In various examples, teleoperation architectures for safe control of machines are described. Systems and methods are disclosed that use an end-to-end safety architecture that covers both a vehicle or machine and a remote system providing a control center, where the vehicle or machine is at least partly or temporarily configured for control by the remote system. In some examples, the end-to-end architecture uses a layered safety policy monitoring system, where the remote system uses first policies to ensure that operator commands are viable and the vehicle uses second policies to ensure that the operator commands are safe to perform (e.g., will not cause collisions with other objects). Additionally, in some examples, the end-to-end architecture allows for the vehicle to perform minimum risk maneuvers, also referred to as control fallbacks, if problems were to occur.

An information processing method including: obtaining, for each of two or more autonomous mobile bodies, first information related to the autonomous mobile body; estimating, based on the first information, second information indicating a total number of two or more target autonomous mobile bodies beginning to be operated or monitored by a remote operator during a predetermined period, the two or more target autonomous mobile bodies being among the two or more autonomous mobile bodies; determining whether the total number indicated by the second information is greater than or equal to a predetermined value; and outputting at least one of third information for controlling the target autonomous mobile bodies or fourth information for providing the remote operator with notification, when the total number indicated by the second information is determined to be greater than or equal to the predetermined value.

A flight control device performs a flight control process for causing an eVTOL to fly. In a step of the flight control process, the flight control device determines whether the eVTOL is capable of maintaining a stable attitude. In the step, it is determined whether the eVTOL is capable of maintaining the stable attitude even if driving of an abnormal motor is stopped. In the step, it is determined whether the eVTOL is capable of maintaining the stable attitude even if the abnormal motor continues driving. When it is determined that the eVTOL is capable of maintaining the stable attitude, the flight control device performs output adjustment of at least one of a normal motor and the abnormal motor to maintain the eVTOL at the stable attitude.

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. More specifically, systems, devices, and methods are configured to manage a fleet of autonomous vehicles. In particular, a method may include determining destination locations for autonomous vehicles, calculating, at an autonomous vehicle service platform, delivery locations to which the autonomous vehicles are directed, identifying data to implement a delivery location associated with an autonomous vehicle, and transmitting data representing a command to the autonomous vehicle. The command may be configured to cause navigation of the autonomous vehicle to the delivery location.

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide map data for autonomous vehicles. In particular, a method may include accessing subsets of multiple types of sensor data, aligning subsets of sensor data relative to a global coordinate system based on the multiple types of sensor data to form aligned sensor data, and generating datasets of three-dimensional map data. The method further includes detecting a change in data relative to at least two datasets of the three-dimensional map data and applying the change in data to form updated three-dimensional map data. The change in data may be representative of a state change of an environment at which the sensor data is sensed. The state change of the environment may be related to the presence or absences of an object located therein.

Systems, apparatus, and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input in real-time to detect an object. In particular, a method may include determining a predicted object path of the object. The method may further include determining a predict point of impact between the autonomous vehicle and the object based in part on the predicted object path. The method may further include identifying a preferred point of impact that is associated with a safety system disposed on the autonomous vehicle. The method may further include causing the autonomous vehicle to perform a maneuver based in part on the preferred point of impact.

Systems and techniques are provided for management of autonomous cargo by autonomous vehicles (AVs). An example method can include determining, based on data from one or more sensors, a location for deploying a ramp that enables robots to enter the AV, the location comprising an area free of obstacles having one or more dimensions above a threshold; generating an instruction configured to trigger the AV to stop at the location; based on a determination that the AV is at the stopping position, deploying the ramp; sending, to the robots, a message instructing the robots to enter a cabin of the AV via the ramp and guiding each robot to a respective location within the cabin; and based on a determination that the AV has reached a destination of one or more robots, deploying the ramp and guiding the one or more robots to exit the cabin via the ramp.

A ferry controlling system for controlling a ferry in a marina where a watercraft of a user is moored and stored includes a controller and a storage. The storage stores identification information identifying the user, first location information regarding a first location where a ferry is docked/undocked, and second location information regarding a second location on the water where the watercraft of the user is moored and stored. Upon receiving the identification information, the controller is configured or programmed to allow driving of the ferry and set the second location as a destination of the ferry based on the second location information.