A self-propelled floor processing device with an evaluation unit, which is designed to navigate the floor processing device within an environment based on an area map and, during a movement, to determine a behavior parameter of the floor processing device and a movement path of the floor processing device. The evaluation unit is set up to analyze the behavior parameter and movement path, automatically determine a no-go area which the floor processing device must not traverse depending on the result of the analysis, and enter the determined no-go area in the area map or change a no-go area already entered in the area map.

An on-board device for a vehicle includes an unavailability location determination unit configured to determine whether an area around where the vehicle is traveling is an autonomous driving unavailable location, and a transmission control unit configured to cause the on-board device to transmit to a server a result of determination made by the unavailability location determination unit.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Systems, methods, and computer-readable media are provided for detecting a pavement marking around an autonomous vehicle, comparing the detected pavement marking with a pavement marking present in a semantic data map, determining whether a change has occurred between the detected pavement marking and the pavement marking present in the semantic data map, and updating the semantic data map based on the determining of whether the change has occurred between the detected pavement marking and the pavement marking present in the semantic data map.

A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

A method for path planning, an automatic gardening device, and a computer program product are provided. The method includes: receiving a preset travel direction in a work region; dividing the work region into a plurality of subregions; determining, for each of the subregions, an actual planned direction in the subregion from the preset travel direction and a recommended planned direction in the subregion, and determining a local planned path corresponding to the subregion based on the actual planned direction, wherein a path of traversing the subregion along the recommended planned direction has a shortest length; acquiring a moving sequence between the subregions; and determining a global planned path of the work region based on the local planned path of each of the subregions and the moving sequence between the subregions.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include inputting data into a model and receiving an output from the model representing a discretized representation. The discretized representation may be associated with a probability of an object reaching a location in the environment at a future time. A vehicle computing system may determine a trajectory and a weight associated with the trajectory using the discretized representation and the probability. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory and the weight output by the vehicle computing system.

This disclosure is directed to calibrating sensors mounted on an autonomous vehicle. A dense depth map can be generated in a two-dimensional camera space using point cloud data generated by one of the sensors. Image data from another of the sensors can be compared to the dense depth map in the two-dimensional camera space. Differences determined by the comparison can indicate alignment errors between the sensors. Calibration data associated with the errors can be determined and used to calibrate the sensors without the need for calibration infrastructure.

A hybrid modular storage fetching system with a robot execution system (REX) is described. In an example implementation, a REX may induct, into the hybrid modular storage fetching system, an order identifying items to be fulfilled by automated guided vehicles (AGVs) at an order fulfillment facility. The REX may generate at task list including tasks for a first and second AGV, instruct the first AGV to retrieve a first item in the order from a first storage area based on the task list and deliver the first item to a pick-cell station. The REX may also instruct the second AGV to retrieve a second item of the order from a second storage area and deliver the second item to the pick-cell station. The REX may communicate with other components of the hybrid modular storage fetching system to coordinate the paths of the AGVs to fulfill the order.

Embodiments of the present disclosure relate generally to generating and utilizing three-dimensional terrain maps for vehicular control. Other embodiments may be described and/or claimed.