In an example, a method captures, at a first connected vehicle situated in a travel path segment, first sensor data describing an environment proximate to the first connected vehicle. The environment includes a first unconnected vehicle. The method wirelessly receives, via a communication network at the first connected vehicle from a second connected vehicle situated in the travel path segment, second sensor data describing one or more operating characteristics of the first unconnected vehicle. The method estimates, using the first sensor data and the second sensor data, a vehicle action of the first unconnected vehicle.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

A processing device includes a path generator and a movement controller. The path generator, by using map information enabling a planned movement path of a first mobile robot to be set, sets a first scheduled occupancy space required when the first mobile robot moves in one area of the map information along a planned movement path of the mobile robot. The movement controller performs control such that the first mobile robot moves on the basis of the planned movement path of the first mobile robot. The path generator includes information of a scheduled occupancy space required when a second mobile robot moves in conditions for setting the planned movement path of the first mobile robot.

A system and method for controlling movement of transporting devices arranged to transport containers, the containers being stored in stacks arranged in a facility. A facility having pathways arranged in a grid-like structure above stacks, the transporting devices being configured to operate on the grid-like structure. A control unit configured to determine at least one task to be performed by at least one transporting device, wherein the at least one task is determined based on at least one of: transporting device battery condition, transporting device damage, transporting device maintenance issues, and transporting device service issues.

A method for zone passage control in an underground worksite having a plurality of operation zones for autonomously operating mobile vehicle operations includes the steps of receiving position information of at least one autonomously operating mobile vehicle in a fusion zone merged of at least a first zone and a second zone associated with a first passage control unit, and in response to detecting a mobile object by a second passage control unit associated with the first zone, performing: checking position of the at least one autonomously operating mobile vehicle, in response to an autonomously operating mobile vehicle being positioned in the second zone, preventing a control command to stop the autonomously operating mobile vehicle in the second zone, and demerging the first zone and the second zone.

Methods and systems for communicating between autonomous vehicles are described herein. Such communication may be performed for signaling, collision avoidance, path coordination, and/or autonomous control. A computing device may receive data for the same road segment from autonomous vehicles, including (i) an indication of a location within the road segment, and (ii) an indication of a condition of the road segment. The computing device may generate, from the data for the same road segment, an overall indication of the condition of the road segment, which may include a recommendation to vehicles approaching the road segment. Additionally, the computing device may receive a request from a computing device within a vehicle approaching the road segment to display vehicle data. The overall indication for the road segment may then be displayed on a user interface of the computing device.

A driving system includes a central controller, a plurality of driving paths forming a least crossing position, and a plurality of autonomous driving devices configured to drive along at least one of the driving paths through the crossing position. The central controller is configured to determine a plurality of target driving devices from among the autonomous driving devices that are within a certain range of the crossing position, designate one of the target driving devices as a master driving device and the other of the target driving device as slave driving devices. The master driving device controls passage of the slave driving devices through the crossing position.

A controller determines, when a traveling vehicle to proceed in a first direction from a predetermined cell toward a destination is present, whether or not to grant the traveling vehicle occupation permission for a cell adjacent to the predetermined cell in the first direction. The traveling vehicle proceeds in the first direction if occupation permission for the adjacent cell has been granted from the controller, whereas the traveling vehicle stops at the predetermined cell if occupation permission has not been granted. The controller assigns to the traveling vehicle a traveling instruction in which a cell situated at a plurality of cells ahead of the predetermined cell in the second direction is designated as a waypoint to the destination if the traveling vehicle has not obtained occupation permission for the adjacent cell and has continuously been in a stop state at the predetermined cell for a predetermined period of time.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.