Accident risk model determination using autonomous vehicle operating data
US-10510123-B1 · Dec 17, 2019 · US
US11069157B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11069157-B2 |
| Application number | US-201816180681-A |
| Country | US |
| Kind code | B2 |
| Filing date | Nov 5, 2018 |
| Priority date | Mar 12, 2013 |
| Publication date | Jul 20, 2021 |
| Grant date | Jul 20, 2021 |
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A system and method for a telematic application on a mobile electronic device that can include detecting, in the telematic application, vehicular travel; collecting, in the telematic application, driver status identification data; determining a driver status based on the driver status identification data; recording the driver status; and recording telematic data based on the driver status.
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I claim: 1. A method for driver identification comprising: sampling a vehicle motion dataset during a time period corresponding to movement of a vehicle using at least one of an accelerometer and a GPS sensor of a user device arranged within the vehicle; extracting a vehicular motion signature from the vehicle motion dataset during the time period; determining a vehicle identifier of the vehicle based on a comparison between the vehicular motion signature, a characteristic vehicle motion, and movement of the user device as a user enters the vehicle that is determined using accelerometer data and GPS sensor data from the user device; and based on the vehicle identifier and the vehicle motion dataset, determining an identity of a driver operating the vehicle throughout the time period. 2. The method of claim 1 , wherein extracting the vehicular motion signature comprises extracting an acceleration pattern during the time period, and wherein the characteristic vehicle motion comprises an average acceleration rate of the vehicle. 3. The method of claim 1 , wherein extracting the vehicular motion signature comprises extracting a braking pattern during the time period, and wherein the characteristic vehicle motion comprises an anti-lock braking pattern. 4. The method of claim 1 , wherein extracting the vehicular motion signature comprises a vibration pattern during the time period, and wherein the characteristic vehicle motion comprises a suspension response pattern. 5. The method of claim 1 , wherein extracting the vehicular motion signature comprises extracting a driving route, and wherein the characteristic vehicle motion comprises a driving route pattern. 6. The method of claim 1 , wherein the vehicle motion signature comprises a combination of steering behavior, acceleration behavior, and braking behavior during the time period, and further comprising determining the identity of the driver based on the combination of steering behavior, acceleration behavior, and braking behavior. 7. The method of claim 1 , wherein the user device is movably coupled to the vehicle through an article of clothing of the driver, wherein the driver is coupled to the vehicle by a seatbelt. 8. The method of claim 1 , further comprising sampling audio data at a microphone of the user device; and determining the identity of the driver based further on the audio data. 9. The method of claim 1 , further comprising determining a relative position of the user device with respect to the vehicle, and determining the identity of the driver based further on the relative position. 10. The method of claim 9 , wherein determining the relative position comprises: at the user device, sampling a set of beacon signal data; and determining the relative position based on the set of beacon signal data. 11. A method for driver identification comprising: at a user device arranged in a vehicle, determining a relative position of the user device with respect to the vehicle during a time period corresponding to vehicular travel; based on the relative position of the user device with respect to the vehicle, determining a vehicle identifier of the vehicle; sampling a motion dataset during the time period using at least one of an accelerometer and a GPS sensor of the user device; and based on the vehicle identifier and the motion dataset, determining an identity of a driver operating the vehicle throughout the time period. 12. The method of claim 11 , further comprising removably coupling the user device to the vehicle through a mount fixed to the vehicle. 13. The method of claim 11 , further comprising sampling audio data at a microphone of the user device. 14. The method of claim 13 , wherein the audio data is emitted by the vehicle; and further comprising determining the vehicle identifier based on the set of audio data. 15. The method of claim 14 , further comprising extracting an acoustic property of the vehicle from the audio data, and determining the vehicle identifier based on the acoustic property. 16. The method of claim 13 , wherein the identity of the driver is determined based further on the audio data. 17. The method of claim 13 , further comprising determining the relative position based on the audio data. 18. The method of claim 11 , wherein determining the relative position comprises: at the user device, sampling a set of beacon signal data; and determining the relative position based on the set of beacon signal data. 19. The method of claim 11 , further comprising collecting vehicle use data during the time period, and determining the identity of the driver based on the vehicle use data. 20. The method of claim 19 , wherein the vehicle use data comprises a climate control setting determined by a vehicle occupant during the time period.
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using a mobile data collecting device, e.g. walk by or drive by · CPC title
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