Abstract: A method for three dimensional visualization and manipulation of downhole data. A measured signal is received a downhole environment and a three dimensional virtualization of the measured signal is generated. A stereographic viewer displays the three dimensional virtualization of the measured signal. The three dimensional virtualization can be manipulated in response to an input from a user, thereby creating a manipulated three dimensional virtualization. The stereographic viewer can display the manipulated three dimensional virtualization.

Abstract: The disclosed technology provides solutions for optimizing a hardware configuration of an autonomous vehicle (AV) using continuous learning machine. Processes of the disclosed technology can include steps for collecting AV sensor data, receiving hardware profile data relating to the AV sensor data, wherein the hardware profile data describes a hardware configuration of an AV, and providing the AV sensor data and the hardware profile data to a continuous learning machine model of an AV stack. The process can further include receiving an output of the continuous learning machine model based on the AV sensor data and the hardware profile data and determining an updated hardware configuration to replace the hardware configuration based on the output of the continuous learning machine model. Systems and machine-readable media are also provided.

Abstract: Systems and techniques are provided for determining a distribution for a neural network parameter in designing a neural network architecture of an autonomous vehicle (AV). An example method can include determining an exploration distribution of a neural network parameter for one or more neural networks of one or more AVs; determining, for a target context, a target distribution of the neural network parameter from the exploration distribution, the target context comprising at least one of a driving environment associated with a location, a hardware configuration of one or more AVs, a software configuration of the one or more AVs, and a task of the one or more AVs; and providing, to a computer of an AV, the target distribution for implementing one or more neural network parameter values in the target distribution to adjust a neural network of the computer of the AV for operation in the target context.

Abstract: Systems and techniques are provided for management of autonomous cargo by autonomous vehicles (AVs). An example method can include determining, based on data from one or more sensors, a location for deploying a ramp that enables robots to enter the AV, the location comprising an area free of obstacles having one or more dimensions above a threshold; generating an instruction configured to trigger the AV to stop at the location; based on a determination that the AV is at the stopping position, deploying the ramp; sending, to the robots, a message instructing the robots to enter a cabin of the AV via the ramp and guiding each robot to a respective location within the cabin; and based on a determination that the AV has reached a destination of one or more robots, deploying the ramp and guiding the one or more robots to exit the cabin via the ramp.

Abstract: Systems and techniques are described for generating visual projections of information for display on surfaces within a vehicle.

Abstract: Aspects of the disclosed technology provide solutions for generating synthetic driving scenarios using text-based inputs that describe an intended operating goal. A process of the disclosed technology can include steps for generating a first synthetic scene for testing an autonomous vehicle (AV), providing the first synthetic scene to a first machine-learning model to generate a first text description of the synthetic scene, and providing the first text description to a second machine-learning model to determine if the first text description aligns with the predetermined operating goal for the AV. In some aspects, the process can further include generating a second text description for the synthetic scene, if the first text description does not align with the predetermined operating goal for the AV and providing the second text description to a third machine-learning model to generate a second synthetic scene. Systems and machine-readable media are also provided.

Abstract: Systems and techniques are provided for rare scenario handling in autonomous vehicles. In some aspects, an AV management system can determine a target likelihood value range for identifying relevant rare scenarios. In some cases, the AV management system may use the target likelihood value range to generate new relevant rare scenarios that have a likelihood value that falls within the target likelihood value range. In some examples, the AV management system may initiate simulations of the generated relevant rare scenarios and capture synthetic AV scene data, which can be used to train machine learning models used in AVs. As a result, the performance of the AVs may be improved when faced with rare scenarios.

Abstract: Systems and techniques are provided for selecting a runtime performance estimator.

Abstract: Some implementations include a method for estimating a first pipe thickness of a first pipe within multiple nested conductive pipes, the method comprising: forming a measured log including a set of log measurements at different depths using an electromagnetic pulsed tool disposed in multiple nested conductive pipes in a wellbore; generating a plurality of remote-field eddy current (RFEC) look-up curves based on measurements of normalized signal level responses for the multiple nested conductive pipes at one or more points in a time decay response; and selecting an RFEC look-up curve from the plurality of RFEC look-up curves at a point in the time decay response that indicates the first pipe thickness of the first pipe.

Abstract: Systems and techniques are provided for optimizing autonomous vehicle compute paths and identifying latency violations. An example method can include identifying at least one compute path for processing input sensor data by an autonomous vehicle, wherein the at least one compute path includes a set of nodes that are interconnected by one or more edges; implementing a first test iteration of at least one simulation scenario for testing the at least one compute path, wherein the first test iteration of the at least one simulation scenario applies a first artificial delay to at least one edge from the one or more edges; and calculating at least one of a safety parameter and a comfort parameter, wherein the safety parameter and the comfort parameter are based on a result of the at least one simulation scenario.

Abstract: Aspects of the subject technology relate to intelligent components for a vehicle. A system can include a centralized computer system of a vehicle, first and second sensors, and a component of the vehicle coupled to the centralized computer system and the second sensor. The component comprises a localized computer system that is configured to receive a first control signal from the centralized computer system that is generated based on sensor data gathered by the first sensor. The localized computer system is also configured to generate a second control signal based on sensor data gathered by the second sensor, and locally determine whether to control the component according to either the first control signal or the second control signal. The localized computer system can also control the component according to either the first control signal or the second control signal.

Abstract: Systems and techniques are provided for learning deep learning compute paths. An example method can determine a neural network element of a first compute path within a neural network, the first compute path comprising neural network elements including the neural network element, the neural network element comprising a neural network layer and/or a neural network node; determine a second compute path comprising the neural network element and different neural network elements, the second compute path having a smaller size than the first compute path, a lower latency than the first compute path, and/or a smaller compute cost than the second compute path; determine, based on one or more conditions, whether to process data from the neural network element through the first compute path or the second compute path; and process the data from the neural network element through one of the first compute path or the second compute path.

Abstract: The present disclosure generally relates to autonomous vehicle detection of emergency events and, more specifically, to updates in autonomous vehicle behavior based on the detection of an emergency vehicle (EMV) responding to an emergency event. In some aspects, the present disclosure provides a process for receiving sensor data pertaining to an environment of autonomous vehicle (AV), detecting, based on the sensor data, an emergency event in the environment, and identifying, based on the sensor data, an emergency vehicle in the environment. In some aspects, the process can further include steps for determining, based on a behavior of the emergency vehicle, if the emergency vehicle is responding to the emergency event. Systems and computer-readable media are also provided.

Abstract: A method and system for resistivity imaging may comprise disposing a downhole tool into a borehole, wherein the downhole tool comprises a pad and a plurality of electrodes disposed on the pad, taking an impedance measurement with the plurality of electrodes, applying a correction to the measured impedance by using a relaxation constant of the measured impedance to find a corrected impedance for each electrode of the plurality of electrodes, and constructing an image from the corrected impedance, wherein the image maps formation impedance. The system for resistivity imaging may comprise a downhole tool, a conveyance for disposing the downhole tool in a borehole, and an information handling system. The downhole tool may further comprise an arm and a pad wherein the pad comprises a plurality of electrodes and at least one return electrode.

Abstract: The present technology is directed to dynamically adjusting an autonomous vehicle (AV) system based on deep learning optimizations. An AV management system can generate a downscaling signal based on a result of comparing a complexity of an environment for an AV to navigate with a predetermined complexity threshold. Further, the AV management system can perform a downscaling of a neural network associated with an AV system based on the downscaling signal and determine a scenario to test the downscaled neural network in a simulation. The AV management system can adjust one or more parameters of the AV system based on simulated outputs and perform the simulation of the AV based on the adjusted one or more parameters of the AV system and the downscaled neural network to generate simulated performance data. Furthermore, the AV management system can compare the simulated performance data with a predetermined performance threshold.

Abstract: Apparatus and methods to investigate a multiple nested conductive pipe structure can be implemented in a variety of applications. An electromagnetic pulsed tool disposed in the multiple nested conductive pipe structure in a wellbore can make a set of log measurements and provide a measured log at different depths in the multiple nested conductive pipe structure. The total thickness of the multiple nested conductive pipes can be determined at each depth in the measured log using a remote field eddy current look-up curve. The remote field eddy current look-up curve can be correlated to a remote field eddy current regime in time-domain associated with time decay response. Additional apparatus, systems, and methods are disclosed.

Abstract: An autonomous vehicle (AV) has an electromagnetic (EM) sensor that captures EM sensor data about the environment. The captured EM data is processed to generate an EM map of the environment. The EM data may also be used to identify EM characteristics of the environment. The EM map may be used to modify control and operation of the AV and may also be used to signal conditions to another system managing AVs, to correct localization of the AV, identify anomalies in the environment, and modify a previous signature EM map.

Abstract: A system may provide a method of training a deep learning (DL) model, comprising: running a first version of the DL model on a first hardware platform using a first input set; running a second version of the DL model on a second hardware platform using the first input set, comprising imperfectly emulating the first hardware platform on the second hardware platform; computing an adjustment based at least in part on a difference in results between the first version of the DL model and second version of the DL model; and training the DL model on the second hardware platform using the adjustment and a plurality of input sets.

Abstract: Particular embodiments described herein provide for a system and method for determining an intent of a third-party autonomous vehicle by an autonomous vehicle. The method can include identifying the third-party autonomous vehicle, identifying a third-party autonomous vehicle behavior signature associated with the third-party autonomous vehicle, capturing sensor data related to the third-party autonomous vehicle, analyzing the captured sensor data to determine behaviors of the third-party autonomous vehicle, and using a third-party autonomous vehicle behavior signature to determine if the behaviors of the third-party autonomous vehicle indicate a specific intent of the third-party autonomous vehicle. The third-party autonomous vehicle behavior signature can be created by identifying behaviors of the third-party autonomous vehicle and associating the behaviors with an intent of the third-party autonomous vehicle.

Abstract: Particular embodiments described herein provide for a system and method for supplementing a vehicle's sensor data, the system and method can include identifying a personal sensor on a user device, communicating a library of object recognition profiles for the personal sensor to the user device, capturing personal sensor data related to an environment, using the library of object recognition profiles for the personal sensor to use the captured personal sensor data to identify objects and/or features in the environment, and using the identified objects and/or features in the environment to supplement data collected by the autonomous vehicle's sensors.