Lyft, Inc.

Yahoo! Inc.

Excalibur Ip, LLC

Microsoft Technology Licensing, LLC

Linkedin Corporation

Frank Taehyun Yoo

Jisi Guo

Marc Haumann

Craig Dehner

Patrick Steven Wong

Karina Van Schaardenburg

Eli Guerron

Henrique Dias Penha

Linda L Dong

Taggart Matthiesen

Robert Earl Rasmusson, Jr

John Robert Tighe

Matt Vitelli

Steven Lawrence Brooks

David White

Tapan V Bhat

Michael Hans Mueller

Harrison Joel Bowden

Zachary Alistair Cole

Jonathan Aaron Tourtellot

Jay Burke Culligan

Lev Timourovich Popov

Chris Lambert

Logan Green

David Dryjanski

Maxamilion Ab Gustofson

Michelle Venetucci Harvey

Paul Alex Broman

Tim Rechin

Jessi Dong

Majd Taby

Jon Sweet

Sean Beausoleil

Sebastian Rolf Johan Brannstrom

Steven Flory

Evan Goldin

Chia Hsieh

David Ikuye

Burke Culligan

Eduardo Alfio Perez Rico

Sam Huskins

Luke Wroblewski

Oleg Vadim Panichev

Brandon Souba

Molly Angelica Ingles Lorenzo

Prasanna Padmanabhan

David Florian Kayode Ikuye

John Greene

Shannyn Timrott

Paul Thurlow

James Kittock

Kent Brewster

Michael Li Wang

Michael Shannon Potter

Linzi Renee Berry

Nikolas Laufer-Edel

Lin Wang

Martin Conte Mac Donell

Maxamillion Ab Gustofson

The present disclosure relates to providing a dynamic graphical user interface for efficiently presenting users with relevant ride information throughout the fulfilment of a ride request. In some embodiments, the system detects a trigger event during a ride, and based on detecting the trigger event, the system expands or collapses an information portion within a graphical user interface. When in a collapsed state, for example, the information portion of the graphical user interfaces includes a first set of content. Upon detecting a trigger event, the system dynamically expands the information portion to provide a second set of content that includes information associated with the detected trigger.

Optimizing transportation networks through dynamic user interfaces

In one embodiment, a method includes determining a plurality of available locations for a vehicle to pick up or drop off a user in an area based on sensor data that is captured by the vehicle and is associated with physical characteristics of the area. The method includes calculating a viability value for each of the plurality of available locations. The viability value is calculated based on the physical characteristics of the area as determined from the sensor data. The method includes ranking the plurality of available locations according to the viability values of the plurality of available locations. An available location with a higher viability value is ranked above an available location with a lower viability value. The method includes sending instructions to present, on a computing device, one or more selectable locations of the plurality of available locations based on the ranking.

Evaluating and presenting pick-up and drop-off locations in a situational awareness view of an autonomous vehicle

In one embodiment, a method includes receiving sensor data associated with an object external to a vehicle. The sensor data includes a sequence of data packets. Each data packet corresponds to a time frame in a sequence of time frames. The method includes determining a classification of the object based at least on the sensor data, determining that a particular data packet corresponding to a particular time frame in the sequence of time frames is corrupt or missing, generating a replacement data packet based on one or more data packets that correspond to one or more time frames adjacent to the particular time frame, generating a sequence of visual representations of the object corresponding to the sequence of time frames. At least one visual representation in the sequence of visual representations corresponding to the particular time frame is generated based on the replacement data packet.

Identifying objects for display in a situational-awareness view of an autonomous-vehicle environment

In one embodiment, a method includes receiving sensor data from a sensor array of a vehicle while traveling on a road. The method includes determining a confidence score for a classification of an object based on the sensor data. The method includes generating an object graphic corresponding to the object based on the confidence score for the classification of the object. The method includes retrieving, from one or more third-party systems, third-party data associated with an environment in proximity to the object and associated with the road based on the classification. The method includes generating an overlay graphic corresponding to the environment in proximity to the object based on the third-party data. The method includes providing for display the object graphic rendered in association with the overlay graphic corresponding to the environment.

Rendering a situational-awareness view in an autonomous-vehicle environment

Display screen or portion thereof with graphical user interface

In one embodiment, a method includes sending a set of instructions to present, on a computing device, one or more available locations for a vehicle to pick-up or drop-off a user in an area. The one or more available locations are based on sensor data of the area that is captured by the vehicle. The method includes receiving a user selection to select a location associated with the area for the vehicle to pick-up or drop-off the user. The method includes adjusting a viability value of one or more locations to pick-up or drop-off the user. The viability value is adjusted based at least on the selected location. The method includes, based on the adjusted viability value of the one or more locations, determining a location from the one or more locations. The method includes instructing the vehicle to travel to the determined location.

In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

A system for determining a dispatch includes an input interface, a processor, and an output interface. The input interface is to receive a request for a first pickup including a first pickup location and a first destination. The processor is configured to determining a driver to dispatch to the first pickup location. The output interface is to provide a first pickup indication to the driver to go to the first pickup location. The input interface is further to receive a first pickup arrival indication indicating the driver arrived at the first pickup location. The output interface is further to provide a first destination indication indicating to the driver to go to the first destination. The input interface is further to receive a request for a second pickup including a second pickup location and a second destination.

Ride chaining

A system for connecting a driver and a rider comprises a processor and an interface. The processor is configured to select a driver for a requested ride from the rider. The interface is configured to, in the event that the driver accepts the rider, provide a connect notification to the rider. The connect notification comprises an image associated with the driver, an image associated with the driver's car, and an anonymized interface for contacting the driver.

System for connecting a driver and a rider

Display screen or portion thereof with transitional graphical user interface

In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method also includes accessing secondary data associated with the object from one or more secondary-data sources that are independent of the sensor array of the autonomous vehicle. The method also includes determining, based on the autonomous-vehicle sensor data and the secondary data, an object graphic to visually represent the object. The method also includes providing for display a visual representation of the external environment of the autonomous vehicle that includes the determined object graphic to visually represent the object.

In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method further includes determining a confidence score for a classification of the object. The method further includes determining an object graphic with a level of detail to visually represent the object. The level of detail of the object graphic is based on the confidence score. The method further includes providing for display a visual representation the object graphic.

Growing community of inventors

San Francisco, CA, United States of America

Frank Taehyun Yoo