Waymo LLC

Clayton Kunz

Pierre-yves Droz

Luke Wachter

Timothy Campbell

Andreas Wendel

Blaise Gassend

Benjamin T Ingram

Nicholas Armstrong-Crews

Christian Lauterbach

Xiaoxiang Hu

Larry Smith

Roshni Cooper

Grigory Dzhavadyan

David Gallup

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

Systems and methods are disclosed to identify a presence of a volumetric medium in an environment associated with a LIDAR system. In some implementations, the LIDAR system may emit a light pulse into the environment, receive a return light pulse corresponding to reflection of the emitted light pulse by a surface in the environment, and determine a pulse width of the received light pulse. The LIDAR system may compare the determined pulse width with a reference pulse width, and determine an amount of pulse elongation of the received light pulse. The LIDAR system may classify the surface as either an object to be avoided by a vehicle or as air particulates associated with the volumetric medium based, at least in part, on the determined amount of pulse elongation.

LIDAR pulse elongation

Example embodiments relate to radar reflection filtering using a vehicle sensor system. A computing device may detect a first object in radar data from a radar unit coupled to a vehicle and, responsive to determining that information corresponding to the first object is unavailable from other vehicle sensors, use the radar data to determine a position and a velocity for the first object relative to the radar unit. The computing device may also detect a second object aligned with a vector extending between the radar unit and the first object. Based on a geometric relationship between the vehicle, the first object, and the second object, the computing device may determine that the first object is a self-reflection of the vehicle caused at least in part by the second object and control the vehicle based on this determination.

Methods and systems for radar reflection filtering during vehicle navigation

One example system includes a first light detection and ranging (LIDAR) device that scans a first field-of-view defined by a first range of pointing directions associated with the first LIDAR device. The system also includes a second LIDAR device that scans a second FOV defined by a second range of pointing directions associated with the second LIDAR device. The second FOV at least partially overlaps the first FOV. The system also includes a first controller that adjusts a first pointing direction of the first LIDAR device. The system also includes a second controller that adjusts a second pointing direction of the second LIDAR device synchronously with the adjustment of the first pointing direction of the first LIDAR device.

Synchronization of multiple rotating sensors of a vehicle

The present disclosure relates to systems, vehicles, and methods for adjusting a pointing direction and/or a scanning region of a lidar. An example method includes determining a plurality of points of interest within an environment of a vehicle. The method also includes assigning, to each point of interest of the plurality of points of interest, a respective priority score. The method additionally includes partitioning at least a portion of the environment of the vehicle into a plurality of sectors. Each sector of the plurality of sectors includes at least one point of interest. For each sector of the plurality of sectors, the method includes adjusting a scanning region of a lidar unit based on the respective sector and causing the lidar unit to scan the respective sector.

Sensor steering for multi-directional long-range perception

Disclosed are systems and methods that may be used for determining areas free of objects around a vehicle. The systems and methods include receiving first sensor data from a first sensor having a first field of view and a first range. The systems and methods also include receiving second sensor data from a second sensor having a second field of view and a second range. The systems and methods also include determining, by a processor, a first-sensor occlusion in the first field of view. The systems and methods further include determining, by the processor, an occlusion free-region of the first field of view based on data from the second sensor. Additionally, the systems and methods include operating, by the processor, the vehicle in an autonomous mode based on determining the occlusion free-region.

Radar field of view extensions

Aspects of the disclosure relate to detecting spurious objects. For instance, a model may be trained using raining data including a plurality of LIDAR data points generated by a LIDAR sensor of a vehicle. Each given LIDAR data point includes location information and intensity information, and is associated with waveform data for that given LIDAR data point. At least one of the plurality of LIDAR data points is further associated with a label identifying spurious objects through which the vehicle is able to drive. The model and/or a plurality of heuristics may then be provided to a vehicle in order to allow the vehicle to determine LIDAR data points that correspond to spurious objects. These LIDAR data points may then be filtered from sensor data, and the filtered sensor data may be used to control the vehicle in an autonomous driving mode.

Growing community of inventors

Mountain View, CA, United States of America

Clayton Kunz