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 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 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:
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:
Aerial images, such as images from satellites or other aerial imaging devices, may be used to assist in responding to the occurrence of events (such as vehicle accidents or other emergency events) or conditions. In one implementation, an alert may be received indicating a condition or event associated with a user device. In response, an aerial image associated with the location of the user device may be requested. The alert may be responded to based on the received image. Aerial imaging may also provide views of the road ahead of a driver that terrain, topography, or darkness may otherwise impede. Image recognition may provide analysis of a hazard, condition, or occurrence at a scene that the aerial imaging system has captured and transmitted in response to a request.