A digital map of a transport vehicle for transporting vehicles includes one or more digital reference locations that are respectively associated with real reference locations of the transport vehicle. A method includes: ascertaining respective reference coordinates of the real reference locations relative to a reference coordinate system, so that the ascertained reference coordinates are associated with the corresponding digital reference locations; deriving respective coordinates of further digital locations of the digital map from the reference coordinates of the reference locations based on a derivation protocol, so that coordinates relative to the reference coordinate system are respectively associated with the further digital locations, so that an edited digital map of the transport vehicle is created, in which map coordinates relative to the reference coordinate system are associated with the digital locations.

This description provides an autonomous or semi-autonomous excavation vehicle that is capable determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

A route search system for a work vehicle includes a receiver, a processor, and a controller. The receiver is to obtain a reference position of the work vehicle. The processor is to define, as a search area, an area around a vehicle reference point in a plan view. The vehicle reference point indicates the reference position of the work vehicle. The controller is to determine a guidance travel route along which the work vehicle is to travel and which is closest to the vehicle reference point in the search area among travel route candidates stored in a memory.

A driving support Electronic Control Unit (ECU) initializes a target trajectory calculation parameter at a start of Lane Change Assist Control (LCA), calculates, based on the target trajectory calculation parameter, a target trajectory function representing a target lateral position in accordance with an elapsed time from the start of LCA; and calculates a target control amount according to the target trajectory function. When it is determined that the own vehicle has crossed a boundary white line, the driving support ECU again initializes the target trajectory calculation parameter, and calculate the target trajectory function based on the target trajectory calculation parameter.

A movement route generating apparatus includes an angle calculating unit calculating an angle formed by a vehicle travel direction at a target position compared with that at the current position, a graph generating unit generating a graph that has most gentle inclinations by plotting the curvature of a travel trajectory matching the steering angle of the vehicle and a distance traveled on two axes, under the condition that the area of a graph generated in correspondence to a travel trajectory from the current position to the target position is equal to the angle and other conditions, and a route setting unit setting a travel trajectory represented by the graph as the movement route of the vehicle. Accordingly, a travel trajectory with the smallest degree of change in curvature per unit distance traveled, that is, a low horizontal angular velocity caused by vehicle steering, can be set as a movement route.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Systems and methods for curiosity development in an agent located in an uncertain environment are provided. In one embodiment, the system includes a goal state module, a curiosity module, and a planning module. The goal module is configured to calculate a goal state of a goal associated with the environment. The curiosity module is configured to determine an uncertainty value for the environment and calculate a curiosity reward based on the uncertainty value. The planning module is configured to update a motion plan based on the goal state and the curiosity reward.

Disclosed is a golf-shot-tracking-self-driving-path central controlling system, comprising a predetermined-paths-determining module, a golf-ball-next-shot-location determining module and a path driving controlling module, to centrally control each of a plurality of golf-shot-tracking fairway-self-driving golf carts to drive in one of a plurality of predetermined paths or shift among the plurality of predetermined paths.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining sensor data indicative of a surrounding environment of the autonomous vehicle, the surrounding environment including one or more occluded sensor zones. The method includes determining that a first occluded sensor zone of the occluded sensor zone(s) is occupied based at least in part on the sensor data. The method includes, in response to determining that the first occluded sensor zone is occupied, controlling the autonomous vehicle to travel clear of the first occluded sensor zone.