Abstract: Vehicle collisions may be automatically detected and reported based to a call center. The collisions may be automatically detected based on a collision detection model that receives sensor data, or other data, as input, and outputs an indication of whether there is a collision. The collision detection model may be trained on historical sensor data associated with potential vehicle collisions, where the historical sensor data is labeled to indicate whether the data corresponds to an actual collision.

Abstract: An approach is provided for causing an extension of secure emergency network resources via one or more trusted point of presence. The approach involves determining a networking context, wherein the networking context initiates a request to join an extension mesh network to a currently trusted network. The approach also involves determining a target network trust level associated with the networking context, the currently trusted network, or a combination thereof. The approach further involves selecting the extension mesh network based on the target network trust level. The approach also involves initiating a joining of the extension mesh network to the currently trusted network.

Abstract: A device may receive sensor data regarding a user device; and determine, based on the sensor data, that a user of the user device is in a vehicle or driving the vehicle. When determining that the user of the user device is in the vehicle or driving the vehicle, the device may compare the sensor data, received from the user device, with a reference dataset that includes reference data associated with users being present in or driving a vehicle, or determine that a value of a measurement, received as part of the sensor data, satisfies a threshold that is related to whether the user is in the vehicle or driving the vehicle. The device may output a particular control instruction to the user device based on determining that the user is in the vehicle or is driving the vehicle.

Abstract: A device may receive sensor data regarding a user device; and determine, based on the sensor data, that a user of the user device is in a vehicle or driving the vehicle. When determining that the user of the user device is in the vehicle or driving the vehicle, the device may compare the sensor data, received from the user device, with a reference dataset that includes reference data associated with users being present in or driving a vehicle, or determine that a value of a measurement, received as part of the sensor data, satisfies a threshold that is related to whether the user is in the vehicle or driving the vehicle. The device may output a particular control instruction to the user device based on determining that the user is in the vehicle or is driving the vehicle.

Abstract: Vehicle data may be analyzed to predict potential component failures, diagnostic trouble codes (DTCs), or other mechanical failures relating to the vehicle. In one implementation the vehicle data may be received from a number of vehicles, the vehicle data including DTCs generated by on-board diagnostic (OBD) systems of the vehicles. The vehicle data may be evaluated using a predictive model to output predictions of DTCs that are likely to occur for a particular vehicle.

Abstract: A system comprise a server configured to communicate vehicle information with a vehicle transceiver of a vehicle moving along a vehicle route and communicate drone information with a drone transceiver of a drone moving along a drone route. A computing device with a memory and a processor may be configured to communicatively connect with the server, process the vehicle information and the drone information, identify a plurality of pickup locations based in part on the vehicle information and drone information, select at least one of the plurality of pickup locations based in part on a priority score associated with a travel time to or wait time for each of the plurality of pickup locations, and update the drone route based in part on the selected pickup location.

Abstract: An “in-vehicle device” may provide location information relating to one or more vehicles in which the in-vehicle device is installed. The location information may be used to track the location of vehicles in a vehicle dealership or auction facility. In one implementation, the location of vehicles may be used, during a vehicle auction, to provide prospective bidders with real-time information relating to the location of a vehicle and the estimated auction time of the vehicle. In other implementations, the in-vehicle device may provide sensor data, such as audio or acceleration data, relating to the vehicle. The sensor data may be used to detect abnormal operating conditions of the vehicle.

Abstract: The axes of an accelerometer, installed in a vehicle at an arbitrary orientation, may be realigned to the coordinate frame of the vehicle. In one implementation, a method may include determining, based on acceleration measurements from the accelerometer that likely corresponds to stopping, a dominant orientation of the accelerometer in relation to gravity, including calculating a first transformation angle and a second transformation angle as parameters to perform coordinate realignment of a coordinate frame of the accelerometer to a coordinate frame of the vehicle. The method may further include identifying, based on the acceleration measurements, an occurrence of acceleration events of the vehicle; determining, based on an analysis of the acceleration events, a third transformation angle; and storing the first, second, and third transformation angles.

Abstract: A system may determine driving information associated with a group of users, the driving information may be based on sensor information collected by at least two of a group of user devices, a first group of vehicle devices connected to a corresponding group of vehicles associated with the group of users, or a group of second vehicle devices installed in the corresponding group of vehicles. The system may determine non-driving information associated with the group of users. The system may create a driver behavior prediction model based on the driving information and the non-driving information, and may store the driver behavior prediction model. The driver behavior prediction model may permit a driver prediction to be made regarding a particular user (e.g., a user that is not necessarily included in the group of users). The driver behavior prediction may be associated with a particular geographic location.

Abstract: The embodiments disclosed are directed to systems and methods for determining the inclination of a surface beneath a vehicle. In some embodiments, a method uses an accelerometer to determine a final surface inclination value. The final surface inclination value can then be used to compensate for the acceleration of the vehicle caused by gravity. The method includes receiving accelerometer values from the accelerometer. The method also includes determining surface inclination values based on the accelerometer values. The method further includes storing the surface inclination values. And the method includes applying a digital filter to the stored surface inclination values to determine a final surface inclination value.

Abstract: A personal emergency response (PER) device may include an audio sensor that is used to enhance the operation of the PER device. The audio sensor may obtain audio data that can be used to verify whether a fall event that is detected by the PER device is a false positive signal and/or enhance the reliability of a detected fall event. In some implementations, the audio data may be used in the detection of vehicle crashes in which a user of the PER device is involved.

Abstract: Techniques described herein relate to the classification of fall events for PER (personal emergency response) devices. In one implementation, data relating to acceleration events that occurred at the PER devices may be received. The data relating to the acceleration events may be associated with indications of whether the acceleration events correspond to fall events of users of the PER devices. A classification model may be trained based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events. The classification model may be transmitted to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices.

Abstract: The axes of an accelerometer, installed in a vehicle at an arbitrary orientation, may be realigned to the coordinate frame of the vehicle. In one implementation, a method may include determining, based on acceleration measurements from the accelerometer that likely corresponds to stopping, a dominant orientation of the accelerometer in relation to gravity, including calculating a first transformation angle and a second transformation angle as parameters to perform coordinate realignment of a coordinate frame of the accelerometer to a coordinate frame of the vehicle. The method may further include identifying, based on the acceleration measurements, an occurrence of acceleration events of the vehicle; determining, based on an analysis of the acceleration events, a third transformation angle; and storing the first, second, and third transformation angles.