Abstract: An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a computing device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

Abstract: An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a remote device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data from one or more sensors of the vehicle, wherein the sensor data is based on an environment of the vehicle, identifying, based on the sensor data, one or more objects in the environment, generating, based on the one or more objects, a set of points that represent the environment, wherein each object has one or more corresponding points in the set of points, and each of the points is associated with one or more features associated with the corresponding object, generating a prediction for at least one of the objects in the environment or the vehicle by processing the set of points using a machine-learning model, and causing the vehicle to perform one or more operations based on the prediction.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data of an environment of the vehicle, the sensor data being captured by one or more sensors associated with the vehicle, generating, based on the sensor data, one or more representations of the environment of the vehicle, determining a target speed for the vehicle by processing the one or more representations of the environment of the vehicle using a machine-learning model that has been trained using human-driven vehicle speed observations and corresponding representations of environments associated with the observations, determining a trajectory plan and a planned speed for the vehicle based on at least the target speed, and causing the vehicle to perform one or more operations based on the trajectory plan and the planned speed.

Abstract: In one embodiment, a method includes accessing contextual data associated with a vehicle, the contextual data being captured using one or more sensors associated with the vehicle and including perception data, generating, based on at least a portion of the perception data, one or more representations of an environment of the vehicle, determining a predicted risk score by processing the one or more representations of the environment of the vehicle using a machine-learning model, wherein the machine-learning model has been trained using human-driven vehicle risk observations and corresponding representations of environments associated with the observations, determining that one or more vehicle operations are to be performed based on a comparison of the predicted risk score to a threshold risk score, and causing the vehicle to perform the one or more vehicle operations based on the predicted risk score and the threshold risk score.