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:
Information identifying one or more conditions associated with a vehicle is collected. This information may include first data associated with the vehicle, second data associated with a driver of the vehicle, and third data associated with an environment around the vehicle. The information may be collected from a vehicle sensor, a telematics unit, a user device associated with the driver or a passenger, or a remote server. A likelihood of a collision involving the vehicle, a likelihood of avoiding the collision, and a risk associated with the collision may be determined based on the collected data. An appropriate response may be selected based on the likelihood of the collision, the likelihood of avoiding the collision, and the risk associated with the collision.
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:
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:
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:
A wearable fall-detection device has a variety of sensors, including a pressure sensor, that provide signals for sampling environmental conditions acting on the device. An average of pressure data samples is used to determine a resultant that may indicate an amount of noise in a pressure data signal, and statistical analysis of the noise and the pressure signal average may be used to determine a confidence estimate value that indicates a level of confidence in the amount of noise that a pressure signal is subject to, or includes. The confidence estimate and known fall data, such as change in pressure between a person standing and lying, can create a threshold function that may adapt according to sampled data thus providing a customizable (either statically or dynamically) threshold function for comparing sensor data against rather than comparing data with just a linear threshold function.
Abstract:
Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.
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:
Information identifying one or more conditions associated with a vehicle is collected. This information may include first data associated with the vehicle, second data associated with a driver of the vehicle, and third data associated with an environment around the vehicle. The information may be collected from a vehicle sensor, a telematics unit, a user device associated with the driver or a passenger, or a remote server. A likelihood of a collision involving the vehicle, a likelihood of avoiding the collision, and a risk associated with the collision may be determined based on the collected data. An appropriate response may be selected based on the likelihood of the collision, the likelihood of avoiding the collision, and the risk associated with the collision.
Abstract:
Techniques described herein may be used to provide a driver of a vehicle with an accurate assessment of the remaining life of the vehicle battery. An on-board device may collect information from one or more sensors or devices within the vehicle. The information may be processed to generate a data set that accurately describes the current status and operating conditions of the battery. The data set may be used to evaluate the health of the battery and make predictions regarding the future performance of the battery, which may be communicated to the driver of the vehicle. Machine-learning techniques may be implemented to improve upon methodologies to evaluate the health of the battery and make predictions regarding battery performance.