Waymo LLC

Nichola Abdo

Congcong Li

Christopher John Sweeney

Zhifeng Chen

Jonathon Shlens

Alper Ayvaci

Ruichi Yu

Junhua Mao

Edward Stephen Walker, Jr

Xu Chen

Zijian Guo

Daniel Rudolf Maurer

Xiaohan Jin

David Lee

Philipp Florian Jund

Robert William Anderson

Chang Gao

The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

Identification of spurious radar detections in autonomous vehicle applications

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting scene flow. One of the methods includes obtaining a current point cloud representing an observed scene at a current time point; obtaining object label data that identifies a first three-dimensional region in the observed scene; determining, for each current three-dimensional point that is within the first three-dimensional region and using the object label data, a respective preceding position of the current three-dimensional point at a preceding time point in a reference frame of the sensor at the current time point; and generating, using the preceding positions, a scene flow label for the current point cloud that comprises a respective ground truth motion vector for each of a plurality of the current three-dimensional points.

Generating scene flow labels for point clouds using object labels

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and, for each sensor sample, an embedding of the sensor sample; receiving a request specifying a query sensor sample, wherein the query sensor sample characterizes a query environment region; and identifying, from the collection of sensor samples, a plurality of relevant sensor samples that characterize similar environment regions to the query environment region, comprising: processing the query sensor sample through the embedding neural network to generate a query embedding; and identifying, from sensor samples in a subset of the sensor samples in the collection, a plurality of sensor samples that have embeddings that are closest to the query embedding.

Searching an autonomous vehicle sensor data repository

Systems, methods, devices, and techniques for generating object-heading estimations. In one example, methods include actions of receiving sensor data representing measurements of an object that was detected within a proximity of a vehicle; processing the sensor data with one or more preliminary heading estimation subsystems to respectively generate one or more preliminary heading estimations for the object; processing two or more inputs with a second heading estimation subsystem to generate a refined heading estimation for the object, the two or more inputs including the one or more preliminary heading estimations for the object; and providing the refined heading estimation for the object to an external processing system.

Multi-stage object heading estimation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating motion detection based on optical flow. One of the methods includes obtaining a first image of a scene in an environment taken by an agent at a first time point and a second image of the scene at a second later time point. A point cloud characterizing the scene in the environment is obtained. A predicted optical flow is determined between the first image and the second image. A respective initial flow prediction for the point that represents motion of the point between the two time points is determined. A respective ego motion flow estimate for the point that represents a motion of the point induced by ego motion of the agent is determined. A respective motion prediction that indicates whether the point was static or in motion between the two time points is determined.

Optical flow based motion detection

Growing community of inventors

Mountain View, CA, United States of America

Nichola Abdo