Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. The statistical data may be used to train a machine learning model for identifying whether a portable computing device is in a vehicle collision based on sensor data from the portable computing device. The machine learning model may be trained based on a first subset of sensor data collected from vehicle trips involved in vehicle collisions and a second subset of sensor data collected from vehicle trips not involved in vehicle collisions. When a current set of sensor data is obtained from a portable computing device in a vehicle, the current set of sensor data is compared to the machine learning model to determine whether the portable computing device is in a vehicle involved in a vehicle collision.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. Each sensor data characteristic may have a threshold value, the portable computing device may compare a value for the sensor data characteristic to the threshold value. If the portable computing device identifies a vehicle collision based on the comparison, the portable communication device may display collision information. Further, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. Each sensor data characteristic may have a threshold value, the portable computing device may compare a value for the sensor data characteristic to the threshold value. If the portable computing device identifies a vehicle collision based on the comparison, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision, where the statistical model includes sensor data characteristics which correspond to the vehicle collision. The sensor data characteristics may include several threshold sample rate ranges at which another sensor data characteristic is measured and for each of the threshold sample rate ranges, a different threshold value for the other sensor data characteristic. If the portable computing device identifies a vehicle collision based on the comparison, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.

Abstract: A method and system may identify vehicle collisions in real-time or at least near real-time based on statistical data collected from previous vehicle collisions. A user's portable computing device may obtain sensor data from sensors in the portable computing device and compare the sensor data to a statistical model indicative of a vehicle collision. If the portable computing device identifies a vehicle collision based on the comparison, notifications may be sent to emergency contacts and/or emergency personnel to provide assistance to the user.