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: Historical vehicle telematics data, corresponding to trips known to have been driven by specific persons within a limited pool of potential drivers, may be processed to generate a statistical model that is customized for those persons. Once generated, the custom statistical model may be used to process vehicle telematics data from trips where it is known that the driver was one of the drivers in the pool of drivers, but the specific identify of the driver is not known. Because the statistical model is specifically optimized or designed to distinguish among the drivers in the pool, the model may be more accurate than universal driver identification models.

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 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 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 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 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: Methods and systems for improving vehicular safety by notifying vehicle operators of location-based risks are provided. According to embodiments, a processing server may receive an initial location of a vehicle. Based on location data associated with the initial location, the processing server can determine the risk of an incident. The processing server can generate a notification to communicate to the vehicle operator, and the vehicle operator can assess the risk and take action to mitigate the risk, for example by relocating the vehicle. The processing server can receive updated location data for the vehicle and can determine, based on the updated location data, that the risk has been mitigated.

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: Methods and systems for offering and providing trip-based vehicle insurance are provided. Information is received regarding a vehicle operator and a vehicle, and trip-based insurance policies including quantities of vehicle use units are offered to the customer. Based on selected coverage types, the insurance provider may generate an insurance quote for a policy having an amount of the vehicle use units and may facilitate a purchase transaction with the customer for the insurance policy. Once a policy is selected and purchased, the system and method monitor vehicle use to determine each use of a vehicle use unit. Each vehicle use unit generally corresponds to one vehicle trip, but additional vehicle trip limitations may be added that may result in additional charges when exceeded during the course of a vehicle trip.

Abstract: A computer implemented method for providing insurance comprises receiving a plurality of vehicle data including a start point, an end point and a frequency value. The method further comprises analyzing the plurality of vehicle data to determine a driving route associated with the vehicle. The method also comprises determining, based on the frequency value, that the driving route is a common driving route and a risk level of the common driving route. The method further comprises processing one or more insurance options, including pricing and underwriting, based at least in part on the risk level of the common driving route.

Abstract: A computer implemented method for providing insurance receives vehicle data that indicates one or more driving maneuvers of a vehicle, wherein the vehicle data originates from one or more sensors. The method also analyzes the received vehicle data to determine a driving pattern associated with one or more operators of the vehicle, wherein the driving pattern includes a plurality of driving maneuvers and the vehicle corresponds to an insurance policy data structure having a rating data component. The method further determines that the driving pattern indicates a change in the rating and adjusts the rating data component. The method further processes one or more insurance options based at least in part on the changed rating.

Abstract: Methods and systems for offering and providing trip-based vehicle insurance are provided. Information is received regarding a vehicle operator and a vehicle, and trip-based insurance policies including quantities of vehicle use units are offered to the customer. Based on selected coverage types, the insurance provider may generate an insurance quote for a policy having an amount of the vehicle use units and may facilitate a purchase transaction with the customer for the insurance policy. Once a policy is selected and purchased, the system and method monitor vehicle use to determine each use of a vehicle use unit. Each vehicle use unit generally corresponds to one vehicle trip, but additional vehicle trip limitations may be added that may result in additional charges when exceeded during the course of a vehicle trip.

Abstract: A mobile device for capturing motion data of a vehicle when the mobile device is travelling with the vehicle, the mobile device comprising: at least one sensor; a processor; a non-transitory storage medium; and an orientation algorithm comprising a set of computer readable instructions stored in the non-transitory storage medium and when executed by the processor configured to allow the mobile device to determine an orientation of the mobile device relative to an orientation of the vehicle; and a transformation algorithm comprising a set of computer readable instructions stored in the non-transitory storage medium and when executed by the processor configured to allow the mobile device to transform motion sensor data to remove secondary movement of the mobile device, which corresponds to the relative movement of the mobile device within the vehicle, and retain primary movement of the mobile device, which corresponds to the motion of the vehicle.