Waymo LLC

Brandyn White

Simon Verghese

Caner Onal

Brendan Hermalyn

David Schleuning

Ury Zhilinsky

Alex Mccauley

Ralph Hamilton Shepard

Alexander Patrick McCauley

Nicholas Armstrong-Crews

Sean Rafferty

Congyu Gao

Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

Automatic labeling of objects in sensor data

The present disclosure relates to systems and methods that provide both an image of a scene and depth information for the scene. An example system includes at least one time-of-flight (ToF) sensor and an imaging sensor. The ToF sensor and the imaging sensor are configured to receive light from a scene. The system also includes at least one light source and a controller that carries out operations. The operations include causing the at least one light source to illuminate at least a portion of the scene with illumination light according to an illumination schedule. The operations also include causing the at least one ToF sensor to provide information indicative of a depth map of the scene based on the illumination light. The operations additionally include causing the imaging sensor to provide information indicative of an image of the scene based on the illumination light.

Hybrid time-of-flight and imager module

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a simulation of an environment that is being interacted with by a plurality of agents over a plurality of time steps, wherein the simulation comprises a respective simulation state for each time step that specifies a respective state of each agent at the time step. In one aspect, a method comprises, for each time step: obtaining a current simulation state for the current time step; generating a plurality of candidate next simulation states for a next time step; determining, for each candidate next simulation state, a discriminative score characterizing a likelihood that the candidate next simulation state is a realistic simulation state; and selecting a candidate next simulation state as the simulation state for the next time step based on the discriminative scores for the candidate next simulation states.

Multi-agent simulations

The present disclosure relates to systems and methods that provide information about a scene based on a time-of-flight (ToF) sensor and a structured light pattern. In an example embodiment, a sensor system could include at least one ToF sensor configured to receive light from a scene. The sensor system could also include at least one light source configured to emit a structured light pattern and a controller that carries out operations. The operations include causing the at least one light source to illuminate at least a portion of the scene with the structured light pattern and causing the at least one ToF sensor to provide information indicative of a depth map of the scene based on the structured light pattern.

Time-of-flight sensor with structured light illuminator

The present disclosure relates to systems and methods that utilize machine learning techniques to improve object classification in thermal imaging systems. In an example embodiment, a method is provided. The method includes receiving, at a computing device, one or more infrared images of an environment. The method additionally includes, applying, using the computing device, a trained machine learning system on the one or more infrared images to determine an identified object type in the environment by at least: determining one or more prior thermal maps associated with the environment; using the one or more prior thermal maps and the one or more infrared images, determining a current thermal map associated with the environment; and determining the identified object type based on the current thermal map. The method also includes providing the identified object type using the computing device.

Growing community of inventors

Mountain View, CA, United States of America

Brandyn White