Abstract: A computer implemented method for determining a primary movement window from a vehicle trip is presented. A client computing device may be placed in a vehicle, be free to move with respect to movement of the vehicle, and include an accelerometer. A computer network may receive a plurality of telematics data originating from the client computing device. One or more processors may select one or more data points from the telematics data, and determine that a total spectral power of the selected data points meets a threshold value. The one or more processors may identify a primary movement window including the selected data points if the total spectral power of the selected data points does not meet the threshold value.

Abstract: A computer implemented method for determining a primary movement window from a vehicle trip is presented. A client computing device may be placed in a vehicle, be free to move with respect to movement of the vehicle, and include an accelerometer. A computer network may receive a plurality of telematics data originating from the client computing device. One or more processors may summarize the plurality of telematics data at a specified sample rate, convert the plurality of telematics data from a time domain to a spectral domain, select one or more data points from the converted telematics data, and determine that a total spectral power of the selected data points meets a threshold value. The one or more processors may identify a first primary movement window including the selected data points if the total spectral power of the selected data points does not meet the threshold value.

Abstract: A computer implemented method for determining one or more idling time windows from a vehicle trip is presented. A data server may receive, via a computer network, a plurality of telematics data originating from a client computing device and identify primary movement data from the plurality of telematics data. The data server may also measure a total variance from the plurality of telematics data at one or more time stamps and determine an average total variance for an entire trip from the plurality of telematics data. The data server may further normalize total variance at the one or more time stamps using the generated average and determine one or more idling time windows from the normalized total variance.

Abstract: A system estimates the speed of a moving vehicle and hence the driving behavior of an individual driving the vehicle using accelerometer data. To do so, the system analyzes received accelerometer data to find idling points when the vehicle is not moving during a driving session. Based on the idling points, the system may divide the driving session into two or more segments. The system may then determine the speed of the vehicle at one or more boundary points of each segment. For each segment, the system may analyze the accelerometer data to determine the acceleration of the vehicle for points when the vehicle is moving. Subsequently, the system may calculate the speed of the vehicle for the points when the vehicle is moving based on the acceleration of the vehicle at the points when the vehicle is moving and the speed of the vehicle at the boundary points.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. The statistical data may be used to train a machine learning model for identifying whether a portable computing device is in a vehicle collision based on sensor data from the portable computing device. The machine learning model may be trained based on a first subset of sensor data collected from vehicle trips involved in vehicle collisions and a second subset of sensor data collected from vehicle trips not involved in vehicle collisions. When a current set of sensor data is obtained from a portable computing device in a vehicle, the current set of sensor data is compared to the machine learning model to determine whether the portable computing device is in a vehicle involved in a vehicle collision.

Abstract: A computer implemented method for determining a primary movement window from a vehicle trip is presented. A client computing device may be placed in a vehicle, be free to move with respect to movement of the vehicle, and include an accelerometer. A computer network may receive a plurality of telematics data originating from the client computing device. One or more processors may summarize the plurality of telematics data at a specified sample rate, convert the plurality of telematics data from a time domain to a spectral domain, select one or more data points from the converted telematics data, and determine that a total spectral power of the selected data points meets a threshold value. The one or more processors may identify a first primary movement window including the selected data points if the total spectral power of the selected data points does not meet the threshold value.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. Each sensor data characteristic may have a threshold value, the portable computing device may compare a value for the sensor data characteristic to the threshold value. If the portable computing device identifies a vehicle collision based on the comparison, the portable communication device may display collision information. Further, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. Each sensor data characteristic may have a threshold value, the portable computing device may compare a value for the sensor data characteristic to the threshold value. If the portable computing device identifies a vehicle collision based on the comparison, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A computer implemented method for determining a driving pattern from raw telematics data is presented. A data server may receive, via a computer network, a plurality of telematics data corresponding to a trip of a vehicle, wherein the plurality of telematics data originates from a client computing device. The data server may also identify a first primary movement window of the vehicle trip and one or more constant speed and idling windows of the vehicle trip. The data server may further estimate gravity from the telematics data in the first primary movement window of the vehicle trip and generate a pitch and a roll angle from the first primary movement window of the vehicle trip, as well as one or more yaw angle estimates from the first primary movement window of the vehicle trip. The data server may further determine a driving pattern using at least constant speed times, idling times, acceleration, breaking, vehicle turns and relate that to estimate driving risk and insurance premium.

Abstract: A method for determining a yaw angle estimate or vehicle heading direction is presented. A potential range of yaw angles is generated based on a plurality of primary telematics data. One or more yaw angle estimates are generated from the potential range of yaw angles. A driving pattern is determined based on at least one of the yaw angle estimates. The primary telematics data is a plurality of telematics data originated from a client computing device. The effects of gravity have been removed from the plurality of telematics data in a first primary movement window.

Abstract: A computer implemented method for determining a primary movement window from a vehicle trip is presented. A data server may receive a plurality of telematics data originating from a client computing device and summarize the plurality of telematics data at a specified sample rate. The data server may also select one or more data points from the plurality of telematics data and determine that the selected data points meets a threshold value. The data server may further identify a first primary movement and constant speed windows including the data points and associate the first primary movement and constant speed windows with a customer account and auto insurance risk.

Abstract: A method based on separating ambient gravitational acceleration from a moving three-axis accelerometer data for determining a driving pattern is presented. A server may receive telematics data originating from a client computing device and combine the telematics data. The server may estimate a gravitational constant to the combined telematics data and generate a function for pitch and a roll angle from the combined telematics data. The server may further determine a driving pattern using at least the pitch and the roll angle.

Abstract: A computer implemented method for determining a driving pattern from raw telematics data is presented. A data server may receive, via a computer network, a plurality of telematics data corresponding to a trip of a vehicle, wherein the plurality of telematics data originates from a client computing device. The data server may also identify a first primary movement window of the vehicle trip and one or more constant speed and idling windows of the vehicle trip. The data server may further estimate gravity from the telematics data in the first primary movement window of the vehicle trip and generate a pitch and a roll angle from the first primary movement window of the vehicle trip, as well as one or more yaw angle estimates from the first primary movement window of the vehicle trip. The data server may further determine a driving pattern using at least constant speed times, idling times, acceleration, breaking, vehicle turns and relate that to estimate driving risk and insurance premium.

Abstract: A system estimates the speed of a moving vehicle and hence the driving behavior of an individual driving the vehicle using accelerometer data. To do so, the system analyzes received accelerometer data to find idling points when the vehicle is not moving during a driving session. Based on the idling points, the system may divide the driving session into two or more segments. The system may then determine the speed of the vehicle at one or more boundary points of each segment. For each segment, the system may analyze the accelerometer data to determine the acceleration of the vehicle for points when the vehicle is moving. Subsequently, the system may calculate the speed of the vehicle for the points when the vehicle is moving based on the acceleration of the vehicle at the points when the vehicle is moving and the speed of the vehicle at the boundary points.

Abstract: A computer implemented method for determining one or more idling time windows from a vehicle trip is presented. A data server may receive, via a computer network, a plurality of telematics data originating from a client computing device and identify primary movement data from the plurality of telematics data. The data server may also measure a total variance from the plurality of telematics data at one or more time stamps and determine an average total variance for an entire trip from the plurality of telematics data. The data server may further normalize total variance at the one or more time stamps using the generated average and determine one or more idling time windows from the normalized total variance.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. The sensor data characteristics may include several threshold sample rate ranges at which another sensor data characteristic is measured and for each of the threshold sample rate ranges, a different threshold value for the other sensor data characteristic. If the portable computing device identifies a vehicle collision based on the comparison, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A computer implemented method for determining a yaw angle estimate or vehicle heading direction is presented. A data server may receive a plurality of telematics data originating from a client computing device and determine a first primary movement window from the telematics data. The data server may also determine a potential range of yaw angles from the plurality of telematics data from the first primary movement window and generate an equality that evaluates the potential range of yaw angles. The data server may further maximize the count of acceleration events of the telematics data from the first primary movement window to further generate one or more refined yaw angle estimates. The data server stores the one or more yaw angle estimates on a memory.

Abstract: A method for determining a yaw angle estimate or vehicle heading direction is presented. A potential range of yaw angles is generated based on a plurality of primary telematics data. One or more yaw angle estimates are generated from the potential range of yaw angles. A driving pattern is determined based on at least one of the yaw angle estimates. The primary telematics data is a plurality of telematics data originated from a client computing device. The effects of gravity have been removed from the plurality of telematics data in a first primary movement window.

Abstract: A method for determining a yaw angle estimate or vehicle heading direction is presented. A potential range of yaw angles is generated based on a plurality of primary telematics data. One or more yaw angle estimates are generated from the potential range of yaw angles. A driving pattern is determined based on at least one of the yaw angle estimates. The primary telematics data is a plurality of telematics data originated from a client computing device. The effects of gravity have been removed from the plurality of telematics data in a first primary movement window.

Abstract: A system estimates the speed of a moving vehicle and hence the driving behavior of an individual driving the vehicle using accelerometer data when GPS is intermittent, unavailable, or inaccurate. To do so, the system may first determine time periods when GPS data is not available. For each time period, the system may analyze the accelerometer data to find idling points when the vehicle is not moving. Based on the idling points, the system may divide each time period into segments. The system may then determine the speed of the vehicle at the boundary points of each segment. For each segment, the system may analyze the accelerometer data to determine the acceleration of the vehicle for points when the vehicle is moving. Subsequently, the system may calculate the speed of the vehicle for the points when the vehicle is moving based on the acceleration of the vehicle at those points and the speed of the vehicle at the boundary points.