematics data are matched with the searchable predefined measuring parameter sets of driving maneuvers by the dynamic time warping-based telematics circuit,

storing, for pattern template identification, in the dynamic time warping-based telematics circuit, searchable predefined measuring parameter pattern templates of driving maneuvers, wherein each single driver maneuver is composed of a plurality of prototypical parameter patterns, wherein an individuated time interval is measured spanning the duration of a captured maneuver pattern by the telematics circuit, the measured individuated time interval is used as a time index to extract the portions of features effectively corresponding to a selected maneuver, wherein for each captured maneuver pattern and each operation or feature, a similarity measurement of the detected operation and/or feature profiles is performed ranging over possible trajectories and used as a benchmark and highly correlated operations or features are selected, and

automatically individuating, using the circuitry, captured driver maneuvers within the various car trajectories provided by the matched searchable measuring parameter sets of driving maneuvers and the measured pattern templates of driving maneuvers, wherein, for two temporal sequences within the captured usage-based and/or user-based and/or operation-based telematics data, a temporal-variation independent measuring parameter is measured, each time, by the dynamic time warping based telematics circuit, wherein the dynamic time warping based telematics circuit provides an optimal match between the two temporal sequences by non-linearly warping the temporal sequences in the time dimension, and wherein one of the two temporal sequences, which comprise two time series, is locally stretched or compressed while optimizing the measured similarity between the two temporal sequences of usage-based and/or user-based and/or operation-based telematics data.