Abstract: A system includes a power supply and a plurality of buses coupled to the power supply. A bus selector is coupled to an output of each of the plurality of buses. A power storage unit that is separate from the power supply is coupled to an output of the power supply via the bus selector. A power controller is coupled to an output of the bus selector and to an output of the power storage unit. The power controller selects a power output as the output of the power supply or the output of the power storage unit.

Abstract: Fault state recovery using predefined configurations, including: determining that a first node of a plurality of nodes has failed; identifying, based on the first node failing, a failure state from a plurality of predefined failure states each corresponding to a different combination of one or more failed nodes, wherein each of the plurality of predefined failure states corresponds to a respective configuration of a plurality of configurations; and applying the respective configuration corresponding to the identified failure state, wherein applying the respective configuration comprises at least one of: updating one or more memory mapping tables based on the respective configuration or copying data from memory locations used based on a previous configuration to memory locations indicated in the respective configuration.

Abstract: Process scheduling based on data arrival in an autonomous vehicle, including: receiving, by a node and from one or more other nodes of a distributed automation computing system, a plurality of portions of data; generating a process schedule by scheduling, for each portion of data of the plurality of portions of data, a process for processing a corresponding portion of data within a time window, wherein an ordering of the process schedule corresponds to an order of arrival of the plurality of portions of data; and executing, during the time window, the process schedule.

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: The disclosure relates to a device for vehicle simulation, testing, and validation. The device may comprise a memory and a processor operatively coupled to the memory. The processor may be configured to identify a first parameter set defined using a first scenario format, where the first parameter set comprises: one or more first scenario format axes, one or more first scenario format relationships, and one or more first scenario format constraints. The processor may be configured to map the first parameter set to an internal parameter set using an internal parameter format, where the internal parameter format comprises: one or more internal axes, one or more internal relationships, and one or more internal constraints. The processor may be configured to generate a test configuration object using the internal parameter set, and send the test configuration object to a testing modality for testing.

Abstract: The disclosure relates to a device for vehicle testing that is configured to perform operations including testing a first test sample batch comprising a plurality of first test samples to output a plurality of first test sample results. identifying one or more asynchronous test sample results of the plurality of first test samples before the first test sample batch is complete. computing a parametric metric based on the one or more asynchronous test sample results. and adjusting a second test sample batch based on the parametric metric.

Abstract: Reducing throughput of paired cameras, including: selecting, by a first camera of an autonomous vehicle, a first area of focus for a first frame generated by the first camera; selecting, by a second camera of the autonomous vehicle, a second area of focus for a second frame generated by the second camera by matching the first area of focus for the first frame; generating, by the first camera from the first frame, a first downsampled frame and a first cropped frame, wherein the first cropped frame is based on the first area of focus; and generating, by the second camera from the second frame, a second downsampled frame and a second cropped frame, wherein the second cropped frame is based on the second area of focus.

Abstract: Recalibrating stereoscopic cameras during vehicle operation may include: determining a disparity between first image data from a first camera and second image data from a second camera, wherein the first camera and the second camera are in a stereoscopic configuration, and wherein the disparity comprises a difference in placement of one or more objects in the first image data relative to the second image data; and adjusting one or more of the first camera or the second camera, based on the disparity and sensor data from a sensor other than the first camera or the second camera, to calibrate the stereoscopic configuration of the first camera and the second camera to achieve stereoscopic camera distance functionality.

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: Processing predictive input data in an autonomous vehicle, including: receiving input data for a model; determining whether the input data for the model comprises an indication that the input data was generated based on some amount of predicted data; generating, by the model and based on the input data, output data by modifying, in response to the input data comprising the indication, one or more thresholds or one or more confidence scores of the model used in generating output data; and causing an autonomous vehicle to perform one or more driving decisions based on the output data of the model.

Abstract: Modifying autonomous vehicle sensors cameras based on detected lighting boundaries, including: determining, based on video data, that a vehicle will pass through a lighting boundary; determining, based on the video data, that an illumination of a predicted input to a camera of the vehicle after passing the lighting boundary falls outside a range of illumination based on a current exposure setting of the camera; and modifying, in response to determining that the illumination of the predicted input falls outside the range of illumination, an exposure setting of one or more cameras.

Abstract: Disengagement of an autonomous driving mode based on vehicle operating conditions, including: receiving, from a steering torque sensor, torque sensor data indicating an amount of torque applied to a steering system of an autonomous vehicle; determining a predicted torque based on one or more motion attributes of the steering system of the autonomous vehicle; determining an estimated operator-provided torque as a differential between the predicted torque and the amount of torque; selecting a threshold from a plurality of thresholds, wherein each threshold of the plurality of thresholds corresponds to a different possible operating condition of the autonomous vehicle; and disengaging an autonomous driving mode of the autonomous vehicle based on the differential exceeding the threshold.

Abstract: Reducing camera throughput to downstream systems using an intermediary device, including: receiving, by a device and from a camera via a first data link between the device and the camera, a frame; selecting, by the device, an area of focus for the frame; generating, by the device from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus; and providing, by the device to a computing system of an autonomous vehicle via a second data link between the device and the computing system, the downsampled frame and the cropped frame instead of the frame, wherein the second data link has a lower bandwidth than the first data link.

Abstract: According to an aspect of an embodiment, a method may include obtaining map data of a physical area. The map data may include surface data included in the physical area. The surface data may include locations of one or more objects at the surface. The method may also include identifying a first relationship between the map data and sensor information used by autonomous-vehicle software of an autonomous vehicle for navigation. The method may also include generating a three-dimensional environmental representation that represents the physical area based on the map data and the first relationship between the map data and the sensor information. The three-dimensional environmental representation may be configurable as a simulation environment with respect to testing the autonomous-vehicle software.

Abstract: This disclosure relates to methods and systems for vehicle simulation, testing, and validation. The method may include defining one or more test cases for a vehicle stack based on system requirements; linking the one or more test cases to one or more parameterized scenarios, where the one or more parameterized scenarios include one or more parameter permutations; and testing the vehicle stack using the one or more test cases and the one or more parameterized scenarios.

Abstract: According to an aspect of an embodiment, a method may include obtaining map data of a physical area. The map data may include surface data included in the physical area. The surface data may include locations of one or more objects at the surface. The method may also include identifying a first relationship between the map data and sensor information used by autonomous-vehicle software of an autonomous vehicle for navigation. The method may also include generating a three-dimensional environmental representation that represents the physical area based on the map data and the first relationship between the map data and the sensor information. The three-dimensional environmental representation may be configurable as a simulation environment with respect to testing the autonomous-vehicle software.

Abstract: A method may include obtaining HD mapping data including multiple data topics with one or more distinct instances. The method may include creating multiple meshes from corresponding distinct instances. The method may include defining multiple textures for each data topic. The method may include selecting a first data topic and a first distinct instance of the first data topic. The method may include selecting a first texture for the first data topic and applying the first texture to a first mesh corresponding with the first distinct instance. The method may include selecting a second distinct instance of the first data topic. The method may include selecting a second texture and applying the second texture to a second mesh corresponding with the second distinct instance. The method may also include combining the first mesh and the first texture with the second mesh and the second texture to generate a combined mesh.

Abstract: According to an aspect of an embodiment, a method may include obtaining road data and general area data of a geographic area. The method may also include obtaining filtered road data according to a first degree of accuracy based on a first relationship between the road data and sensor information used by autonomous-vehicle software of an autonomous vehicle to navigate roadways. The method may also include obtaining filtered general area data according to a second degree of accuracy based on a second relationship between the sensor information used by the autonomous-vehicle software and the general area data. The method may also include generating a three-dimensional environmental representation representing the geographic area based on the filtered road data and the filtered general area data. The three-dimensional environmental representation may be for testing the autonomous-vehicle software.