Waymo LLC

Xiaoxiang Hu

Mingcheng Chen

Colin Andrew Braley

Nicholas Armstrong-Crews

Luke Wachter

Christian Lauterbach

Gil Shotan

Ming Zou

Yunshan Jiang

Clayton Kunz

Grigory Dzhavadyan

Richard Chaussee

David Gallup

One example method involves obtaining a plurality of scans of a field-of-view (FOV) of a light detection and ranging (LIDAR) device disposed inside a housing. Obtaining each scan of the plurality of scans comprises: transmitting, through a plurality of sections of the housing, a plurality of light pulses emitted from the LIDAR device in different directions toward the housing; and detecting a plurality of returning light pulses comprising reflected portions of the transmitted plurality of light pulses that are reflected back toward the LIDAR device. The method also involves detecting an obstruction that at least partially occludes the LIDAR device from scanning the FOV through the housing based on the plurality of scans.

Methods and systems for detecting obstructions on a sensor housing

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to predict reflections in an environment. One of the methods includes receiving a surfel map comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment. Sensor data for one or more locations in the environment is obtained. The sensor data has been captured by one or more sensors of a vehicle. A range map that represents a projection of the surfel map is generated. Range data in the sensor data is compared to the range map to identify one or more locations in the range map that do not match the range data in the sensor data. The one or more locations in the range map that do not match the range data in the sensor data is classified as reflections.

False positive object removal with surfel maps

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling Doppler-assisted segmentation of points in a point cloud for efficient object identification and tracking in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, the one or more velocity values and the one or more coordinates obtained for the same instance of time, identifying that the set of the return points is associated with an object in an environment, and causing a driving path of the AV to be determined in view of the object.

Point cloud segmentation using a coherent lidar for autonomous vehicle applications

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling velocity estimation for efficient object identification and tracking in autonomous vehicle (AV) applications, including: obtaining, by a sensing system of the AV, a plurality of return points, each return point having a velocity value and coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying an association of the velocity values and the coordinates of return points with a motion of a physical object, the motion being a combination of a translational motion and a rotational motion of a rigid body, and causing a driving path of the AV to be determined in view of the motion of the physical object.

Velocity estimation and object tracking for autonomous vehicle applications

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling efficient object identification and tracking in autonomous vehicle (AV) applications by using velocity data-assisted mapping of first set of points obtained for a first sensing data frame by a sensing system of the AV to a second set of points obtained for a second sensing data frame by the sensing system of the AV, the first set of points and the second set of points corresponding to an object in an environment of the AV, and causing a driving path of the AV to be determined in view of the performed mapping.

Doppler-assisted object mapping for autonomous vehicle applications

The technology relates to handling of self-reflections of sensor signals off of a portion of a vehicle that is operating in an autonomous driving mode. Vehicle pose information and sensor pose information are determined at a given point in time while operating in the autonomous driving mode. Return signals from one or more scans of the environment are received from onboard sensors such as lidar sensors. The system evaluates, based on the vehicle and sensor pose information, whether a segment between a given one of the one or more sensors and a received point from a selected one of the return signals crosses any surface of a 3D model of the vehicle. The received point is identified as a self-return point. In response to identifying the received point as a self-return point, the vehicle is able to perform a driving operation in the autonomous driving mode.

Self-reflection filtering

Aspects and implementations of the present disclosure relate to filtering of return points from a point cloud based on radial velocity measurements. An example method includes: receiving, by a sensing system of an autonomous vehicle (AV), data representative of a point cloud comprising a plurality of return points, each return point comprising a radial velocity value and position coordinates representative of a reflecting region that reflects a transmission signal emitted by the sensing system; applying, to each of the plurality of return points, at least one threshold condition related to the radial velocity value of a given return point to identify a subset of return points within the plurality of return points; removing the subset of return points from the point cloud to generate a filtered point cloud; and identifying objects represented by the remaining return points in the filtered point cloud.

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Mountain View, CA, United States of America

Xiaoxiang Hu