Motional Ad LLC

Irobot Corporation

Paul Schmitt

Ellen B Cargill

John P O'Brien

Douglas Dell'Accio

Flavia Pastore

Benjamin Pheil

Jugal Buddhadev

Alex Hunter Lang

Yunpeng Xu

Alok Sharma

Cristhian Guillermo Lizarazo Jimenez

Rajesh K Venkateswaran

Luke Fletcher

Michael Lee

Matthew Andromalos

Emilio Frazzoli

Philipp Robbel

Yimu Wang

Radboud Duintjer Tebbens

Amitai Bin-Nun

Noushin Mehdipour

Maria Antoinette Meijburg

Ji Hyun Jeong

Provided are methods for autonomous vehicle yielding, which can include obtaining sensor data associated with an environment and obtaining a rule indicative of a target expressive operation. Some methods described also include determining whether the sensor data meets a first criterion, applying the rule in response to the sensor data meeting the first criterion, evaluating a first trajectory of the autonomous vehicle, and selecting a second trajectory. Systems and computer program products are also provided.

Autonomous vehicle yielding

In an embodiment, a method includes: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

Emergency vehicle detection system and method

Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

Communicating vehicle information to pedestrians

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

Vehicle location using combined inputs of redundant localization pipelines

An evacuation station for collecting debris from a cleaning robot includes a controller configured to execute instructions to perform one or more operations. The one or more operations includes initiating an evacuation operation such that an air mover draws air containing debris from the cleaning robot, through an intake of the evacuation station, and through a canister of the evacuation station and such that a receptacle received by the evacuation station receives at least a portion of the debris drawn from the cleaning robot. The one or more operations includes ceasing the evacuation operation in response to a pressure value being within a range. The pressure value is determined based at least in part on data indicative of an air pressure, and the range is set based at least in part on a number of evacuation operations initiated before the evacuation operation.

Control of evacuation stations

A method for operating a vehicle includes detecting a traffic light located at a first spatiotemporal location based on a first digital video stream captured by a first camera and a second digital video stream captured by a second camera. It is determined that the vehicle is located at a second spatiotemporal location by validating first location data received from sensors against second location data obtained by filtering the first location data. It is determined that the traffic light is expected at the first spatiotemporal location based on a semantic map referenced by the second spatiotemporal location. Responsive to determining that the traffic light is expected at the first spatiotemporal location, a traffic signal of the traffic light is detected based on the two digital video streams. A trajectory is determined in accordance with the traffic signal. A control circuit operates the vehicle in accordance with the trajectory.

Traffic light detection system for vehicle

In an embodiment, a method comprises: capturing, by one or more microphone arrays of a vehicle, sound signals in an environment; extracting frequency spectrum features from the sound signals; predicting, using an acoustic scene classifier and the frequency spectrum features, one or more siren signal classifications; converting the one or more siren signal classifications into one or more siren signal event detections; computing time delay of arrival estimates for the one or more detected siren signals; estimating one or more bearing angles to one or more sources of the one or more detected siren signals using the time delay of arrival estimates and a known geometry of the microphone array; and tracking, using a Bayesian filter, the one or more bearing angles. If a siren is detected, actions are performed by the vehicle depending on the location of the emergency vehicle and whether the emergency vehicle is active or inactive.

Detection and classification of siren signals and localization of siren signal sources

In an embodiment, a method comprises: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

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Merrimack, NH, United States of America

Paul Schmitt