Gm Cruise Holdings LLC

Oath Inc.

Yahoo Assets LLC

Netflix Inc.

Zhonghua Ma

Jian Zhou

Michael Chang-Ping Chen

Xumin Wu

Congxia Dai

Heri Zhao

Sagar Dattatraya Bhandare

Juan Fasola

Adam Samuel Cadien

Ankit Rohatgi

Nicholas Semansky

Jabeer Ahmed

Anne Aaron

The subject technology is related to autonomous vehicles (AV) and, in particular, to calibrating multiple sensors of an AV in an undefined training area. An example method includes instructing the AV to pilot itself in an undefined training area subject to at least one constraint, wherein the AV is instructed to pilot itself along a path until the AV has overlapped at least a portion of the path, and at a location at which the AV has overlapped at least the portion of the path, determining that first returns from a previous LIDAR scan overlaps with second returns from a subsequent LIDAR scan taken when the AV has overlapped at least the portion of the path. Initially, the AV does not include a location reference to identify locations of objects in the undefined training area and a plurality of sensors of the AV are uncalibrated with respect to each other.

Multiple sensor calibration in autonomous vehicles performed in an undefined environment

The subject technology is related to autonomous vehicles (AV) and, in particular, to calibration of an sensors of an AV on open roads after calibrating the sensors in an undefined training area. An example method includes calibrating a plurality of sensors of the AV based on performing a plurality of training steps of increasing complexity within the undefined environment, wherein the plurality of sensors of the AV are uncalibrated with respect to each other, determining that the AV satisfies an open road requirement to navigate an open road based on completing the plurality of training steps in the undefined environment, and operating the AV on open roads subject to at least one constraint. The method of claim, wherein the at least one constraint comprises a list of prohibited maneuvers that limit operation of the AV on open roads. The constraints are configured to dynamically change.

Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

Extrinsic calibration of multiple vehicle sensors using combined target detectable by multiple vehicle sensors

One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Video encoding with content adaptive resource allocation

In one embodiment of the present invention, an encode validator identifies and classifies errors introduced during the parallel chunk-based translation of a source to a corresponding aggregate encode. In operation, upon receiving a source for encoding, a frame difference generator creates a frame difference file for the source. A parallel encoder then distributes per-chunk encoding operations across machines and creates an aggregate encode. The encode validator decodes the aggregate encode and creates a corresponding frame difference file. Subsequently, the encode validator performs phase correlation operations between the two frame difference files to detect errors generated by encoding process faults (i.e., dropping a frame, etc.) while suppressing discrepancies inherent in encoding, such as those attributable to low bit-rate encoding. Advantageously, since the encode validator leverages frame difference files, this indirect verification technique enables efficient debugging of parallel encoding processes in which the complete source is unavailable for post-encode analysis.

Techniques for identifying errors introduced during encoding

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San Jose, CA, United States of America

Zhonghua Ma