Cambridge Mobile Telematics Inc.

Truemotion, Inc.

Sanujit Sahoo

Brad Cordova

Nicholas Arcolano

Sushrut Karnik

John Emhoff

Yuting Qi

Harrison Kitchen

Burak Erem

Hari Balakrishnan

Rafi Finegold

Samuel Ross Madden

Brian Sachetta

Robert Scott Kobetis

Arash Hadapanah

Gábor Tyukász

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

According to some embodiments of the invention, movement measurements may be obtained from a movement sensor (e.g., an accelerometer) of a mobile device in a vehicle. In addition, location measurements may be obtained from a location sensor (e.g., a GPS) of the mobile device in the vehicle. The movement measurements and the location measurements may be cross-referenced to each other to remove erroneous measurements, such as physically impossible measurements. The remaining measurements may be used to draw conclusions about the movements or locations, such as to identify a movement event (e.g., a braking event, an acceleration event, or the like).

Methods and systems for combining sensor data to determine vehicle movement information

A method of providing a quaternion for reference frame transformation includes (a) initiating a driving event in a vehicle having a vehicle reference frame and (b) receiving, from a mobile device, mobile device data associated with a time stamp. The method also includes (c) computing a quaternion operable to transform the mobile device data to the vehicle reference frame and (d) determining if the driving event is complete. The method further includes incrementing the time stamp and iterating steps (a), (b), (c), and (d) if the driving event is not complete and providing the quaternion for reference frame transformation if the driving event is complete.

Methods and systems for orienting a mobile device to a vehicle's reference frame

A method for providing proactive driving alerts includes determining a road segment of interest, receiving GPS data and contextual data, and identifying a triggering condition in a vicinity of the segment of interest. The method also includes determining an applicability of the triggering condition to the segment of interest, retrieving an alert threshold corresponding to the triggering condition, and obtaining an action factor correlating to a user's driving behavior. The method further includes determining whether the action factor exceeds the alert threshold and providing a proactive alert related to the triggering condition to the user.

Method and system for driver alerts based on sensor data and contextual information

Embodiments of the present invention provide systems and methods for combining sensor data to measure vehicle movement. Embodiments may collect vehicle and driving data using sensors of a mobile device of a user, such as a location determination sensor and a movement determination sensor. In some embodiments, the data from the sensors may be combined to more accurately estimate vehicle movement, such as vehicle acceleration.

Methods and system for combining sensor data to measure vehicle movement

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 for combining sensor data to determine vehicle movement information

A system for pattern-based identification of a driver of a vehicle includes a mobile device; and a server configured to communicate with the mobile device. The mobile device collects movement information during a driving trip via one or more sensors in the mobile device, and communicates the collected movement information to a server. The server analyzes the movement information via a classifier, identifies driving features for the driver based at least in part on the analysis of the movement information, creates an identification model for the driver based on identified driving features, and stores the identification model.

Methods and systems for pattern-based identification of a driver of a vehicle

A method of determining a mode of transportation includes: collecting trip data utilizing sensors included in a mobile device during a trip; receiving contextual data related to a transportation system; forming one or more segments using the collected trip data; determining that at least one of the one or more segments is not associated with an airplane; determining that the at least one of the one or more segments is not associated with a train; determining that the at least one of the one or more segments is not associated with a bus; marking the at least one of the one or more segments as associated with a car.

Motion detection method for transportation mode analysis

A method of orienting a mobile device to a vehicle includes determining an orientation of a gravity vector and aligning a first axis of the mobile device with respect to the gravity vector. The method also includes determining an orientation of a magnetic direction and aligning a second axis of the mobile device with respect to the magnetic direction. The method further includes determining a direction of travel for the vehicle and orienting the mobile device to the vehicle.

Methods and systems for combining sensor data to measure vehicle movement

A method of determining vehicle acceleration information includes obtaining a plurality of movement measurements from a mobile device in a vehicle, and determining a plurality of gravity vectors, each of the plurality of gravity vectors associated with at least two of the plurality of movement measurements. The method further includes removing a movement measurement from the plurality of movement measurements using the plurality of gravity vectors to provide a set of remaining movement measurements. An acceleration measurement for the vehicle is determined using the remaining movement measurements.

Methods and systems for aligning a mobile device to a vehicle

A mobile-device-based system for classifying a mode of transportation during a trip includes a mobile device including a location detection system and an accelerometer. The mobile device is configured to collect location data and acceleration data during the trip. The system also includes a data processing block configured to receive the location data from the mobile device, receive contextual data related to a plurality of transportation systems, and process the location data and at least a portion of the contextual data using a first transportation mode classifier associated with a first of the plurality of transportation systems and at least a second portion of the contextual data using a second transportation mode classifier associated with a second of the plurality of transportation systems. The data processing block is also configured to classify the mode of transportation during the trip and update the classifiers based on user input.

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Cambridge, MA, United States of America

Sanujit Sahoo