Abstract: Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

Abstract: Provided are systems, methods, and computer program products for controlling an autonomous vehicle (AV) to maneuver in a roadway, comprising acquiring, data associated with an actor detected on a route of the AV in the roadway for sensing a trajectory of the actor, predicting that the trajectory of the actor includes at least one characteristic that is associated with invoking a conditionally disallowed action in the AV, automatically restricting the conditionally disallowed action from a motion plan of the AV to prevent the AV from executing the conditionally disallowed action in response to detecting that one or more conditions are present in the roadway, issuing a command to control the AV on a candidate trajectory generated to prevent an option for the conditionally disallowed action.

Abstract: Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

Abstract: To improve resource utilization and reduce the virtual machine sprawl in a data center, resource utilization is predicted based on previously measured utilizations, and then, using the predicted utilizations, optimizing the allocation of the computing resources among the virtual machines in the data center. In operation, measurements related to resource utilization by different virtual machines executing in a data center are collected at regular intervals. At each interval, an optimization system predicts virtual machine resource utilizations based on previously collected measurements and previously-generated virtual machine modelers. Based on the utilization predictions as well as the physical topology of the data center, the optimization system identifies different optimizations to the virtual machine topology for the next interval.

Abstract: To improve resource utilization and reduce the virtual machine sprawl in a data center, resource utilization is predicted based on previously measured utilizations, and then, using the predicted utilizations, optimizing the allocation of the computing resources among the virtual machines in the data center. In operation, measurements related to resource utilization by different virtual machines executing in a data center are collected at regular intervals. At each interval, an optimization system predicts virtual machine resource utilizations based on previously collected measurements and previously-generated virtual machine modelers. Based on the utilization predictions as well as the physical topology of the data center, the optimization system identifies different optimizations to the virtual machine topology for the next interval.

Abstract: To improve resource utilization and reduce the virtual machine sprawl in a data center, resource utilization is predicted based on previously measured utilizations, and then, using the predicted utilizations, optimizing the allocation of the computing resources among the virtual machines in the data center. In operation, measurements related to resource utilization by different virtual machines executing in a data center are collected at regular intervals. At each interval, an optimization system predicts virtual machine resource utilizations based on previously collected measurements and previously-generated virtual machine modelers. Based on the utilization predictions as well as the physical topology of the data center, the optimization system identifies different optimizations to the virtual machine topology for the next interval.

Abstract: To improve resource utilization and reduce the virtual machine sprawl in a data center, resource utilization is predicted based on previously measured utilizations, and then, using the predicted utilizations, optimizing the allocation of the computing resources among the virtual machines in the data center. In operation, measurements related to resource utilization by different virtual machines executing in a data center are collected at regular intervals. At each interval, an optimization system predicts virtual machine resource utilizations based on previously collected measurements and previously-generated virtual machine modelers. Based on the utilization predictions as well as the physical topology of the data center, the optimization system identifies different optimizations to the virtual machine topology for the next interval.