Abstract: Device security across multiple operating system modalities, including: allocating, by a hypervisor, to a first virtual machine comprising a first operating system, based on the first modality, a first one or more access privileges to one or more resources; allocating, by the hypervisor, to a second virtual machine comprising a second operating system, based on the second modality, a second one or more access privileges to the one or more resources; and modifying, by the hypervisor, the second one or more access privileges in response to a change in an execution state of the first virtual machine.

Abstract: A failover system for autonomous vehicles, including: detecting an error associated with a hardware resource of a first autonomous driving system of the autonomous vehicle; performing a failover from the first autonomous driving system to a second autonomous driving system comprising fewer hardware resources relative to the first autonomous driving system, wherein performing the failover causes control operations generated by the second autonomous driving system to be applied to the autonomous vehicle instead of control operations generated by the first autonomous driving system; and reconfigure one or more other hardware resources of the first autonomous driving system not associated with the error into a redundant autonomous driving system.

Abstract: Selective model execution in an autonomous vehicle, including: identifying, based on a first state space associated with a first machine learning model, one or more second state spaces neighboring the first state space, wherein the one or more second state spaces are each associated with a corresponding second machine learning model of one or more second machine learning models; and executing the one or more second machine learning models.

Abstract: Scheduling state transitions in an autonomous vehicle, including: detecting a transition signal for transitioning from a first state associated with a first machine learning model to a second state associated with a second machine learning model; determining whether a precondition for generating output by the second machine learning model has been satisfied; and delaying, in response for the precondition not being satisfied, a transition from the first state to the second state.

Abstract: Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Abstract: Shared memory access in a distributed system, including: determining, in response to a memory access request, based on a time value, an entry in an access permissions table by: determining, based on a modulo of the time value and a number of entries in the access permissions table, a table index; determining, based on the table index, the entry; and determining, based on the entry, whether to allow the memory access request.

Abstract: Value-based data transmission in an autonomous vehicle, comprising: acquiring sensor data from a plurality of sensors of the autonomous vehicle, the sensor data comprising a plurality of portions; determining, for each portion of the sensor data, a value based on one or more objects identified in the sensor data; determining, based on the values for the sensor data, an upload policy; and transmitting, based on the upload policy, one or more portions of the sensor data to a server.

Abstract: Timed memory access, including: determining, in response to a memory access request, based on a time value, an entry in an access permissions table; and determining, based on the entry, whether to allow the memory access request that can include determining, based on a modulo of the time value and a number of entries in the access permissions table, a table index and determining, based on the table index, the entry.

Abstract: Scheduled data transfer, including: determining, based on a data transfer schedule for a plurality of nodes, a time to transfer data from a first node to a second node; and transferring, at the determined time, data from the first node to the second node.

Abstract: Transforming model data, including: applying a transformation to data generated by a machine learning model; and providing the transformed data to a consumer of the transformed data, wherein the consumer is configured to: reverse the transformation applied to the transformed data; and perform one or more operations based on the data.

Abstract: Transmitting remotely valued data in an autonomous vehicle, including: acquiring sensor data from a plurality of sensors of the autonomous vehicle, the sensor data comprising a plurality of portions; generating an index for the sensor data; transmitting the index to a server; receiving, based on the index, a value for each portion of the sensor data; and transmitting, based on the value for each portion of the sensor data, the one or more portions of the sensor data.

Abstract: Extending fused multiply-add instructions, including: receiving an extended fused multiply-add (FMA) instruction comprising a first subset of bits indicating a corresponding register for each operand of a fused multiply-add (FMA) operation and a second subset of bits indicating a different register storing data describing one or more transformations applicable to one or more operands of the FMA operation; and performing, based on the extended FMA instruction.

Abstract: Automatic disengagement of an autonomous driving mode may include receiving, from a steering torque sensor, torque sensor data indicating an amount of torque applied to a steering system of the autonomous vehicle; determining a predicted torque based on one or more motion attributes of the steering system of the autonomous vehicle; determining a differential between the predicted torque and the amount of torque; and determining, based on the differential, whether to disengage an autonomous driving mode of the autonomous vehicle.

Abstract: Extending fused multiply-add instructions, the method comprising: receiving an extended fused multiply-add (FMA) instruction indicating one or more operands of a fused multiply-add (FMA) operation and one or more transformations to be applied to the one or more operands; and performing, based on the extended FMA instruction, the one or more transformations and the FMA operation.

Abstract: A redundant processing fabric in an autonomous vehicle may include: processing, by a first processing unit of a plurality of processing units, sensor data from a first sensor of a plurality of sensors, where the plurality of processing units are coupled to the plurality of sensors via a switched fabric, wherein the plurality of processing units and plurality of sensors are included in the autonomous vehicle, wherein the sensor data corresponds to an environment external to the autonomous vehicle; determining a failure in processing the sensor data by the first processing unit; and severing, in the switched fabric, a first communications path between the first sensor and the first processing unit; and establishing, in the switched fabric, a second communications path between the first sensor and a redundant processing unit.

Abstract: Extending fused multiply-add instructions, the method comprising: receiving an extended fused multiply-add (FMA) instruction indicating one or more operands of a fused multiply-add (FMA) operation and one or more transformations to be applied to the one or more operands; and performing, based on the extended FMA instruction, the one or more transformations and the FMA operation.

Abstract: Handling input data errors in an autonomous vehicle using predictive inputs, including: determining an error in input data for a model of a plurality of models of an automation system of the autonomous vehicle; generating predicted input data for the model; and generating, based on the predicted input data, output data for the model.

Abstract: Modifying settings of autonomous vehicle sensors based on predicted environmental states, including: determining, based on sensor data, that a predicted environmental state of the autonomous vehicle is associated with a sensory input outside an operating range of a sensor; determining a modified operating range of the sensor; and modifying one or more sensors to operate according to the modified operating range.

Abstract: Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

Abstract: A redundant processing fabric in an autonomous vehicle may include processing, by a first processing unit of a plurality of processing units, sensor data from a first sensor of a plurality of sensors, where the plurality of processing units are coupled to the plurality of sensors via a switched fabric, wherein the plurality of processing units and plurality of sensors are included in the autonomous vehicle; determining a failure in processing the sensor data by the first processing unit; and redirecting, via the switched fabric, sensor data from the first sensor a redundant processing unit.