Waymo LLC

Christian Lauterbach

Mingcheng Chen

Colin Andrew Braley

Peter Morton

Luke Wachter

Gil Shotan

Andreas Wendel

Michael R James

Nicholas Armstrong-Crews

Craig Lewin Robinson

Daniel L Rosenband

Christopher Kennedy Ludwick

Scott McCloskey

Benjamin Pitzer

Etai Bruhis

Alexander Zbrozek

Pieter Kapsenberg

Xiaoxiang Hu

Ryan Joseph Andrade

Emily Chi

Zhuyuan Liu

Ming Zou

Blaise Gassend

Michael Steven Montemerlo

Mark Alexander Shand

Carlos Hernandez Esteban

Volker Grabe

Clayton Kunz

Roshni Cooper

Nathaniel Quillin

Ian Mahon

Sachithra Hemachandra

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

The technology relates to tracking objects in an environment around an autonomous vehicle. A computing system of the autonomous vehicle determines accurate motion characteristics of objects detected in its environment despite various sensor measurement limitations. By correcting motion distortion for fast moving objects and accounting for discrepancies in sensor data gathering, motion characteristics may be determined for the detected objects with enhanced accuracy. Multiple sets of correspondences are determined for clusters from multiple sensor spins, enabling better alignment using a surface matching algorithm even when clusters have fewer data points. Efficiency is also enhanced by selecting hypotheses based on confidence levels. These techniques provide for identifying the types of objects for which a yaw rate can be accurately determined. Object classification can also be improved by accumulating associated clusters corresponding to a detected object. In addition, under- or over-segmentation can be mitigated with such techniques.

Onboard cluster tracking system

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 training and using a model for verifying accuracy of ICP alignments or alignments between data points using an iterative closest point algorithm. For instance, a model may be trained using ICP alignment data, including alignments between an object appearing in LIDAR sensor frames. The training may also include setting a definition for a trusted ICP alignment. In this regard, the model may be trained such that, n response to receiving additional LIDAR sensor frames and corresponding additional ICP alignment data, output a value indicative of whether the additional ICP alignment data is trusted according to the definition. The model may then be used to control a vehicle in an autonomous driving mode by determining whether alignment data for object determined using the ICP algorithm should be trusted.

Verification of iterative closest point alignments for autonomous vehicles

Example embodiments relate to calibration and localization of a light detection and ranging (lidar) device using a previously calibrated and localized lidar device. An example embodiment includes a method. The method includes receiving, by a computing device associated with a second vehicle, a first point cloud captured by a first lidar device of a first vehicle. The first point cloud includes points representing the second vehicle. The method also includes receiving, by the computing device, pose information indicative of a pose of the first vehicle. In addition, the method includes capturing, using a second lidar device of the second vehicle, a second point cloud. Further, the method includes receiving, by the computing device, a third point cloud representing the first vehicle. Yet further, the method includes calibrating and localizing, by the computing device, the second lidar device.

Calibration and localization of a light detection and ranging (lidar) device using a previously calibrated and localized lidar device

The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Localization initialization for autonomous vehicles

The technology relates to partially redundant equipment architectures for vehicles able to operate in an autonomous driving mode. Aspects of the technology employ fallback configurations, such as two or more fallback sensor configurations that provide some minimum amount of field of view (FOV) around the vehicle. For instance, different sensor arrangements are logically associated with different operating domains of the vehicle. Fallback configurations for computing resources and/or power resources are also provided. Each fallback configuration may have different reasons for being triggered, and may result in different types of fallback modes of operation. Triggering conditions may relate, e.g., to a type of failure, fault or other reduction in component capability, the current driving mode, environmental conditions in the vicinity of vehicle or along a planned route, or other factors. Fallback modes may involve altering a previously planned trajectory, altering vehicle speed, and/or altering a destination of the vehicle.

Redundant hardware system for autonomous vehicles

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sensor calibration with environment map. In some implementations, a three-dimensional surfel representation of a real-world environment is obtained. One or more surfels of the surfel representation having a particular classification of the different classifications are selected. Input sensor data from one or more sensors installed on an autonomous or semi-autonomous vehicle are received. The input sensor data is compared to the surfel representation to identify one or more differences between the observation and the surfel representation. At least one sensor of the one or more sensors is calibrated using the one or more differences between the observation and the surfel representation.

Sensor calibration with environment map

A system includes a light detection and ranging device configured to generate, for each respective point of a plurality of points in an environment, a corresponding waveform that represents physical characteristics of the respective point. The system also includes a signal processor configured to determine, based on the corresponding waveform of each respective point, a map of the environment that includes a representation of a corresponding position of the respective point. The system additionally includes an embedding model configured to determine, for each respective point and based on the corresponding waveform, a corresponding vector comprising a plurality of values representative of the physical characteristics of the respective point. The system further includes a feature detector configured to detect or classify a physical feature based on (i) the corresponding positions of one or more points of the plurality of points and (ii) the corresponding vectors of the one or more points.

Laser waveform embedding

Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.

Computation of the angle of incidence of laser beam and its application on reflectivity estimation

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

Campbell, CA, United States of America

Christian Lauterbach