Waymo LLC

Google Inc.

Peter Morton

Christian Lauterbach

Nicholas Armstrong-Crews

Mingcheng Chen

Scott McCloskey

Benjamin Pitzer

Jiajun Zhu

Jonathan Baldwin Dowdall

Lo Po Tsui

Nafees Ahmed

The technology relates to autonomous vehicles having articulating sections such as the trailer of a tractor-trailer. Aspects include approaches for tracking the pose of the trailer, including its orientation relative to the tractor unit. Sensor data is analyzed from one or more onboard sensors to identify and track the pose. The pose information is usable by on-board perception and/or planning systems when driving the vehicle in an autonomous mode. By way of example, on-board sensors such as Lidar sensors are used to detect the real-time pose of the trailer based on Lidar point cloud data. The orientation of the trailer is estimated based on the point cloud data, and the pose is determined according to the orientation and other information about the trailer. Aspects also include determining which side of the trailer the sensor data is coming from. A camera may also detect trailer marking information to supplement the analysis.

Lidar-based trailer tracking

The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Model for excluding vehicle from sensor field of view

Aspects of the disclosure relate to generating a grid or a visual list to facilitate operator review of labels. The system receives a first set of labels generated by a first labeling source and a second set of labels generated by a second labeling source. The first set of labels and the second set of labels each classify one or more objects perceived in one or more scenes captured by a sensor of a vehicle, such that each of the one or more objects has a corresponding first label and a corresponding second label. The system determines discrepancies between the corresponding first label and the corresponding second label for each of the one or more objects, and generates a grid or a visual list using the determined discrepancies. The system provides the grid or the visual list for display to a human operator.

Perception visualization tool

An autonomous vehicle configured to detect and avoid pedestrians may use information from LIDAR or other range-based sensors. An example method involves: (a) receiving, at a computing device, range data corresponding to a plurality of objects in an environment of a vehicle, wherein the range data comprises a plurality of first data points; (b) generating a spherical data set comprising a plurality of second data points, wherein spherical coordinates for each second data point are generated based on a corresponding one of the first plurality of data points; and (c) determining a two-dimensional map based on the spherical data set comprising the plurality of second data points, wherein the two-dimensional map comprises a plurality of pixels, wherein each pixel of the plurality of pixels is indicative of a plurality of parameters corresponding to the plurality of objects in the environment.

Methods and systems for pedestrian avoidance using LIDAR

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Mountain View, CA, United States of America

Peter Morton