Toyota Motor Engineering & Manufacturing North America, Inc.

Waymo LLC

Rxsafe LLC

Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Michael R James

William K Holmes

Thomas Francis Gaasch

Michael Edward Samples

Christopher Kennedy Ludwick

Ryan Joseph Andrade

Emily Chi

David Harrison Silver

Katsuhiro Sakai

Jens-Steffen Ralf Gutmann

Xin Zhou

Avdhut S Joshi

Roshni Cooper

Danil V Prokhorov

Joshua Seth Herbach

Edwin B Olson

Ioan-Alexandru Sucan

Andrew Barton-Sweeney

Misha Balakhno

Andreas Wendel

Nobuhide Kamata

Michael Steven Montemerlo

Robert J Chen

Daniel L Rosenband

Christian Lauterbach

Etai Bruhis

Alexander Zbrozek

Pieter Kapsenberg

Ryan M Eustice

Yuchi Wang

Zhuyuan Liu

Derrick Dominic

Samuel Panzer

Dmitri A Dolgov

Toshiki Kindo

Masahiro Harada

Bunyo Okumura

Kuan-Hui Lee

Kenneth P Laberteaux

Michael James Delp

Derek Stanley Caveney

Ryan W Wolcott

Steven F Kalik

Benjamin Pitzer

Yusuke Kanzawa

Lorenzo Caminiti

Tomoki Nishi

Matthew Derry

Jeff B Rogers

Boris Sofman

Thomas F Gaasch

The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.

Road condition deep learning model

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

Systems for implementing fallback behaviors for autonomous vehicles

An example method involves identifying one or more potential route segments that collectively connect at least two geographical points, receiving spatiotemporal weather information that predicts future weather conditions along each of the potential segments, and, for each potential segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential segment. The method also involves selecting, based on a minimization of a total cost function, a set of selected segments and corresponding segment target speeds for the vehicle to utilize while traversing between the at least two geographical points so as to avoid adverse weather conditions, the total cost function being the sum of partial cost functions associated with a set of segments that collectively connect the at least two geographical points.

Speed and route planning in view of weather

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

Detecting general road weather conditions

Aspects of the disclosure relate to generating a puddle occupancy grid including a plurality of cells. For instance, a first probability value for a puddle being located at a first location generated using sensor data from a first sensor may be received. A second probability value for a puddle being located at a second location generating using sensor data from a second sensor different from the first sensor may be received. A first cell may be identified from the plurality of cells using the first location. The first cell may also be identified using the second location. A value for the cell may be generated using the first probability value and the second probability value.

Puddle occupancy grid 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

Aspects of the disclosure provide for localizing a vehicle. In one instance, a weather condition in which the vehicle is currently driving may be identified. A plurality of sensor inputs including intensity information, elevation information, and radar sensor information may be received. For each of the plurality of sensor inputs, an alignment score is determined by comparing the intensity information, elevation information, and radar sensor information to a corresponding pre-stored image for each of the intensity information, the elevation information, and the radar sensor information. A set of weights for the plurality of sensor inputs may be determined based on the identified weather condition. The alignment scores may then be combined using the set of weights in order to localize the vehicle.

Localization adaptation based on weather estimation

A pharmaceutical storage and retrieval system and a method of storing and retrieving pharmaceutical containers from the system. The system includes a pharmaceutical storage and retrieval and a controller operatively coupled to the device to control storage and retrieval functions of the device. The device includes a gantry assembly, a shelving assembly, a user access assembly, and a user authorization system that function in a coordinated manner to carry out the storage and retrieval functions of the device.

Pharmaceutical storage and retrieval system and methods of storing and retrieving pharmaceuticals

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Dearborn, MI, United States of America

Michael R James