Cambridge Mobile Telematics Inc.

Truemotion, Inc.

Yuting Qi

Sushrut Karnik

Harrison Kitchen

Rizki Syarif

Edward J Gramer

Cornelius F Young

Brad Cordova

Sanujit Sahoo

Eddie Vaisman

William Abildgaard, Jr

Ankit Singhania

Matthew Evan Lewin

Burak Erem

Edward Gelberg

Rafi Finegold

John Collins

Techniques described herein provide for methods and systems for vehicle crash prediction using multi-vehicle data. In one example, a method includes receiving event data for events identified by devices disposed in a plurality of vehicles from movements of the vehicles collected by the devices. For each pair of events, a degree of alignment is determined and used to identify candidate pairs of events. For each candidate pair of events, single event detection features are extracted and input into a crash prediction model to determine a crash correlation probability for whether both events correspond to a same crash event. Based on the probability, an indication of the same crash event is transmitted to a remote server associated with either or both vehicles involved in the crash event.

Method and system for vehicle crash prediction using multi-vehicle data

Apparatuses and methods for determining a route are provided. In some examples, the method comprises: receiving measurements of one or more motion sensors of a mobile device disposed in a vehicle; selecting, based on a start time and an end time of a first route, a subset of the measurements; determining, based on the subset of the measurements, turns or stops made by the vehicle as part of the first route and their directions; determining, based on the subset of the measurements, distances of path segments traveled by the vehicle between the turns or stops; constructing an estimated route including a sequence of the turns or stops and path segments; selecting, based on comparing the estimated route and a plurality of candidates routes from a map, the first route; and displaying the map including the first route.

Method and system for vehicle route determination based on motion data

A method for detecting airbag deployment includes operating a plurality of sensors of the mobile device disposed in a vehicle during a drive to obtain a plurality of measurement signals, determining a change in at least one measurement signal of the plurality of measurement signals and that the change exceeds a first threshold. In response to determining that the change exceeds the first threshold, obtaining a pressure measurement signal from a pressure sensor of the plurality of sensors, determining a derivative of the pressure measurement signal, and determining that the derivative of the pressure measurement signal exceeds a second threshold. In response to determining that the derivative of the pressure measurement signal exceeds the second threshold, detecting a deployment of a vehicle airbag based on the change in the at least one measurement signal exceeding the first threshold and the derivative of the pressure measurement signal exceeding the second threshold.

Systems and methods for detecting airbag deployment resulting from a vehicle crash

Techniques are disclosed for detecting a point-of-impact of a vehicle collision. A mobile device receives a set of sensor measurements from sensors of the mobile device. The mobile device generates a crash feature vector from the sensor measurements and one or more point-of-impact features. The mobile device executes a set of classifiers using the crash feature vector and the one or more point-of-impact features. Each classifier of the set of classifiers is configured to generate an output that is partially indicative of a point-of-impact of the collision. The mobile device generates, from the output of each classifier of the set of classifiers, a prediction of a point of impact of the vehicle collision. The mobile device then transmits an indication of the point of impact to a remote device.

Methods and systems for point of impact detection

A method includes receiving, from a mobile device disposed within a vehicle, a set of sensor measurements collected from an accelerometer of the mobile device during a first time period and converting the set of sensor measurements into a frequency domain. The method also includes filtering the set of sensor measurements to eliminate high frequency sensor measurements and defining a set of contiguous windows based on a remaining sensor measurements in the set of sensor measurements. Each contiguous window of the set of contiguous windows represents a contiguous portion of the remaining sensor measurements. The method further includes generating, for each contiguous window of the set of contiguous windows, a set of features by resampling the remaining sensor measurements of the contiguous window at one or more predefined frequencies and generating an estimated speed of the vehicle during the first time period using the set of features.

Method and system for vehicle speed estimation

A method includes receiving, from a first mobile device associated with a first vehicle, first data related to a first event involving the first vehicle, receiving, from a second mobile device associated with a second vehicle, second data related to a second event involving the second vehicle, and determining, based on a comparison between the first data and the second data, a degree of alignment between the first event and the second event. The method also includes determining, based on the degree of alignment between the first event and the second event, that the first event and the second event correspond to a same crash event involving the first vehicle and the second vehicle. The method further includes performing one or more actions based on determining that the first event and the second event correspond to the same crash event.

Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Methods and systems are disclosed for detecting physical interaction with an electronic device. Gyroscopic data may be received from gyroscopic sensors incorporated within the electronic device. A set of frequency bands may be defined for each axis of the plurality of axes. A frequency in which each value of the gyroscopic data is included in the gyroscopic data may be determined. The values associated with each frequency of a frequency band may be aggregated to generate a magnitude for the frequency band. A probability may be determined using the modified magnitude associated with each frequency band. If the probability exceeds a threshold then an indication that the user is physically interacting with the electronic device may be transmitted to a remote device.

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Lexington, MA, United States of America

Yuting Qi