Abstract: A method for determining usage of a mobile device in a vehicle by a user includes receiving a first value indicating an “off” state of a screen at a first time, operating at least one sensor of the mobile device to obtain first movement measurements, and classifying the first movements as usage movements. The method also includes generating an indication of mobile device usage, receiving a second value indicating an “on” state of the screen at a second time, operating the at least one sensor to obtain second movement measurements, and classifying the second movements as usage movements or non-usage movements. The method further includes maintaining the indication of mobile device usage in response to the second movements being classified as usage movements or changing the indication of mobile device usage to an indication of mobile device non-usage in response to the second movements being classified as non-usage movements.

Abstract: A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.

Abstract: Embodiments of the present invention relate to transportation systems. More particularly, embodiments relate to methods and systems of vehicle data collection by a user having a mobile device. In a particular embodiment, vehicle movement data (also termed herein “driving data” or “data”) is collected, analyzed and transformed, and combinations of collected data and transformed data are used in different ways, including, but not limited to, reporting and display of the combinations.

Abstract: A system for reporting accident data includes: a mobile device having a plurality of sensors; a memory; and a processor coupled to the memory. The processor is configured to perform operations including: operating the plurality of sensors to collect driving data; receiving the driving data collected over a plurality of time intervals from at least one of the plurality of sensors in the mobile device of a user during a trip in a vehicle, assigning the driving data to the trip; receiving input indicating the trip is associated with an accident, wherein the input includes additional data associated with the accident; transmitting the driving data assigned to the trip associated with the accident; and transmitting the additional data associated with the accident.

Abstract: A method of determining a position of a mobile device in a vehicle during a drive includes measuring at least one first acceleration magnitude of the mobile device in a gravity direction with at least one sensor of the mobile device, measuring at least one second acceleration magnitude of the mobile device in the gravity direction with the at least one sensor of the mobile device, the at least one second acceleration magnitude separated in time from the at least one first acceleration magnitude, comparing the at least one first acceleration magnitude with the at least one second acceleration magnitude, and based on a result of the comparing, predicting the position of the mobile device in the vehicle.

Abstract: Embodiments of the present invention utilize mobile devices to provide information on a user's behaviors during transportation. For example, a mobile device carried by a user can be used to detect and analyze driving behaviors during trips in vehicles. The mobile device can further be used to assign and display scores relating to the driving behaviors, scores for individual trips, and an overall driving score for the particular user.

Abstract: A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.

Abstract: Embodiments of the present invention relate to transportation systems. More particularly, embodiments relate to methods and systems of vehicle data collection by a user having a mobile device. In a particular embodiment, vehicle movement data (also termed herein “driving data” or “data”) is collected, analyzed and transformed, and combinations of collected data and transformed data are used in different ways, including, but not limited to, reporting and display of the combinations.

Abstract: A method of determining a position of a mobile device in a vehicle during a drive includes measuring at least one first acceleration magnitude of the mobile device in a gravity direction with at least one sensor of the mobile device, measuring at least one second acceleration magnitude of the mobile device in the gravity direction with the at least one sensor of the mobile device, the at least one second acceleration magnitude separated in time from the at least one first acceleration magnitude, comparing the at least one first acceleration magnitude with the at least one second acceleration magnitude, and based on a result of the comparing, predicting the position of the mobile device in the vehicle.

Abstract: Embodiments of the present invention provide systems and methods for combining sensor data to measure vehicle movement. Embodiments may collect vehicle and driving data using sensors of a mobile device of a user, such as a location determination sensor and a movement determination sensor. In some embodiments, the data from the sensors may be combined to more accurately estimate vehicle movement, such as vehicle acceleration.

Abstract: A system for pattern-based identification of a driver of a vehicle includes a mobile device; and a server configured to communicate with the mobile device. The mobile device collects movement information during a driving trip via one or more sensors in the mobile device, and communicates the collected movement information to a server. The server analyzes the movement information via a classifier, identifies driving features for the driver based at least in part on the analysis of the movement information, creates an identification model for the driver based on identified driving features, and stores the identification model.

Abstract: A system for reporting accident data includes: a mobile device having a plurality of sensors; a memory; and a processor coupled to the memory. The processor is configured to perform operations including: operating the plurality of sensors to collect driving data; receiving the driving data collected over a plurality of time intervals from at least one of the plurality of sensors in the mobile device of a user during a trip in a vehicle, assigning the driving data to the trip; receiving input indicating the trip is associated with an accident, wherein the input includes additional data associated with the accident; transmitting the driving data assigned to the trip associated with the accident; and transmitting the additional data associated with the accident.

Abstract: Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Abstract: Embodiments of the present invention provide systems and methods for combining sensor data to measure vehicle movement. Embodiments may collect vehicle and driving data using sensors of a mobile device of a user, such as a location determination sensor and a movement determination sensor. In some embodiments, the data from the sensors may be combined to more accurately estimate vehicle movement, such as vehicle acceleration.

Abstract: A system for pattern-based identification of a driver of a vehicle includes a mobile device; and a server configured to communicate with the mobile device. The mobile device collects movement information during a driving trip via one or more sensors in the mobile device, and communicates the collected movement information to a server. The server analyzes the movement information via a classifier, identifies driving features for the driver based at least in part on the analysis of the movement information, creates an identification model for the driver based on identified driving features, and stores the identification model.

Abstract: A method for determining usage of a mobile device in a vehicle by a user includes receiving a first value indicating an “off” state of a screen at a first time, operating at least one sensor of the mobile device to obtain first movement measurements, and classifying the first movements as usage movements. The method also includes generating an indication of mobile device usage, receiving a second value indicating an “on” state of the screen at a second time, operating the at least one sensor to obtain second movement measurements, and classifying the second movements as usage movements or non-usage movements. The method further includes maintaining the indication of mobile device usage in response to the second movements being classified as usage movements or changing the indication of mobile device usage to an indication of mobile device non-usage in response to the second movements being classified as non-usage movements.

Abstract: Methods and systems are disclosed for detecting physical interaction with an electronic device. Gyroscopic data may be received from gyroscopic sensors incorporated within the electronic device. A set of frequency bands may be defined for each axis of the plurality of axes. A frequency in which each value of the gyroscopic data is included in the gyroscopic data may be determined. The values associated with each frequency of a frequency band may be aggregated to generate a magnitude for the frequency band. A probability may be determined using the modified magnitude associated with each frequency band. If the probability exceeds a threshold then an indication that the user is physically interacting with the electronic device may be transmitted to a remote device.

Abstract: Methods and devices for identifying a driver of a vehicle are provided. Embodiments collect vehicle and driving data using a mobile device of a user. The collected data may be used to estimate whether a user of the mobile device is a driver or a passenger in a vehicle. Data collected using the mobile device may be analyzed to determine when the user is engaging in distracted driving behavior.

Abstract: Methods and systems are disclosed for detecting physical interaction with an electronic device. Gyroscopic data may be received from gyroscopic sensors incorporated within the electronic device. A set of frequency bands may be defined for each axis of the plurality of axes. A frequency in which each value of the gyroscopic data is included in the gyroscopic data may be determined. The values associated with each frequency of a frequency band may be aggregated to generate a magnitude for the frequency band. A probability may be determined using the modified magnitude associated with each frequency band. If the probability exceeds a threshold then an indication that the user is physically interacting with the electronic device may be transmitted to a remote device.

Abstract: A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.