Systems and methods for safe and reliable autonomous vehicles

Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a faster, more reliable, safer, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.

Method of spacer-defined direct patterning in semiconductor fabrication

A method of spacer-defined direct patterning in semiconductor fabrication includes: providing a photoresist structure having a target width of lines; trimming the photoresist structures such that a width of each trimmed photoresist structure is smaller than the target width; depositing an oxide film on the template, thereby entirely covering with the oxide film an exposed top surface of the template and the trimmed photoresist structures; etching the oxide film-covered template to remove an unwanted portion of the oxide film without removing the trimmed photoresist structures so as to form vertical spacers isolated from each other, each spacer substantially maintaining the target width and being constituted by the trimmed photoresist structures and a vertical portion of the oxide film covering sidewalls of the trimmed photoresist structures; and etching the spacer-formed template to transfer a pattern constituted by the spacers to the template.

Substrate processing method and apparatus

Provided are a substrate processing apparatus and a substrate processing method capable of achieving uniform trimming throughout an entire surface of a substrate. The substrate processing apparatus includes a gas channel including a center gas inlet and an additional gas inlet spaced apart from the center gas inlet, and a shower plate including a plurality of holes connected to the center gas inlet and the additional gas inlet, wherein a gas flow channel is formed having a clearance defined by a lower surface of the gas channel and an upper surface of the shower plate, the lower surface and the upper surface being substantially parallel.

Object detection and detection confidence suitable for autonomous driving

In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

RECONFIGURABLE DISPLAY IN COMPUTER-ASSISTED TELE-OPERATED SURGERY

The technology described in this document can be embodied in a method that includes operating a surgical system to perform a surgical process, the surgical system including a display device, and receiving, at one or more processing devices, data from multiple data sources. The method also includes determining a current phase of the surgical process, and displaying, on the display device, visual representations corresponding to the data from a first set of the multiple data sources in a first arrangement within a display region of the display device. At least one of the first set of the multiple data sources and the first arrangement is associated with the current phase of the surgical process.

Smart de-identification using date jittering

System and method to produce an anonymized cohort having less than a predetermined risk of re-identification. The method includes receiving a data query of requested traits for the anonymized cohort, querying a data source to find records that possess at least some of the traits, forming a dataset from at least some of the records, and grouping the dataset in time into a first boundary group, a second boundary group, and one or more non-boundary groups temporally between the first boundary group and second boundary group. For each non-boundary group, calculating maximum time limits the non-boundary group can be time-shifted without overlapping an adjacent group, calculating a group jitter amount, capping the group jitter amount by the maximum time limits and by respective predetermined jitter limits, and jittering said non-boundary group by the capped group jitter amount to produce an anonymized dataset. Return the anonymized dataset.

METHOD AND SYSTEM FOR DETERMINING POLICIES, RULES, AND AGENT CHARACTERISTICS, FOR AUTOMATING AGENTS, AND PROTECTION

A method of automating an agent for environment includes loading an action determination model that indicates an action option, determining whether the action determination model indicates a next action option, and if so, determining an action based on the action determination model, determining whether an execution data model has been stored, and if so, obtaining data pertaining to the determined action, executing the action, determining a result of the action, storing the data pertaining to the result in an execution data model. Then, until the environment or agent reach an end state, the following are repeated: determining whether the action determination model indicates the next action option, and if so, determining the action based on the action determination model, and determining whether the execution data model has been stored, and if so, obtaining the data pertaining to the determined action.

Data processing and scanning systems for generating and populating a data inventory

In particular embodiments, a data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers personal data (e.g., such as a software application, “internet of things” computerized device, database, website, data-center, server, etc.). The system may be configured to identify particular data assets and/or personal data in data repositories using any suitable intelligent identity scanning technique.

Data processing systems for generating and populating a data inventory

In particular embodiments, a data processing data inventory generation system is configured to: (1) generate a data model (e.g., a data inventory) for one or more data assets utilized by a particular organization; (2) generate a respective data inventory for each of the one or more data assets; and (3) map one or more relationships between one or more aspects of the data inventory, the one or more data assets, etc. within the data model. In particular embodiments, a data asset (e.g., data system, software application, etc.) may include, for example, any entity that collects, processes, contains, and/or transfers personal data (e.g., such as a software application, “internet of things” computerized device, database, website, data-center, server, etc.). For example, a first data asset may include any software or device (e.g., server or servers) utilized by a particular entity for such data collection, processing, transfer, storage, etc.

Seamless stream failover with distributed manifest generation

A device for seamlessly distributing a media stream from different ingest devices of a delivery platform. The device fails over from a first ingest device to a second ingest device in response to one or more errors from attempts to retrieve parts of the media stream from the first ingest device prior to any signaling or messaging (e.g., an unpublish event) from the first ingest device indicating that the media stream has ended, is no longer available, or is otherwise removed from the first ingest device. The device also produces manifests identifying two or more bitrates for a media stream when each of the two or more bitrates is uploaded to and ingested by a different ingest device of the delivery platform.