Waymo LLC

Giulia Guidi

Kimberly Geneva Toth

Choon Ping Chng

Lucian Ion

Jeremy Dittmer

Peter Avram

Cheng-Han Wu

Nirav Dharia

Ralph Hamilton Shepard

Vlad Constantin Cardei

Matthew Emanuel Last

Caner Onal

Michael Shagam

Shane McGuire

Unique Rahangdale

Roya Mirhosseini-Schubert

The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.

Perimeter sensor housings

The technology provides an interior camera sensing system for self-driving vehicles. The sensor system includes image sensors and infrared illuminators to see the vehicle's cabin and storage areas in all ambient lighting conditions. The system can monitor the vehicle for safety purposes, to detect the cleanliness of the cabin and storage areas, as well as to detect whether packages or other objects have been inadvertently left in the vehicle. The cameras are arranged to focus on selected regions in the vehicle cabin and the system carries out certain actions in response to information evaluated for those regions. The interior space is divided into multiple zones assigned different coverage priorities. Regardless of vehicle size or configuration, certain actions are performed according to various ride checklists and the imagery detected by the interior cameras. The checklists include pre-ride, mid-ride, and post-ride checklists.

Interior camera system for a self driving car

Close-in sensing camera system

Example embodiments described herein involve a system for testing a light-emitting module. The light-emitting module may include a mounting platform configured to hold a light-emitting module for a camera. The mounting platform may also be configured to rotate. The system may further include a housing holding a plurality of photodiodes arranged in an array over at least a 90 degree arc of a hemisphere. The system may also include a controller configured to control the photodiodes and the rotation of the mounting platform.

Infrared light module uniformity rotational test module

A system includes a near-infrared (NIR) illuminator, an NIR image sensor, a light detection and ranging (LIDAR) device, and control circuitry configured to perform operations. The operations include causing the NIR illuminator to illuminate a portion of an environment, and obtaining, from the NIR image sensor, NIR image data representing the portion of the environment illuminated by the NIR illuminator. The operations also include detecting a retroreflector within the NIR image data and, based on detecting the retroreflector within the NIR image, determining a position of the retroreflector within the environment. The operations further include, based on the position of the retroreflector within the environment, adjusting at least one parameter of the LIDAR device in connection with scanning the retroreflector.

Detecting retroreflectors in NIR images to control LIDAR scan

The present disclosure relates to optical systems, vehicles, and methods that are configured to illuminate and image a wide field of view of an environment. An example optical system includes a camera having an optical axis and an outer lens element disposed along the optical axis. The optical system also includes a plurality of illumination modules, each of which includes at least one light-emitter device configured to emit light along a respective emission axis and a secondary optical element optically coupled to the at least one light-emitter device. The secondary optical element is configured to provide a light emission pattern having an azimuthal angle extent of at least 170 degrees so as to illuminate a portion of an environment of the optical system.

Close-in illumination module

A system may include a first sensor of a first type and a second sensor of a second different type and having a detector. A field of view of the second sensor may be formed by a plurality of regions of interest (ROIs) defined by the detector. Control circuitry of the system may be configured to perform operations including obtaining, from the first sensor, first sensor data representing an environment, and determining, based on the first sensor data, information associated with a feature of interest within the environment. The operations may also include determining, based on the information, a particular ROI that corresponds to an expected position of the feature at a later time, obtaining a plurality of ROI sensor data from the particular ROI instead of obtaining full-resolution sensor data, and analyzing the plurality of ROI sensor data to determine one or more attributes of the feature.

Sensor region of interest selection based on multisensor data

The technology provides an interior camera sensing system for self-driving vehicles. The sensor system includes image sensors and infrared illuminators to see the vehicle's cabin and storage areas in all ambient lighting conditions. The system can monitor the vehicle for safety purposes, to detect the cleanliness of the cabin and storage areas, as well as to detect whether packages or other objects have been inadvertently left in the vehicle. The cameras are arranged to focus on selected regions in the vehicle cabin and the system carries out certain actions in response to information evaluated for those regions. The interior space is divided into multiple zones assigned different coverage priorities. Regardless of elude size or configuration, certain actions are performed according to various ride checklists and the imagery detected by the interior cameras. The checklists include pre-ride, mid-ride, and post-ride checklists.

Growing community of inventors

Mountain View, CA, United States of America

Giulia Guidi