Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selection of ranked configurations.

Abstract: A method for traffic characterization associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a driving session associated with movement of the vehicle; extracting a set of features from the movement dataset associated with movement of the vehicle during the driving session; and determining one or more traffic-related characteristics associated with the vehicle based on the set of features.

Abstract: A method and system for detecting an accident of a vehicle, the method including: receiving a movement dataset collected at least at one of a location sensor and a motion sensor arranged within the vehicle, during a time period of movement of the vehicle, extracting a set of movement features associated with at least one of a position, a velocity, and an acceleration characterizing the movement of the vehicle during the time period, detecting a vehicular accident event from processing the set of movement features with an accident detection model, and in response to detecting the vehicular accident event, automatically initiating an accident response action.

Abstract: A method for monitoring vehicle motion using a mobile device associated with a user including collecting activity data at a mobile device associated with the user; determining a user activity state based on the activity data; determining motion data collection parameters associated with an operating state of the mobile device, based on the user activity state; and collecting motion data at the mobile device based on the motion data collection parameters.

Abstract: A method for traffic characterization associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a driving session associated with movement of the vehicle; extracting a set of features from the movement dataset associated with movement of the vehicle during the driving session; and determining one or more traffic-related characteristics associated with the vehicle based on the set of features.

Abstract: A method for primarily sensor-based navigation includes: in a first time period, collecting geophysical position data using a GPS receiver of a navigation device; in the first time period, collecting a first set of accelerometer data using an accelerometer of the navigation device; analyzing the first set of accelerometer data to produce a first set of vertical vehicular motion data; generating a mapping association between the first set of vertical vehicular motion data and the geophysical position data; in a second time period after the first time period, collecting a second set of accelerometer data using the accelerometer; analyzing the second set of accelerometer data to produce a second set of vertical vehicular motion data; and calculating an estimated location of the vehicle by analyzing the second set of vertical vehicular motion data in light of the mapping association.

Abstract: A method and system for detecting an accident of a vehicle, the method including: receiving a movement dataset collected at least at one of a location sensor and a motion sensor arranged within the vehicle, during a time period of movement of the vehicle, extracting a set of movement features associated with at least one of a position, a velocity, and an acceleration characterizing the movement of the vehicle during the time period, detecting a vehicular accident event from processing the set of movement features with an accident detection model, and in response to detecting the vehicular accident event, automatically initiating an accident response action.

Abstract: A method for monitoring vehicle motion using a mobile device associated with a user including collecting activity data at a mobile device associated with the user; determining a user activity state based on the activity data; determining motion data collection parameters associated with an operating state of the mobile device, based on the user activity state; and collecting motion data at the mobile device based on the motion data collection parameters.

Abstract: A method for improving vehicular traffic-related communications between devices including: collecting a first movement dataset corresponding to a sensor of a device arranged within a vehicle; collecting a supplementary dataset from the device; transmitting the movement dataset and supplementary dataset from the device to a remote computing system; determining a traffic-related event based upon processing the movement dataset and supplementary dataset with a traffic event model; and transmitting a traffic-related communication from the remote computing system to a second device associated with a second user arranged in a second vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selection of ranked configurations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing spoken query terms. In one aspect, a method includes performing speech recognition on an audio signal to select two or more textual, candidate transcriptions that match a spoken query term, and to establish a speech recognition confidence value for each candidate transcription, obtaining a search history for a user who spoke the spoken query term, where the search history references one or more past search queries that have been submitted by the user, generating one or more n-grams from each candidate transcription, where each n-gram is a subsequence of n phonemes, syllables, letters, characters, words or terms from a respective candidate transcription, and determining, for each n-gram, a frequency with which the n-gram occurs in the past search queries, and a weighting value that is based on the respective frequency.

Abstract: A method for primarily sensor-based navigation includes: in a first time period, collecting geophysical position data using a GPS receiver of a navigation device; in the first time period, collecting a first set of accelerometer data using an accelerometer of the navigation device; analyzing the first set of accelerometer data to produce a first set of vertical vehicular motion data; generating a mapping association between the first set of vertical vehicular motion data and the geophysical position data; in a second time period after the first time period, collecting a second set of accelerometer data using the accelerometer; analyzing the second set of accelerometer data to produce a second set of vertical vehicular motion data; and calculating an estimated location of the vehicle by analyzing the second set of vertical vehicular motion data in light of the mapping association.

Abstract: A method for traffic characterization associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a driving session associated with movement of the vehicle; extracting a set of features from the movement dataset associated with movement of the vehicle during the driving session; and determining one or more traffic-related characteristics associated with the vehicle based on the set of features.

Abstract: A method for characterizing a user associated with a vehicle including collecting a movement dataset sampled at least at one of a location sensor and a motion sensor associated with the vehicle, during a time period associated with movement of the vehicle; extracting a set of movement features associated with movement of at least one of the user and the vehicle during the time period; and determining one or more user characteristics describing the user based on the set of movement features, wherein the one or more user characteristics include a classification of the user as at least one of a passenger and a driver for the time period associated with movement of the vehicle.

Abstract: A method and system for detecting an accident of a vehicle, the method including: receiving a movement dataset collected at least at one of a location sensor and a motion sensor arranged within the vehicle, during a time period of movement of the vehicle, extracting a set of movement features associated with at least one of a position, a velocity, and an acceleration characterizing the movement of the vehicle during the time period, detecting a vehicular accident event from processing the set of movement features with an accident detection model, and in response to detecting the vehicular accident event, automatically initiating an accident response action.

Abstract: A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.

Abstract: Embodiments of a method for improving movement characteristic determination using a plurality of mobile devices associated with a vehicle can include: collecting a first movement dataset corresponding to at least one of a first location sensor and a first motion sensor of a first mobile device of the plurality of mobile devices; collecting a second movement dataset corresponding to at least one of a second location sensor and a second motion sensor of a second mobile device of the plurality of mobile devices; determining satisfaction of a device association condition indicative of the first and the second mobile devices as associated with the vehicle, based on the first and the second movement datasets; and after determining the satisfaction of the device association condition, determining a vehicle movement characteristic based on the first and the second movement datasets.

Abstract: A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.

Abstract: A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.

Abstract: A method for assessing risk through a social network includes receiving user social network data, generating a risk map from the user social network data, and calculating a risk assessment based on the risk map and the user social network data.