A robot that is able to move about an environment determines a wait location in the environment to wait at when not otherwise in use. The wait location may be selected based on various factors including position of objects, next scheduled use, previous usage of the robot, availability of wireless connectivity, user traffic patterns, user presence, visibility of the surrounding environment, and so forth. The robot moves to that location and maintains a pose at that location, such as orienting itself to allow onboard sensors a greatest possible view of the environment. If a wait location is occupied, the robot may move to another wait location.

A data processing apparatus includes a communicator, an acquisition unit, and an output controller. The communicator is configured to receive request data from a server. The request data contains a content of a request for collection of traveling state data of a vehicle. The acquisition unit is configured to acquire the traveling state data of the vehicle on the basis of the request data received by the communicator. The output controller is configured to cause an output device to output an acquisition status of the traveling state data acquired by the acquiring unit.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

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.

Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

A method and system for providing a companion autonomous vehicle are described. In one embodiment, a method includes linking a companion autonomous vehicle to at least one vehicle, device, or user. The companion autonomous vehicle is tethered to the at least one vehicle, device, or user such that the companion autonomous vehicle is configured to stay within a predetermined range of the at least one vehicle, device, or user. The method further includes operating the companion autonomous vehicle to travel along with the tethered at least one vehicle, device, or user within the predetermined range.

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

Systems, methods, and devices for reserving a vehicle with a desired vehicle characteristic are disclosed herein. A system includes a receiver to receive a request to reserve a vehicle, wherein the request indicates a desired vehicle characteristic. The system includes a controller configured to determine a change to the vehicle that will satisfy the vehicle characteristic, and the system includes an implementation component configured to implement the change to the vehicle.

A traffic control system for an automatic driving vehicle includes a vehicle control system and a management and control system. The management and control system collects snow removal information by a snow removal information collector, and calculates traveling environment information of the snow-removed area by a snow-removed area traveling environment information calculator. The vehicle control system performs, by a first automatic driving controller, first automatic driving control that is made redundant by a control system based on map information and location information and by a control system based on external environment recognition information. The vehicle control system performs, by a second automatic driving controller, second automatic driving control that is made redundant by a control system based on the location information and the map information corrected using the traveling environment information of the snow-removed area and by a control system based on the external environment recognition information.

A model aggregation device includes a communication device able to communicate with a plurality of vehicles in which neural network models are learned, a storage device storing a part of the neural network models sent from the plurality of vehicles, and a control device. The neural network model outputs at least one output parameter from a plurality of input parameters. The control device is configured to, if receiving a new neural network model from one vehicle among the plurality of vehicles through the communication device, compare ranges of the plurality of input parameters which were used for learning the new neural network model and ranges of the plurality of input parameters which were used for learning a current neural network model stored in the storage device to thereby determine whether to replace the current neural network model with the new neural network model.