Methods and systems for controlling navigation of a vehicle are disclosed. The system will first identify a plurality of goal points corresponding to a drivable area that a vehicle is traversing or will traverse, where the plurality of goal points are potential targets that an uncertain road user (URU) within the drivable area can use to exit the drivable area. The system will then receive perception information relating to the URU within the drivable area, and identify a target exit point from the plurality goal points based on a score. The score is computed based on the received perception information and a loss function. The system will generate a trajectory of the URU from a current position of the URU to the target exit point, and control navigation of the vehicle to avoid collision with the URU.

A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

An agricultural system includes a target vehicle configured to harvest crops and a work vehicle. The work vehicle includes a controller. The controller includes a memory and a processor, and the controller is configured to receive or determine a plurality of vehicle paths as well as a location of the target vehicle. The controller is also configured to identify an active path of the plurality of vehicle paths based on the location of the target vehicle. The target path is a path traversed by the target vehicle.

Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

A system receives confirmation that a vehicle has accepted automatic control imposition for a drive within a geo-fenced boundary. The system tracks travel of a plurality of vehicles, including the vehicle, within the geo-fenced boundary. The system may determine that the vehicle has a threshold likelihood of encountering at least one of another vehicle or a boundary of the geo-fence at a threshold speed or above and responsive to the determination, impose automatic control on the vehicle, including at least one of controlled braking or speed limiting.

According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

Provided are methods for correcting multi-zone motion blur, which include executing, using at least one processor, an alignment of at least one image capturing device with at least one collimating device in a plurality of collimating devices, causing a rotation of at least one collimating device, receiving at least one image of at least one target object captured by the image capturing device for processing by at least one rotating collimating device, and determining, based on the at least one processed image, a degradation of the received image of the target object.

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d.sub.x and a longitudinal host vehicle speed v.sub.x that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase P.sub.t and an traffic light signal timing T.sub.t; obtaining a time of day TOD; providing the host vehicle-traffic light distance d.sub.x, the longitudinal host vehicle speed v.sub.x, the traffic light signal phase P.sub.t, the traffic light signal timing T.sub.t, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.