Abstract: A system for monitoring pipeline leaks including a data acquisition unit configured to receive measurement data from a plurality of pipeline instruments, the measurement data corresponding to operation of a pipeline, at least one leak detection engine configured to receive at least a portion of the measurement data and generate at least one pipeline leak parameter, a process analyzer configured to receive at least a portion of the measurement data and determine a state of operation of the pipeline, and an orchestrator configured to determine whether a pipeline leak has occurred based on the pipeline leak parameters and the state of operation of the pipeline.

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: Autonomous driving system methods and devices which trigger vehicular actions based on the monitoring of one or more occupants of a vehicle are presented. The methods, and corresponding devices, may include identifying a plurality of features in a plurality of subsets of image data detailing the one or more occupants; tracking changes over time of the plurality of features over the plurality of subsets of image data; determining a state, from a plurality of states, of the one or more occupants based on the tracked changes; and triggering the vehicular action based on the determined state.

Abstract: Various embodiments are generally directed to techniques for providing improved privacy protection against vehicle tracking for connected vehicles of a vehicular network. For example, at least one road side unit may: identify a set of vehicles that require pseudonym changes and send an invitation for a pseudonym change event to each of the vehicles, determine at least a total number of the acceptances, determine whether the total number meets or exceeds a predetermined threshold number, send acknowledgement messages to the accepting vehicles if the threshold number is met, and form a vehicle group to coordinate the pseudonym change event during a privacy period. During the privacy period, the RSU and the vehicles may communicate with each other in a confidential and private manner via key-session-based unicast transmission, and coordinate transmission power and vehicle trajectory adjustments to maximize the benefits for safety and obfuscation for privacy.

Abstract: Deep neural networks (DNNs) with budding ensemble architectures may be trained using diversity loss. A DNN may include a backbone and a plurality of heads. The backbone includes one or more layers. A layer in the backbone may generate an intermediate tensor. The plurality of heads may include one or more pairs of heads. A pair of heads includes a first head and a second head duplicated from the first head. The second head may include the same tensor operations as the first head but different internal parameters. The intermediate tensor generated by a backbone layer may be input into both the first head and the second head. The first head may compute a first detection tensor, and the second head may compute a second detection tensor. A similarity between the first detection tensor and the second detection tensor may be used as a diversity loss for training the DNN.

Abstract: Autonomous driving system methods and devices which trigger vehicular actions based on the monitoring of one or more occupants of a vehicle are presented. The methods, and corresponding devices, may include identifying a plurality of features in a plurality of subsets of image data detailing the one or more occupants; tracking changes over time of the plurality of features over the plurality of subsets of image data; determining a state, from a plurality of states, of the one or more occupants based on the tracked changes; and triggering the vehicular action based on the determined state.

Abstract: Autonomous driving system methods and devices which trigger vehicular actions based on the monitoring of one or more occupants of a vehicle are presented. The methods, and corresponding devices, may include identifying a plurality of features in a plurality of subsets of image data detailing the one or more occupants; tracking changes over time of the plurality of features over the plurality of subsets of image data; determining a state, from a plurality of states, of the one or more occupants based on the tracked changes; and triggering the vehicular action based on the determined state.

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: Systems and techniques for securing vehicle privacy in a driving infrastructure are described herein. A vehicle may contact a group identification (ID) issuer to register itself. A group ID may be received from the group ID issuer to indicate acceptance as a member. The vehicle may then contact the driving infrastructure to attach to the driving infrastructure using the group ID to identify the vehicle. In response, the vehicle receives an attachment ID from the driving infrastructure. Here, the attachment ID is used to secure communications between the vehicle and the driving infrastructure.

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: For example, an apparatus may include an input to receive Machine Learning (ML) model information corresponding to an ML model to process input information; and a processor to construct a multi-model ML architecture including a plurality of ML model variants based on the ML model, wherein the processor is configured to determine the plurality of ML model variants based on an attribution-based diversity metric corresponding to a model group including a first ML model variant and a second ML model variant, wherein the attribution-based diversity metric corresponding to the model group is based on a diversity between a first attribution scheme and a second attribution scheme, the first attribution scheme representing first portions of the input information attributing to an output of the first ML model variant, the second attribution scheme representing second portions of the input information attributing to an output of the second ML model variant.

Abstract: Disclosed herein are systems, devices, and methods for an uncertainty-aware robot system that may perform a personalized risk analysis of an observed person. The uncertainty-aware robot system determines a recognized behavior for the observed person based on sensor information indicative of a gait of the observed person. The uncertainty-aware robot system determines an uncertainty score for the recognized behavior based on a comparison of the recognized behavior to potentially expected behaviors associated with the observed person and the environment. The uncertainty-aware robot system generates a remedial action instruction based on the uncertainty score.

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: Various embodiments are generally directed to techniques for providing improved privacy protection against vehicle tracking for connected vehicles of a vehicular network. For example, at least one road side unit may: identify a set of vehicles that require pseudonym changes and send an invitation for a pseudonym change event to each of the vehicles, determine at least a total number of the acceptances, determine whether the total number meets or exceeds a predetermined threshold number, send acknowledgement messages to the accepting vehicles if the threshold number is met, and form a vehicle group to coordinate the pseudonym change event during a privacy period. During the privacy period, the RSU and the vehicles may communicate with each other in a confidential and private manner via key-session-based unicast transmission, and coordinate transmission power and vehicle trajectory adjustments to maximize the benefits for safety and obfuscation for privacy.

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: 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, devices, and methods of a robot safety system for improving the safety of human-robot collaborations. The robot safety system may include a processor that receives a collaboration policy for task collaboration between a robot and a collaborator. The task collaboration may include a movement plan of the robot to move a manipulator to a collaboration position. The robot safety system may also determine a new collaboration position based on the collaboration policy. The robot safety system may also update the collaboration position of the movement plan to the new collaboration position.

Abstract: Various embodiments are generally directed to techniques for providing improved privacy protection against vehicle tracking for connected vehicles of a vehicular network. For example, at least one road side unit may: identify a set of vehicles that require pseudonym changes and send an invitation for a pseudonym change event to each of the vehicles, determine at least a total number of the acceptances, determine whether the total number meets or exceeds a predetermined threshold number, send acknowledgement messages to the accepting vehicles if the threshold number is met, and form a vehicle group to coordinate the pseudonym change event during a privacy period. During the privacy period, the RSU and the vehicles may communicate with each other in a confidential and private manner via key-session-based unicast transmission, and coordinate transmission power and vehicle trajectory adjustments to maximize the benefits for safety and obfuscation for privacy.

Abstract: A device including a processor configured to: receive sensor measurements; determine a viewer position based on the received sensor measurements; generate a control signal based on the viewer position; and determine a view based on the viewer position.