Abstract: Disclosed herein are systems, devices, and methods of a safety system for analyzing and improving the safety of environments that may be shared between robots and humans. The safety system may receive a safety envelope that includes a reachable set of locations at an expected position of an object at a prediction time. The receiver may also receive a perception prediction that may be based on the safety envelope and may include environmental information associated with the object at the expected position at the prediction time. The safety system may also include a processor that generates an instruction to move a robot according to a safe movement instruction based on whether a perception check exceeds a predetermined threshold, wherein the perception check may be based on a difference between the perception prediction and sensor information indicative of the environment of the object at the prediction time.

Abstract: A server may include an interface that is configured to receive sensor data related to a cyber-physical system (CPS); and a processor. The processor may be configured to determine a performance parameter of the CPS from the sensor data; select a resource allocation strategy based on the performance parameter; execute an algorithmic process on at least a portion of the sensor data according to the resource allocation strategy; and control a transmitter to send data representing an output of the executed artificial neural network to the robot.

Abstract: Systems and techniques for controlling a mobile robot. In an example, the system may include a processor and memory, including instructions, which when executed by the processor, cause the processor to determine a state of the mobile robot. The state of the mobile robot may be determined using at least one of: data provided by the mobile robot or data captured by one or more sensors proximate to or attached to the mobile robot. The system may determine a state of an object proximate to the mobile robot using the data captured by the one or more sensors, and identify information relating to one or more available stopping points. The system may identify a condition of the mobile robot that requires the mobile robot to stop and issue a command to the mobile robot to navigate to a particular one of the one or more available stopping points.

Abstract: Devices and methods for determining an action in the presence of road users are provided in this disclosure. A device may include a processor. The processor may be configured to access environment information including an indication of a size of road users intersecting with a predetermined route of a vehicle in a road environment. The processor may further be configured to prioritize an anticipated movement of at least one of the road users over a predicted movement of the vehicle within the predetermined route based on the size of road users. The processor may further be configured to determine a vehicle action allowing the anticipated movement of the at least one road user.

Abstract: Techniques are disclosed for the exploration of environments for the estimation and detection of hazards or near hazards within the environment and the mitigation of hazards therein. The exploration of the environment and mitigation of hazards therein may use one or more autonomous agents, including a hazard response robot. The estimation of the hazards may use a policy learning engine, and the hazards may be detected, and the associated risks therefrom, may be determined using a hazard estimation system.

Abstract: Systems and techniques for reliable real-time deployment of robot safety updates are described herein. Condition data may be collected for an environment in which a robot is operating. The robot may be classified with a condition type. Condition type data selected from the condition data may be analyzed based on the condition type to calculate a safety risk level for the robot. A microservice may be identified to provide a robot safety rule for the robot based on the safety risk level. The microservice may be identified using a safety prediction model generated from the condition type data.

Abstract: Various systems and methods for detecting risk conditions in a physical workspace. An apparatus can include an interface to receive smart sensor signals from at least one autonomous mobile entity (AME) in the physical workspace. The apparatus can also include processing circuitry coupled to the interface to detect a risk condition associated with the at least one AME, based on the smart sensor signals, relative to a user device associated with a human present in the physical workspace. The processing circuitry can also detect a direction of the risk condition relative to the user device and cause a notification to the first user device. The notification can indicate the direction of the risk condition relative to the user device. Other systems, methods and apparatuses are described.

Abstract: Techniques are disclosed to facilitate path planning safety guiding robots (SGR) for safely navigating and guiding humans through autonomous environments having other autonomous agents such as stationary and/or mobile robots.

Abstract: Disclosed herein is a vehicle handover system that monitors an environment of a vehicle. The vehicle handover system receives a transition request to change control of the vehicle from an automated driving mode to a passenger of the vehicle. The vehicle handover system detects a key event that may be relevant to the transition request and the detection of the key event is based on the monitored environment. The vehicle handover system may generate a handover scene that includes images associated with the key event, and the images include an image sequence over a time-period of the key event. Before the vehicle handover system changes control of the vehicle from the automated driving mode to the passenger, the handover scene is displayed to the passenger.

Abstract: Disclosed herein are systems, devices, and methods of a safety system for monitoring the in-vehicle safety of internal objects within a vehicle. The safety system generates a digital twin of the interior objects from vehicle configuration data indicating a configuration of an interior environment of the vehicle, from interior object data associated with the interior object within the interior environment of the vehicle, and from vehicle situation data that indicates an operating status of the vehicle. The digital twin is an abstract model of the interior objects within the interior environment of the vehicle, and the safety system generates, based on the digital twin, a safety score associated with an operating behavior of the vehicle. Based on the safety score, the safety system determines a target level for the operating behavior.

Abstract: Disclosed herein are systems, devices, and methods of a safety system for analyzing and improving the safety of collaborative environments in which a robot may interact a human. The safety system may determine a monitored attribute of a person within an operating environment of a robot, where the monitored attribute may be based on received sensor information about the person in the operating environment. In addition, the safety system may determine a risk score for the person based on the monitored attribute. The risk score may be defined by (1) a collision probability that the person will cause an interference during a planned operation of the robot and (2) a severity level associated with the interference. The safety system may also generate a mitigating instruction for the robot if the risk score exceeds a threshold level.

Abstract: Disclosed herein is a passenger monitoring system for monitoring an observed attribute of a passenger in a vehicle. The observed attribute may include a gaze of the passenger, a head track of the passenger, and other observations about the passenger in the vehicle. Based on the observed attribute(s), a field of view of the passenger may be determined. Based on the field of view, a focus point of the passenger may be determined, where the focus point is estimated to be within the field of view. If a sign (e.g., a road sign, a billboard, etc.) is within the field of view of the passenger, record an attention score for the sign based on a duration of time during which the sign is within the field of view and estimated to be the focus point of the passenger.

Abstract: Disclosed herein is an active cyclist and/or pedestrian safety system for identifying, warning, and reacting to dangerous situations that may be experienced by cyclists and/or pedestrians. The safety system may receive first information indicative of a head position of a cyclist and determine, based on the first information, a field of view of the cyclist. The safety system may receive second information indicative of an environment of the cyclist and determine, based on the field of view and the environment, an expected trajectory of the cyclist in a next road segment. The safety system may also determine, based on the field of view and the environment, a risk probability for the expected trajectory of the cyclist in the next road segment. The safety system may also generate an instruction to transmit a warning to the cyclist if the risk probability exceeds a threshold value.

Abstract: A control device includes a memory device storing, for each storage box of a plurality of storage boxes, respectively associated first box information indicating which multiple objects are arranged in the storage box and in which arrangement the multiple objects are in the storage box; and a processor configured to: receive identification data representing an identification of a storage box; determine the first box information associated with the storage box using the identification data; receive sensor data representing an image of the multiple objects arranged in the storage box; determine, via object recognition using the sensor data, second box information indicating which objects are arranged in the storage box; generate control instructions for controlling the robotic device to pick up at least one object of the multiple objects based on the first box information associated with the storage box and the second box information.

Abstract: A server includes a memory on which a map is stored, wherein the map represents locations of a plurality of mobile units; and a processor, configured to generate an emergency response plan based on sensor data and the map, wherein the emergency response plan comprises actions to be taken by a plurality of robots within a vicinity of the mobile units; and instruct a transceiver to send a signal representing the emergency response plan.

Abstract: Disclosed herein is a vehicle handover system that monitors an environment of a vehicle. The vehicle handover system receives a transition request to change control of the vehicle from an automated driving mode to a passenger of the vehicle. The vehicle handover system detects a key event that may be relevant to the transition request and the detection of the key event is based on the monitored environment. The vehicle handover system may generate a handover scene that includes images associated with the key event, and the images include an image sequence over a time-period of the key event. Before the vehicle handover system changes control of the vehicle from the automated driving mode to the passenger, the handover scene is displayed to the passenger.

Abstract: A driving assistance device comprises a processor which is configured to generate a risk map using road surface data and vehicle data; and to generate a driving instruction based on the risk map; wherein the risk map represents areas of risk related to a road surface within a vicinity of a vehicle; and wherein the road surface data represent irregularities of a road surface within the vicinity of the vehicle.

Abstract: A monitoring system may include a memory having computer-readable instructions stored thereon and a processor operatively coupled to the memory. The processor may read and execute the computer-readable instructions to perform or control performance of operations. The operations may include receive, prior to a collision involving a vehicle, sensor data representative of a feature of an internal environment and determine the collision has occurred. The operations may include automatically instruct, based on the collision, a sensor to generate another sensor data representative of another feature of the internal environment. The operations may include receive the another sensor data from the sensor and compare the sensor data and the another sensor data to accident data corresponding to previous accidents. The accident data may include a diagnosed injury and an accident severity of each of the previous accidents. The operations may include determine a severity of the collision based on the comparison.

Abstract: Mobility-as-a-Service (MaaS) provides technical solutions for technical problems facing this shift toward public and private vehicles for hire, including providing a platform for users to identify and select public transportation and private vehicles for hire. Users may plan and book transportation services through a MaaS platform, such as a smartphone application. Technical solutions described herein include improved identification and selection of a vehicle based on wireless communication, such as using Near-Field Communication (NFC), Bluetooth, Wi-Fi, and other wireless communication.

Abstract: Various systems and methods for providing assistance at the end of an autonomous system journey are provided. A system can include an autonomous system configured to transport the user, a first sensor coupled to the autonomous system and configured to provide a sensor stream of an environment between the autonomous system and a terminus, and a terminus assistance processor mechanically coupled to the autonomous system. In an example, the terminus assistance processor can receive and process the sensor stream, detect and can track the user in the environment in response to the sensor stream, and can provide status information to a first mobile device based on the sensor stream.