Abstract: Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

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 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: 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 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 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 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: Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

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 method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include metrics indicative of different driving behaviors and/or different features of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis that includes executing a clustering algorithm may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.

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: Systems and methods for real-time detection and mitigation anomalous behavior of a remote vehicle are provided, e.g., vehicle behavior that is consistent with distracted or unexpectedly disabled driving. On-board and off-board sensors associated with a subject vehicle may monitor the subject vehicle's environment, and behavior characteristics of a remote vehicle operating within the subject vehicle's environment may be determined based upon collected sensor data. The remote vehicle's behavior characteristics may be utilized to detect or determine the presence of anomalous behavior, which may be anomalous for the current contextual conditions of the vehicles' environment. Mitigating actions for detected remote vehicle anomalous behaviors may be suggested and/or automatically implemented at the subject vehicle and/or at proximate vehicles to avoid or reduce the risk of accidents, injury, or death resulting from the anomalous behavior. In some situations, authorities may be notified.

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: 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 method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include one or more metrics each indicative of a different driving behavior or a different feature of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.

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 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.