Provided is a navigation system for a leader vehicle leading follower vehicles, including: the leader vehicle, configured to transmit, real-time movement data to follower vehicles; and, the follower vehicles, each comprising: a signal receiver for receiving the data from the leader vehicle; sensors configured to detect at least one maneuverability condition; a memory; a vehicle maneuver controller; a distance sensor; and a processor configured to: determine a route for navigating the local follower vehicle from an initial location; determine a preferred range of distances from the vehicle in front of the respective follower vehicle that the respective follower vehicle should stay within; determine a set of active maneuvering instructions for the respective follower vehicle based on at least a portion of the data received from the guiding vehicle; determine a lag in control commands; and, execute the set of active maneuvering instructions in the respective follower vehicle.

Systems and methods for vehicle lane change control are described. Some implementations may include determining a kinematic state of a vehicle moving in an origin lane; detecting, based on data from one or more sensors of the vehicle, objects that are moving in a target lane of the road; determining a headway constraint in terms of a preparation time, a preparation acceleration to be applied to the vehicle during the preparation time, and an execution time during which the vehicle is to transition from the origin lane to the target lane; determining values of the preparation time, the execution time, and the preparation acceleration subject to a set of constraints including the headway constraint; and determining a motion plan that will transition the vehicle from the origin lane to the target lane based at least in part on the preparation time, the execution time, and the preparation acceleration.

A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle.

A system and method that reduces spatial positioning control requirements for maintaining reduced wave-making resistance of at least one following vessel in a fleet operating in a seaway, by determining a position of the at least one following vessel within a coordinated zone, which is a zone within a reduced wave-making resistance region of the Kelvin wake of at least one lead vessel, at which a surge motion of the vessels is synchronized with each other. By positioning the at least one following vessel in the coordinated zone, the spatial positioning control requirements of the at least one following vessel can be reduced.

In a driving assistance apparatus, an image acquiring unit acquires a captured image captured by an onboard camera. Based on the captured image acquired by the image acquiring unit, a boundary line recognizing unit recognizes a boundary line that demarcates a traffic lane in which an own vehicle is driving. A road information acquiring unit acquires road information related to a road on which the own vehicle is driving. Based on the road information acquired by the road information acquiring unit, a degree-of-reliability setting unit sets a degree of reliability of the boundary line recognized by the boundary line recognizing unit. Based on the boundary line recognized by the boundary line recognizing unit, a driving assisting unit performs driving assistance of the own vehicle and varies control content of the driving assistance based on the degree of reliability.

Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.

An information processing apparatus includes an acquirer that acquires first information indicating a remaining charge of a first battery that is detachably mounted in an electric vehicle and supplies electric power for traveling of the electric vehicle and second information regarding a destination of the electric vehicle, a travel route predictor that predicts a travel route of the electric vehicle based on the second information acquired by the acquirer, and a determiner that refers to map information indicating, on a map, positions of a plurality of charging stations at which a second battery to be rented to a user is charged and determines a charging station at which the second battery is rented as a replacement for the first battery mounted in the electric vehicle based on the travel route predicted by the travel route predictor.

A localization method and system for mobile remote inspection and/or manipulation tools in confined spaces are provided. The system comprises a mobile remote inspection and/or manipulation device including a carrier movable within the confined space and an inspection and/or manipulation tool, such as an inspection camera, pose sensors arranged on the movable carrier for providing signals indicative of the position and orientation of the movable carrier, and distance sensors arranged on the movable carrier for providing signals indicative of the distance to interior surfaces of the confined space. The localization method makes use of probalistic sensor fusion of the measurement data provided by the pose sensors and the distance sensors in order to precisely determine the actual pose of the movable carrier and localize data generated by the inspection and/or manipulation tool.

In some embodiments, methods and systems are provided that provide for facilitating delivery, via autonomous ground vehicles, of products ordered by customers of a retailer to customer-specified restricted areas accessible by an entryway openable via an access code.

Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.