Zoox, Inc.

Nuro Corp.

Hao Li

Russell Leigh Smith

Andres Guillermo Morales Morales

Michael Haggblade

Matthew Jeremy Sarett

Jeffrey Ronald Pyke

Kun Geng

Ethan Petrick Dreyfuss

Christian Yang

David Xenakis

Chunlei Dai

Prakul Agarwal

Yun Zhang

Frank Cheng

Conrad Herrmann

Michael Owens

Techniques for capturing videos of content displayed and/or generated by a web-based application are described herein. The videos may be captured by automating screenshots of the content within a headless browser at a fixed frame interval. The headless browser may be used to automate control of the web-based application to generate the content and capture screenshots of the content as the content would appear if the web-based application was being accessed through a traditional web browser graphical user interface. The screenshots may be captured at specific frame intervals while the content is being generated. Additionally, the headless browser or a server executing headless browser may wait for the web-based application to load individual frames of the content before capturing screenshots. The captured screenshots may then be combined to generate a video of the content.

Video generation using a headless browser

Techniques and systems for testing, evaluating and/or otherwise assessing the operation of vehicles are discussed herein. Aspects relate to a system and method comprising receiving vehicle data derived from a vehicle data logging system, the vehicle data being associated with a vehicle run and comprising a plurality of data topics associated with a corresponding plurality of vehicle components and displaying the vehicle data with a timeline component of a user interface during playback of the vehicle run, the timeline component comprising a portion in the user interface and the portion being configurable with a start time and an end time. The vehicle data may comprise timing data for at least one of the data topics and the vehicle data may be displayed at points in time along the portion in the user interface corresponding to the timing data.

Systems and methods for visualizing vehicle data

According to one aspect, an autonomous vehicle that includes a lidar unit collects lidar point cloud data that includes false returns or false positives, and characterizes the data associated with the false returns or false positives as drivable or not drivable. The false returns or false positives may be phantom points that are not associated with actual objects which may pose collision risks. Analyzing lidar point cloud data to characterize false returns or false positives as either drivable or not drivable enables an autonomous vehicle to operate efficiently by not having to avoid non-existent collision risks. False positives may be indicated when a wet or icy road surface acts as a mirror which reflects objects, and when precipitation such as raindrops appear as objects. Characterizing such false positives as drivable facilitates the efficient operation of an autonomous vehicle as the autonomous vehicle may drive over a mirror and/or through precipitation.

Methods and apparatus for characterizing point cloud data for autonomous vehicle systems

According to one aspect, a relatively low-cost sensor for use on an autonomous vehicle is capable of detecting moving objects in a range or a zone that is between approximately 80 meters and approximately 300 meters away from the autonomous vehicle. A substantially single fan-shaped light beam is scanned for a full 360 degrees in azimuth. Using frequency-modulated-continuous-wave (FMCW) or phase coded modulation on the beam, with back end digital signal processing (DSP), moving objects may effectively be distinguished from a substantially stationary background.

Single beam digitally modulated lidar for autonomous vehicle distance sensing

Techniques are disclosed for executing a data processing pipeline. The techniques may include receiving a job at a data pipeline queue, setting up one or more distributed processing environments, and allocating the job to one of the distributed processing environments. The techniques may further include receiving the allocated job at a job queue within the distributed processing environment, increasing a priority level of the job, and executing the job within the distributed processing environment. The techniques can further include providing a retry pipeline at the data processing pipeline, and re-executing the job at a stage following a failure of at least one of its components. The techniques may decrement the retry budget as the job is re-executed.

Data processing pipeline error recovery

A system is presented in accordance with aspects of the present disclosure. In various embodiments, the system includes a light source configured to emit light, an emitting lens positioned to obtain the emitted light and configured to produce a shaped beam, an optical element positioned to obtain the shaped beam and redirect the shaped beam toward a near field object to produce scattered light from the near field object, and to obtain and redirect at least a portion of the scattered light, and a collection lens configured to focus the at least the portion of the scattered light on a light detector.

LiDAR for vehicle blind spot detection

Techniques are disclosed for re-executing a data processing pipeline following a failure of at least one of its components. The techniques may include a syntax for defining a compute graph associated with the data processing pipeline and receiving such a compute graph in association with a specific data processing pipeline. The technique may include executing the data processing pipeline, determining that a component of the data processing pipeline failed, and determining a portion of the data processing pipeline to execute/re-execute based at least in part on dependencies defined by the data processing pipeline in association with the failed component. Re-executing the one or more components may comprise retrieving an output saved in association with a component upon which the failed component depends.

Growing community of inventors

San Jose, CA, United States of America

Hao Li