Lyft, Inc.

Waymo LLC

Google Inc.

Woven by Toyota, U.s., Inc.

David Tse-Zhou Lu

Brian Douglas Cullinane

Ying Liu

Vinay Shet

YooJung Ahn

Sameer Qureshi

Calvin Karl Johnson

Weiyi Hou

Shaojing Li

Asif Haque

Emilie Jeanne Anne Danna

Shaohui Sun

Lina Dong

Ivan Kirigin

Robert Charles Kyle Pinkerton

Somesh Khandelwal

Philip Nemec

Albert Shane

Renaud-Roland Hubert

Min Li Chan

Anne Kristina Aula

Christopher Kennedy Ludwick

Gerard Joyce

Hassan Rashid Mir

Isaac Lopes Nogueras

Mark Douglas Huyler

Adam Lee Frost

Philippe Robert Agostino Holland

Marcos Aniceto Villalón Caro

Nathaniel Fairfield

Joshua Seth Herbach

Kumar Hemachandra Chellapilla

Jennifer Arden

Anne Kristiina Aula

Alexis Weill

Suneet Rajendra Shah

Ranjith Unnikrishnan

Daxiao Liu

Sheng Yang

Ivan Kirigan

Luc Vincent

Luca Del Pero

Christy Fernandez Cull

Jonathan Mazur Dyer

Charles J Richey

Yoojung Ahn

Issac Lopes Nogueras

Systems, methods, and non-transitory computer-readable media can access a plurality of schema-based encodings providing a structured representation of an environment captured by one or more sensors associated with a plurality of vehicles traveling through the environment. The plurality of schema-based encodings can be clustered into one or more clusters of schema-based encodings. At least one scenario associated with the environment can be determined based at least in part on the one or more clusters of schema-based encodings.

Approaches for encoding environmental information

Embodiments provide a hardware-based mechanism to continuously calibrate a set of sensors on a vehicle while the vehicle is in motion or in use. Specifically, each sensor is mounted on a respective rigid mounting device placed on the vehicle. A secondary sensing device for calibration is also rigidly mounted on the mounting device such that the relative position between the secondary device and the camera sensor can remain unchanged. The secondary sensing devices can then be used to detect a relative distance and/or position of the same calibration target. The sensed data is then used to determine, via stereo triangulation, the relative positions of the two secondary sensing devices, which in turn indicates the relative positions of the sensors.

Systems and methods for calibration of sensors on a vehicle

Systems, methods, and non-transitory computer-readable media can determine a first utility metric associated with a region and first autonomous vehicle eligibility criteria, wherein the first utility metric is determined based on a first plurality of rides and a subset of the first plurality of rides that can be successfully executed within the region based on the first autonomous vehicle eligibility criteria. A second utility metric associated with the region and second autonomous vehicle eligibility criteria can be determined, wherein the second utility metric is determined based on a second plurality of rides and a subset of the second plurality of rides that can be successfully executed within the region based on the second autonomous vehicle eligibility criteria. An autonomous vehicle associated with the first autonomous vehicle eligibility criteria can be selected to drive in the region based on a comparison of the first utility metric and the second utility metric.

Systems and methods for autonomous vehicle performance evaluation

Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating an environment over a period of time. Information describing one or more agents associated with the environment during the period of time can be determined based at least in part on the captured sensor data. A schema-based encoding describing the environment during the period of time can be generated based at least in part on the determined information and a scenario schema, wherein the schema-based encoding provides a structured representation of the environment during the period of time.

A method includes generating, while a vehicle is operating in an autonomous-driving mode, a planned trajectory associated with a computing system of the vehicle based on first sensor data capturing an environment of the vehicle. The method further includes, while the vehicle is operating according to the planned trajectory, receiving a disengagement instruction associated that causes the vehicle to disengage from operating in the autonomous-driving mode and switch to operating in a disengagement mode. Subsequent to the vehicle operating in the disengagement mode, the method further includes capturing second sensor data and generating a simulation of the environment. The simulation is based on sensor data associated with the environment and the planned trajectory. Additionally, subsequent to the vehicle operating in the disengagement mode, the method concludes with evaluating a performance of an autonomy system based on the simulation, and providing feedback based on the evaluation.

Vehicle disengagement simulation and evaluation

Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating an environment over a period of time. Information describing one or more agents associated with the environment during the period of time can be determined based at least in part on the captured sensor data. A parameter-based encoding describing the one or more agents associated with the environment during the period of time can be generated based at least in part on the determined information and a scenario schema, wherein the parameter-based encoding provides a structured representation of the information describing the one or more agents associated with the environment. A scenario represented by the parameter-based encoding can be determined based at least in part on a cluster of parameter-based encodings to which the parameter-based encoding is assigned.

Aspects of the disclosure relate to vehicles capable of autonomous driving. These vehicles may include seats for accommodating different numbers of passengers. In some cases, the seats may be reconfigurable. For instance, a vehicle may include a first row of seating having a first, passenger use configuration. The first row may allow a passenger to sit in a seat of the first row of seating and access user input controls for the vehicle. The first row of seating may also have a second, folded configuration where the first row is in a folded configuration and no longer usable for passenger seating. The vehicle may also have a second row of seating. When the first row of seating is in the second, folded configuration, the second row of seating may still be usable for seating and may include additional legroom for the passenger as compared to when the first row of seating is in the first passenger use configuration.

Second row priority seating for vehicles

Systems, methods, and non-transitory computer-readable media can receive transportation information associated with a transportation request, the transportation information comprising a pick up location and a drop off location. A first route associated with the transportation request and a non-autonomous vehicle can be determined. A second route associated with the transportation request and an autonomous vehicle can be determined based on an operating design domain (ODD) associated with one or more autonomous vehicles in a fleet of vehicles. At least one performance metric associated with the second route can be determined. The second route can be selected based at least in part on the at least one performance metric and a comparison of the first route and the second route. An autonomous vehicle from the fleet of vehicles can be assigned to the transportation request based on selection of the second route.

Systems, methods, and non-transitory computer-readable media can determine a road segment. A set of features associated with the road segment can be determined based at least in part on data captured by one or more sensors of a vehicle. A level of similarity between the road segment and each of a set of road segment types can be determined by comparing the set of features to features associated with each of the set of road segment types. The road segment can be classified as a road segment type based on the level of similarity. Scenario information associated with the road segment can be determined based on the classified road segment type.

Road segment similarity determination

Systems, methods, and non-transitory computer-readable media can access a plurality of parameter-based encodings providing a structured representation of an environment captured by one or more sensors associated with a plurality of vehicles traveling through the environment. A given parameter-based encoding of the environment identifies one or more agents that were detected by a vehicle within the environment and respective location information for the one or more agents within the environment. The plurality of parameter-based encodings can be clustered into one or more clusters of parameter-based encodings. At least one scenario associated with the environment can be determined based at least in part on the one or more clusters of parameter-based encodings.

Aspects of the present disclosure relate to a vehicle for maneuvering a passenger to a destination autonomously. The vehicle includes one or more computing devices and a set of user input buttons for communicating requests to stop the vehicle and to initiate a trip to the destination with the one or more computing devices. The set of user input buttons consisting essentially of a dual-purpose button and an emergency stopping button different from the dual-purpose button configured to stop the vehicle. The dual-purpose button has a first purpose for communicating a request to initiate the trip to the destination and a second purpose for communicating a request to pull the vehicle over and stop the vehicle. The vehicle has no steering wheel and no user inputs for the steering, acceleration, and deceleration of the vehicle other than the set of user input buttons.

Autonomous vehicles

Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating a road segment. A plurality of features describing the road segment can be extracted from the sensor data. A map representation of the road segment can be determined based at least in part on the sensor data and the plurality of features extracted from the sensor data, the map representation being determined as the vehicle navigates the road segment. While the map representation of the road segment is being determined, at least one scenario associated with the road segment can be determined based at least in part on the map representation and the plurality of features extracted from the sensor data.

Mapping and determining scenarios for geographic regions

Aspects of the present disclosure relate to a vehicle having one or more computing devices that may receive instructions to pick up a passenger at a pickup location and determine when the vehicle is within a first distance of the pickup location. When the vehicle is within the first distance, the computing devices may make a first attempt to find a spot to park the vehicle and wait for the passenger. When the vehicle is unable to find a spot to park the vehicle on the first attempt, the computing devices may maneuvering the vehicle in order to make a second attempt to find a spot to park the vehicle and wait for the passenger. When the vehicle is unable to find a spot to park the vehicle on the second attempt, the computing devices may stop the vehicle in a current lane to wait for the passenger.

Attempting to pull over for autonomous vehicles

Systems, methods, and non-transitory computer-readable media can receive disengagement information associated with one or more autonomous vehicles, the disengagement information identifying a plurality of disengagements of an autonomy system during operation of the one or more autonomous vehicles. Each disengagement of the plurality of disengagements can be categorized based on a plurality of categories, wherein a first category of the plurality of categories is associated with disengagement that would not have led to a negative outcome. A performance metric associated with the one or more autonomous vehicles can be determined based on the categorizing each disengagement of the plurality of disengagements. Autonomous vehicle performance of the one or more autonomous vehicles can be evaluated based on the performance metric.

Growing community of inventors

Menlo Park, CA, United States of America

David Tse-Zhou Lu