Abstract: Methods and systems for reconstructing a route traveled by a vehicle using motion sensor data are provided. In some embodiments, a method comprises receiving motion measurements from a plurality of sensors in a vehicle during a drive. An estimated sequence of turns and distances traveled before and after each turn is extracted from the motion measurements. A start location and an end location of the drive are determined. Candidate routes between the start and end locations are selected from historical routes, each comprising a sequence of turns and distances along road segments within a road network. The candidate routes are compared with the estimated sequence to determine mismatch scores. Based on the mismatch scores, the route traveled by the vehicle is identified. An interactive graphical user interface on a mobile device receives a request to display the drive on a map and displays the identified route in response.

Abstract: A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and available values for one or more additional data types. The mobile device executes a first machine-learning model selected from a plurality of machine-learning models based on the one or more additional data types for which there are available values to generate a first confidence of a total loss event.

Abstract: Techniques are disclosed for virtual tagging of vehicles that include generating an association between a user of a mobile device and the mobile device. The techniques include receiving a first set of measurements from one or more sensors of the mobile device while the mobile device is positioned in a first vehicle during a trip and training a machine-learning model using the first set of measurements. The techniques further include receiving a second set of measurements from the one or more sensors of the mobile device and determining, by executing the machine-learning model using the third set of measurements, that the mobile device is positioned in the first vehicle or a second vehicle.

Abstract: A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and vehicle data that includes an identifier of a vehicle. The mobile device generates a second feature vector using the first set of data and additional data types. The mobile device predicts a confidence of a total loss event by generating a first confidence value from a first machine-learning model using the first feature vector and a second confidence value from a second machine-learning model using the second feature vector.

Abstract: 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.

Abstract: Apparatuses and methods for determining a route are provided. In some examples, the method comprises: receiving measurements of one or more motion sensors disposed in a vehicle during a drive; determining, based on the measurements, that a driving event involving the vehicle occurred during the drive; detecting turns or stops made by the vehicle during the drive as part of the drive and distances between the turns or stops; constructing an estimated sequence of the turns and distances traveled by the vehicle during the drive; identifying, based on comparing the estimated sequence with a plurality of candidate sequences generated from map data, a matching route; and identifying a location where the driving event occurred based on the corresponding location of the driving event in relation to the estimated sequence.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Provided are a method for assaying a specific IgM antibody and a total antibody for the 2019 novel coronavirus (2019-nCOV), a method for determining whether subjects are infected with 2019-nCOV, and a virus antigen and a kit for carrying out the above-mentioned detection.

Abstract: 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.