Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A user's credit score is determined based at least in part upon collected telematics data and user data associated with the user. A credit score prediction model is created using training datasets of historical data including demographics data, driver profile data, and established credit scores. The credit score prediction model is then used to predict credit scores for users without formal credit scores. A user's credit score is predicted based at least in part upon a driver profile built using telematics data, among other factors.

A user's credit score is determined based at least in part upon collected telematics data and user data associated with the user. A credit score prediction model is created using training datasets of historical data including demographics data, driver profile data, and established credit scores. The credit score prediction model is then used to predict credit scores for users without formal credit scores. A user's credit score is predicted based at least in part upon a driver profile built using telematics data, among other factors.

A method for calculating an anchoring area of a ship includes steps of obtaining ship trajectories of ships within a certain time period in an anchorage; screening out ship trajectories including an anchoring process and eliminating trajectory points in a non-anchored state in the ship trajectories to obtain anchoring trajectories of anchored ships; clustering anchoring points in each of the anchoring trajectories, using a cluster center as an anchoring position point of each of the anchored ships; establishing an anchoring data set according to the anchoring position points; selecting anchoring data records in a predetermined time period in the anchoring data set; establishing an anchored ship position point set corresponding to the predetermined time period; and establishing Thiessen polygons corresponding to the anchoring position points; calculating an area of each of the Thiessen polygons to obtain an anchoring area of a corresponding anchored ship.

A method for calculating an anchoring area of a ship includes steps of obtaining ship trajectories of ships within a certain time period in an anchorage; screening out ship trajectories including an anchoring process and eliminating trajectory points in a non-anchored state in the ship trajectories to obtain anchoring trajectories of anchored ships; clustering anchoring points in each of the anchoring trajectories, using a cluster center as an anchoring position point of each of the anchored ships; establishing an anchoring data set according to the anchoring position points; selecting anchoring data records in a predetermined time period in the anchoring data set; establishing an anchored ship position point set corresponding to the predetermined time period; and establishing Thiessen polygons corresponding to the anchoring position points; calculating an area of each of the Thiessen polygons to obtain an anchoring area of a corresponding anchored ship.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

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.

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.

An unmanned aerial vehicle includes one or more magnetometers, configured to detect a magnetic field and to output magnetometer data corresponding to a magnitude of the detected magnetic field; a position sensor, configured to detect a position of the unmanned aerial vehicle relative to one or more reference points, and to output position sensor data representing the detected position; one or more processors, configured to control the unmanned aerial vehicle to rotate about its z-axis; receive magnetometer data comprising a plurality of z-axis directional measurements taken during the rotation about the z-axis; receive position sensor data and determine from at least the position sensor data a magnetic field inclination of the detected position; and determine a z-axis magnetometer correction value as a difference between the received magnetometer data for the z-axis and the determined magnetic field inclination.