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.

Abstract: An autonomous vehicle includes sensors proximate to one or more wheels that capture wheel sensor data describing wheel-road interactions. The wheel sensor data may be processed to determine characteristics of the road as the vehicle travels, describing the relative audible, vibratory, or pressure effects of the wheel-road interaction. The wheel sensor data and/or characteristics may then be used to generate a road sensor map describing locations of received wheel sensor data and/or detected road conditions. The wheel sensor data and/or road sensor map may also be used to refine or correct localization of the AV within the environment. In navigation, the AV may use a prior road sensor map for route planning and control, for example to prioritize movement over relatively smooth roads or to navigate to regions with unmapped or aging road information.

Abstract: A method is described and includes generating at least one predicted remote assistance (RA) need scenario in connection with a ride service provided by an autonomous vehicle (AV), wherein the generating is performed prior to an actual occurrence of the predicted RA need scenario; presenting the at least one predicted RA need scenario to an RA system for resolution of the at least one predicted RA need scenario, wherein the resolution comprises the RA system providing an operational decision in connection with the presented at least one RA need scenario; and storing the operational decision in an RA instruction buffer.

Abstract: A computing apparatus, comprising: a processor circuit and a memory; and instructions encoded within the memory to instruct the processor to: receive a stored dependency graph for a first hardware configuration for a plurality of compute nodes; receive a second hardware configuration comprising a modification to the first hardware configuration; iteratively model the second hardware configuration comprising adjusting one or more of a plurality of operational parameters for a model of the second hardware configuration, and selecting an optimum configuration of the plurality of operational parameters; and sending the optimum configuration to a real-world embodiment of the second hardware configuration.

Abstract: Downhole ranging from multiple wellbores. In one example, multiple transmitters and multiple receivers are disposed in multiple wellbores to exchange electromagnetic signals. By implementing a full compensation technique, a computer system determines multiple compensated signals. A compensated signal is determined from a signal received from a first wellbore and a second signal received from a second wellbore. In another example, a first transmitter is disposed in a first wellbore, a first receiver is disposed in a second wellbore, and either a second transmitter or a second receiver is disposed in either the first wellbore or the second wellbore. By implementing partial compensation techniques, a computer system determines compensated signals. Using the compensated signals, the computer system determines a position of a first wellbore relative to a second wellbore, and provides the position.

Abstract: A system may include a vehicle controller, comprising: a hardware platform, comprising a processor and a memory; and instructions encoded within the memory to: provide an autonomous vehicle control module with at least L4 vehicle control capability; select a semi-autonomous operating mode that uses a subset of the L4 vehicle control capability; and operate a vehicle in the semi-autonomous operating mode.

Abstract: Some autonomous vehicles rely on deep learning models to generate outputs that can be used to control the autonomous vehicle. Deep learning models can be trained to fit datasets collected in a particular environment. These deep learning models may not perform as well in a different environment, and new deep learning models may need to be created and trained. Training new models can be computationally expensive, and the amount of datasets collected in a new environment for training the new models may be limited. Various techniques involving sharing parameters (e.g., weights) between deep learning models can alleviate some of these challenges.

Abstract: Systems and methods for retrofitting a vehicle computing system dependent on a certain type of maps to operate with other type(s) of maps are provided. For example, a method performed by a vehicle may include receiving, from one or more sensors of the vehicle, first sensor data associated with a surrounding environment of the vehicle; receiving first map data of a first map type; and retrofitting a vehicle controller of the vehicle that is based on a second map type different from the first map type to operate on the first map data, where the retrofitting includes adapting the first map data using the first sensor data to generate second map data associated with the second map type; and determining, by the vehicle controller, an action for the vehicle based at least in part on the generated second map data.

Abstract: Systems and methods for retrofitting a light detection and ranging (LIDAR)-based vehicle computing system to operate with vision-based sensor data are provided. For example, a method implemented by a vehicle may include receiving, from one or more sensors of a first sensing modality at the vehicle, first sensor data associated with a surrounding environment of the vehicle; and retrofitting a vehicle controller of the vehicle that is based on a second sensing modality different from the first sensing modality to operate on the first sensor data, where the retrofitting includes generating second sensor data from the first sensor data based on the second sensing modality; and determining, by the vehicle controller, an action for the vehicle based at least in part on the generated second sensor data.

Abstract: An AV includes a 3D printer. The AV obtains a request to 3D print an object, e.g., based on identification of a feature associated with the AV or a user input. The AV can control the 3D printing process based on a motion of the AV that occurs during the 3D printing process. The AV may determine an influence of the motion on the 3D printing process and adjust the 3D printing process to compensate for the influence. Also, the AV may determine a motion during the 3D printing process to facilitate the 3D printing process, e.g., facilitate a movement of an item associated with the 3D printing process. The item may be at least a part of the 3D printer, a material from which the object is printed, or a portion of the object. A fleet management system can manage 3D printing services provided by a fleet of AVs.

Abstract: Systems and methods for identifying a precise stopping location using vehicle sensors and mechanical simulation. In particular, systems and methods are provided for using vehicle sensor information to understand vehicle surroundings, identifying an appropriate stopping position that either does not disrupt traffic (or minimally disrupts traffic), and identifying a path to the stopping position. Additionally, the suitability of the stopping position is determined by performing a simulation of the vehicle movement to the stopping position as well as a simulation of passenger behavior during vehicle exit. In some examples, the simulation is performed using a short-range relative map of the vehicle surroundings rather than using the driving map.

Abstract: An apparatus includes a processor and a machine-readable medium having program code executable by the processor to cause the apparatus to obtain a scan result based on a core sample, generate a set of contours of the scan result, and generate a set of object associations, wherein the set of object associations includes a first object association that is associated with a first contour from the set of contours and a second contour from the set of contours. The program code also generates a set of contour connections, wherein the set of contour connections includes a first contour connection that is based on the first object association. The program code also generates a reconstructed core having a three-dimensional space based on the set of contour connections, wherein the three-dimensional space includes a volume greater than the core sample.

Abstract: Users getting rides in AVs can engage in virtual environments through client devices (e.g., headsets, etc.) that project the virtual environments to the users. A user may perceive a virtual environment and engage in the virtual environment during a ride in an AV. The virtual environment includes a virtual scene generated based on the ride in the AV or an interaction of the user with another person in the real-world. The virtual scene includes a virtual representation of the user and other virtual objects. A virtual object may be generated based on an object, a behavior of the AV, or an activity of the user in the real-world. The user may perform various virtual activities in the virtual environment, such as communications with other users, digital interactive experiences, etc. A behavior of the AV (e.g., the AV's navigation) may be changed based on a virtual activity of the user.

Abstract: A model for controlling an AV can be trained with a privacy budget. A data set that includes sensor data collected by an AV from an environment around the AV is received. A privacy score of the data set is determined. The privacy score indicates a measurement of privacy information included in the data set. The privacy score may be compared with the privacy budget to determine whether the data set meets the privacy budget. After a determination that the data set does not meet the privacy budget, the data set is adjusted, e.g., by adjusting one or more objects captured by the sensor data. An example adjustment to an object may include removing or modifying a private feature of the object. The adjust data set has a privacy score that falls in the privacy budget and can be used as a privacy-protected data set to train the model.

Abstract: An operation of an AV in a scene is simulated. The AV includes sensors detecting the scene and generating sensor data. The sensor data can be input into a control model that outputs control signals, in accordance with which the AV operates. A learning cost, i.e., a cost of training or applying the control model, is determined. A performance of the AV during the simulation is evaluated. A simulation cost, i.e., a cost of running the simulation is determined. The control model can be optimized based on the learning cost and the performance of the AV. Settings of the sensors can be optimized based on the simulation cost and the performance of the AV. The optimization of the control model or the settings of the sensors can be done through reinforcement learning.

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: A disclosed downhole drilling system may include a drill bit electrically coupled to a pulse-generating circuit to generate electrical arcs between first and second electrodes during pulsed drilling operations, a sensor to record responses to electromagnetic or acoustic waves produced by the electrical arcs, and a sensor analysis system. The electrical arcs occur at different azimuthal locations between the electrodes. The sensor analysis system may obtain a plurality of measurements representing first responses recorded by the sensor during a pulsed drilling operation, generate a model of a source of the electrical arcs based on the measurements, obtain an additional measurement representing a second response recorded by the sensor during the operation, and determine a characteristic of a formation near the drill bit using an inversion based on the model and the additional measurement. The determined characteristic may be used to determine dip parameters or construct images of the formation.

Abstract: Disclosed herein are methods and system for formation fluid sampling. In at least some embodiments, the method includes pumping formation fluid from a public flow line of a downhole tool via a private flow line into a detachable sample chamber. The method also includes isolating the private flow line from the public flow line. The method also includes collecting measurements of the formation fluid in the private flow line. The method also includes associating the measurements with the detachable sample chamber.

Abstract: Machine learning model optimization systems and methods are disclosed. A system receives sensor data captured by one or more sensors of a vehicle during a first time period. The vehicle uses a first trained machine learning (ML) model for one or more decisions of a first decision type during the first time period. The system generates a second trained ML model at least in part by using the sensor data to train the second trained ML model. The system identifies an optimal trained ML model from a plurality of trained ML models. The plurality of trained ML models includes the first trained ML model and the second trained ML model. The system causes the vehicle to use the optimal trained ML model for one or more further decisions of the first decision type during a second time period after the first time period.

Abstract: Pipe]parameter determinations from electromagnetic logs can be improved, in accordance with various embodiments, by weighting signals with frequencies below a threshold associated with resolution degradation lower than signals with frequencies above the threshold. The threshold frequency may be determined based on a spatial resolution associated with the logging tool and a logging speed. Further embodiments are described.