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 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 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 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 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.