Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

Abstract: Aspects concern a method for controlling a braking of a vehicle. The method including detecting a braking situation, determining a classification of the braking situation, selecting a braking profile based on the determined classification, and applying a deceleration based on the selected braking profile to maintain a safety distance based on the selected braking profile.

Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: A safety system (200) for a vehicle (100) is provided. The safety system (200) may include one or more processors (102). The one or more processors (102) may be configured to control a vehicle (100) to operate in accordance with the predefined stored driving model parameters, to detect vehicle operation data during the operation of the vehicle (100), to determine whether to change predefined driving model parameters based on the detected vehicle operation data and the driving model parameters, to change the driving model parameters to changed driving model parameters, and to control the vehicle (100) to operate in accordance with the changed driving model parameters.

Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: An exemplary method includes obtaining vehicle data comprising environmental perception data indicating a risk assessment regarding one or more perceived elements of an environment surrounding a vehicle; obtaining driver perception data regarding a driver inside the vehicle; determining an integrated risk assessment based on the vehicle data and the driver perception data; and determining an Operational Design Doman (ODD) compliance assessment of the vehicle at least based on the determined integrated risk assessment.

Abstract: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., “perception gaps”). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., “filling the peception gaps”). Other embodiments are disclosed and/or claimed.

Abstract: Disclosed herein are systems, devices, and methods for improving the safety of a robot. The safety system may determine a safety envelope of a robot based on a planned movement of the robot and based on state information about a load carried by a robot. The state information may include a dynamic status of the load. The safety system may also determine a safety risk based on a detected object with respect to the safety envelope. The safety system may also generate a mitigating action to the planned movement if the safety risk exceeds a threshold value.

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: A computing device, including: a memory configured to store computer-readable instructions; and unintended human motion detection processing circuitry configured to execute the computer-readable instructions to cause the computing device to: interpret a human action; receive autonomous mobile robot (AMR) sensor data from an AMR sensor; and detect whether the human action is intended or unintended, wherein the detection is based on a predicted human action, a current human emotional or physical state, the interpreted human action, and the AMR sensor data.

Abstract: Disclosed herein are systems and methods for dynamically distributing a safety, awareness task. The systems and methods may include receiving hardware resources data associated with a plurality of remote computing systems. A plurality of safety assurance profiles may be received. Each of the plurality of safety assurance profiles may be associated with a respective service. A safety assurance task may be dynamically assigned to one of the plurality of remote computing systems based on the hardware resources data and one of the plurality of safety assurance profiles.

Abstract: Disclosed herein are systems, devices, and methods of a sanitation system for intelligently adapting the cleaning operations of a sanitation robot. The sanitation system detects a plurality of objects in a workspace and then, for each detected object of the plurality of objects, determines a sanitation score for the detected object based on observations of the detected object over a time period. The sanitation system generates a cleaning schedule based on the sanitation score for each detected object, wherein the cleaning schedule comprises instructions for a sanitation robot to clean the plurality of objects.

Abstract: Devices and methods for a vehicle are provided in this disclosure. A device for controlling an active seat of a vehicle may include a processor and a memory. The memory may be configured to store a transfer function. The processor may be configured to predict an acceleration of the active seat of the vehicle based on a first sensor data and the transfer function. The first sensor data may include information indicating an acceleration of a vibration source for the vehicle. The processor may be further configured to generate a control signal to control a movement of the active seat at a first instance of time based on the predicted acceleration.

Abstract: Disclosed herein are systems and methods for detecting and mitigating inappropriate behavior. The systems and methods may include receiving data. Using the data a harassment score and/or classification for a behavior may be determined. Using the harassment score and/or classification, a determination may be made as to when the harassment score and/or classification for the behavior exceeds a threshold. When the threshold is exceeded, a protection system and/or action engine may be activated to mitigate the inappropriate behavior.

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: Devices and methods for a road user are provided in this disclosure. An apparatus for determining detection of a road user may include a memory configured to store a plurality of data items received from a plurality of further road users. Each of the plurality of data items may include a detection information indicating whether an object has or has not been detected by one of the plurality of further road users. Furthermore, the apparatus may include a processor that is configured to determine a detection result indicating whether the road user has been detected by the one of the plurality of further road users based on the detection information.

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: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., “perception gaps”). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., “filling the perception gaps”). Other embodiments are disclosed and/or claimed.

Abstract: Techniques are disclosed to detect, inform, and automatically correct typical awareness-related human driver mistakes. This may include those that are caused by a misunderstanding of the current situation, a lack of focus or attention, and/or overconfidence in any currently-engaged assistance features. The disclosure is directed to the prediction of vehicle maneuvers using driver and external environment modeling. The consequence of executing a predicted maneuver is categorized based upon its risk or danger posed to the driving environment, and the vehicle may execute various actions based upon the categorization of a predicted riving maneuver to mitigate or eliminate that risk.