Method for activating an application program, on a smartphone. This method forces the driver of a vehicle to activate and keep the application program activated while driving, since it is a telematic insurance for a vehicle that only has coverage while the smartphone is not used and a programmed speed limit is not exceeded. This method makes it difficult to activate immediately after a crash and requires a trip at a certain speed and for a time T, something very difficult in a crash scenario. It also keeps a record of the coordinates, time and date of activation and deactivation of the application program and this does not allow fraud to the insurer. This method makes it possible to ensure and prevent, without the two main causes of accidents, smartphone distraction and speeding.

Feedback for map information is based on an integrated navigation solution for a device within a moving platform using obtained motion sensor data from a sensor assembly of the device, obtained radar measurements for the platform and obtained map information for an environment encompassing the platform. An integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique that uses a nonlinear measurement model for radar measurements. The map information is assessed based at least in part on the integrated navigation solution and radar measurements so that feedback for the map information can be provided.

An aircraft includes at least one source collecting a set of navigational parameters of the aircraft, the at least one source obtaining flight data for the aircraft and including at least one of a global positioning system, an inertial reference system, or a sensor. The aircraft further includes a flight control computer communicatively coupled to the source and including a first processor and a first memory having a machine-readable medium, as well as a flight management system communicatively coupled to the flight control computer.

A system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities overtime of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.

Systems and methods relating to usage of multimodal transportation systems are disclosed. Such systems and methods may identify available local transportation modes for a user, such as vehicle-sharing, ridesharing, rental vehicles, taxicabs, owned vehicles, or public transit options. The available transportation modes may be compared, and recommendations may be presented to the user. Routes may be identified, compared, or recommended to a user, and scheduling or ticket purchasing may be facilitated. With user permission, user transportation data may be collected via a smartphone to identify user transportation patterns and preferences, thereby improving recommendations regarding and assessment of user transportation. Information regarding risks or other relevant factors associated with various transportation modes may be assessed for a user based upon typical characteristics of user transportation choices over a plurality of transportation scenarios, which may be indicated by a user transportation profile.

A wearable computing device, including a device body configured to be affixed to a body of a user. The wearable computing device may further include an inertial measurement unit (IMU) and a processor. The processor may receive kinematic data from the IMU while the device body is affixed to the body of the user. The processor may perform a first coordinate transformation on the kinematic data into a training coordinate frame of a training wearable computing device. At a first machine learning model trained using training data including training kinematic data collected at the training wearable computing device, the processor may compute a training-frame velocity estimate for the wearable computing device based on the transformed kinematic data. The processor may perform a second coordinate transformation on the training-frame velocity estimate to obtain a runtime-frame velocity estimate and may output the runtime-frame velocity estimate to a target program.

A mapping system, comprising an inertial measurement unit; a camera unit; a laser scanning unit; and a computing system in communication with the inertial measurement unit, the camera unit, and the laser scanning unit, wherein the computing system computes first measurement predictions based on inertial measurement data from the inertial measurement unit at a first frequency, second measurement predictions based on the first measurement predictions and visual measurement data from the camera unit at a second frequency and third measurement predictions based on the second measurement predictions and laser ranging data from the laser scanning unit at a third frequency.

A geoid calculation data is collected easily. A geoid calculation data collection device of the present invention comprises an inertial measurement data acquisition part, a comparison data acquisition part, and a recording part. In the inertial measurement data acquisition part, data related to velocity, position, and attitude angle is acquired as inertially-derived data based on an output of an inertial measurement part having a three-axis gyro and a three-axis accelerometer attached to a moving body. In the comparison data acquisition part, data related to velocity is acquired as comparison data from a source other than the inertial measurement part. In the recording part, inertially-derived data and comparison data are recorded in association with each other. In the inertial measurement part, a bias stability is acquired that allows error arising from plumb line deviation to be distinguished to a predetermined degree.

A method for identifying a static phase of a vehicle involves a first step of detecting the static phase without using measurements of a satellite geolocation unit, this first detection step involving checking that a first set of one or more conditions is satisfied, checking, repeatedly, that a second set of one or more conditions is satisfied, then so long as the second set of conditions is satisfied, executing a second step of detecting the static phase from the measurements of the satellite geolocation unit, so long as the second set of conditions is not satisfied, executing the first detection step and inhibiting the execution of the second detection step.

A method, system and computer program tie physical events to virtual events by analyzing motion information from a detecting device or analyzing contextual information, initiating a virtual event with a display device in response to detecting an impending change in center of mass motion of a user from analyzing the motion information or contextual information, and associating a virtual event with subsequent motion information from the detecting device.