Using telematics data to identify a type of a trip

• US 9,900,747 B1
• Filed: 05/16/2017
• Issued: 02/20/2018
• Est. Priority Date: 05/16/2017
• Status: Active Grant

• Current Assignee: Cambridge Mobile Telematics, Inc.
• Original Assignee: Cambridge Mobile Telematics, Inc.
• Inventors: Park, Jun-geun
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): Testardi, David A
• Application Number:
  US15/596,384
• Time in Patent Office: 280 Days
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: G01C 21/3423 Multimodal routing, i.e. combining two or more modes of transportation, where the modes can be any of, e.g. driving, walking, cycling, public transport
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G07C 5/00 Registering or indicating the working of vehicles
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/015 with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

Among other things, an operation of a computational device in identifying a type of a given trip of a person is improved. Historical information is stored about prior trips of the person or of other people or both. The historical information is based on other than recorded motion data of the trips. Features are derived about the prior trips from the historical information. Features indicative of the type of the given trip are identified by the computational device. The type of the given trip is identified based on the features derived from the historical information.

Claims

1. A method for improving operation of a computational device in classifying a vehicle mode of a given trip of a person, the vehicle mode comprising a car mode or non-car mode, the method comprising

storing in a storage device, historical information about prior car mode trips and non-car mode trips of the person or of other people or both,

by computer, deriving features about the prior trips from the historical information, the features being indicative of the car mode or non-car mode of the given trip being identified by the computational device,

storing in persistent storage a classification model representing the features,

receiving from a velocity sensor information about the given trip, the information comprising data representing periods of constant minimum speed,

receiving from a path sensor information representing straightness of a trajectory during the given trip based on noisiness in a direction orthogonal to a direction of instantaneous movement along the trajectory,

receiving from accelerometers and GPS devices, information representative of a distribution of jerk,

receiving from a location measuring device, information representing dispersion and density of location measurements,

deriving features about the given trip from the received information, and

causing the computational device to execute the classification model to classify the vehicle mode of the given trip either as a car mode or as a non-car mode based on the features derived about the given trip, without requiring the person to have identified the vehicle mode of prior trips of the person.

2. The method of claim 1 in which the stored historical information is indicative of historical patterns of the prior trips.

3. The method of claim 2 in which the historical patterns are associated with one or more of:

times of the prior trips, locations of the prior trips, or trajectories of the prior trips.

4. The method of claim 1 in which the features comprise one or more of:

the person having been a passenger in one or more of the prior trips, a locality of one or more of the prior trips, a contextual proximity of one or more of the prior trips, trip similarities of one or more of the prior trips, calendrical variations of the prior trips, or phone usage patterns.

5. The method of claim 1 in which the features comprise the dates, days-of-week, or times of the prior trips.

6. The method of claim 1 in which the features comprise labels of the prior trips provided by the person or other people or both.

7. The method of claim 1 in which the features comprise identifications of the types of the prior trips.

8. The method of claim 1 comprising classifying a role of the person in the trip.

9. The method of claim 8 in which the role of the person comprises a role as a driver or a role as a passenger.

10. The method of claim 1 in which one of the features comprises idle vibration indicative of a characteristic of a vehicle.

11. The method of claim 1 in which one of the features comprises tilting.

12. The method of claim 1 in which one of the features comprises usage of a mobile device during the given trip.

13. The method of claim 1 in which one of the features comprises straightness of a trajectory during the given trip.