Uber Technologies, Inc.

Carnegie Mellon University

Psychogenics Inc.

The Boeing Company

Uatc, LLC

Inmedius, Inc.

Landmark Graphics Corporation

Aurora Operations, Inc.

William Payne Ross

David LaRose

Matthew Sweeney

John Evan Bares

Michael James Aitken

Chenggang Liu

Vijay M Gondhalekar

Daniela Brunner

Emer Leahy

Benjamin Kroop

Andrew Heine

Brett Browning

John E Bares

Christopher Chandler Fromme

Mark DeLouis

Thomas E Pilarski

James F Hoburg

James Beck

Kelly Dolan

Gregory Zelesnik

David Larose

John Bares

Thomas Pilarski

Michael Aitken

A system can receive a transport request from a computing device of a requesting user, where the transport request indicates a service location. The system can determines a preliminary route for the transport request, and select an autonomous vehicle from a fleet of autonomous vehicles operating in the given area to service the transport request based on determining that the selected autonomous vehicle is capable of traversing the preliminary route without intervention.

Systems and methods for managing an autonomous vehicle transport service

A transport arrangement system operates to receive a transport request from a user, and to make a selection of a vehicle type for the user based at least in part on a set of criteria associated with the transport request or user information. For example, the determination of whether an autonomous vehicle is to be provided can be based at least in part on the destination specified with the transport request.

Selecting vehicle type for providing transport

Sensor information is collected from human driven vehicles which are driven in a given region. From the sensor information, a road condition is detected on a road segment, where the road condition has a sufficiently high likelihood of impairing autonomous vehicles in safely navigating through the one or more road segments. Information about the one or more road segments is communicated to the one or more autonomous vehicles.

Autonomous vehicle operated with guide assistance of human driven vehicles

A system can analyze a live sensor view of a self-driving vehicle (SDV) in accordance with a safety threshold to detect objects of interest along a route and classify each detected object of interest. When the safety threshold is not met, the system can transmit a teleassistance inquiry using LIDAR data to a backend computing system. When a certainty threshold is not met for an object of interest, the system can transmit a different teleassistance inquiry using image data to the backend computing system.

Teleassistance data prioritization for self-driving vehicles

An autonomous vehicle (AV) performance optimization system can determine a set of performance metrics for determining AV performance. The system can receive AV performance data from AVs operating or configured for operation throughout a given region. Based on the AV performance data, the system can determine a set of deficient performance metrics in which the AV does not meet one or more performance thresholds of a set of performance metrics. The system may then generate a configuration package, executable by the AV, comprising a set of control system parameter adjustments for meeting or exceeding the one or more performance thresholds corresponding to the deficient performance metrics.

Autonomous vehicle performance optimization system

An event is detected that impairs a confidence level of the autonomous vehicle in progressing through a current route. In response to detecting the event, the autonomous vehicle communicates information about the event to a remote source of guidance. The autonomous vehicle can receive instructions from the remote source of guidance on how to handle the event. The autonomous vehicle can then implement the instructions to handle the event while it operates.

Providing remote assistance to an autonomous vehicle

A self-driving vehicle can operate by analyzing a live sensor view to autonomously operate acceleration, braking, and steering systems of the SDV along a current route. Based on a detected anomaly associated with the live sensor view, the SDV can transmit a teleassistance inquiry to a backend transport system in accordance with a data prioritization scheme. The SDV can receive a resolution response from the backend transport system based on the teleassistance inquiry, and proceed in accordance with the resolution response.

An autonomous vehicle (AV) tracking and updating system tracks locations of AVs traveling throughout a given region. Using a stored network resource map, the system identifies a number of network-limited areas in the given region, and determines when respective AVs will enter one of the network-limited areas. In response, the system transmits a set of configuration commands to a number of proximate AVs to establish a mesh network with the respective AVs.

Autonomous vehicle mesh networking configuration

An autonomous vehicle (AV) can include a communication system to communicate with a backend system, a sensor system to collect sensor data representing an operational environment of the AV, and a control system that can processes the sensor data to perform a localization operation to determine a location and an orientation of the AV within a given region, and autonomously operate the AV's acceleration, braking, and steering systems throughout the given region. Based on the localization operation, the AV can implement a set of configuration commands to configure the communication system to transmit and receive data with the backend system using one or more specified network nodes.

Autonomous vehicle communication configuration system

A self-driving vehicle (SDV) can analyze a live sensor view to autonomously operate acceleration, braking, and steering systems of the SDV along a current route. The SDV can identify an indeterminate object in the live sensor view, and encoding sensor data identifying the indeterminate object for transmission to a backend transport system over one or more networks. The SDV may then receive a resolution response from the backend transport system to resolve the indeterminate object, and cause the SDV to proceed in accordance with the resolution response.

Teleassistance data encoding for self-driving vehicles

A control system of a self-driving vehicle (SDV) can dynamically determine a set of autonomous driving actions to be performed by the SDV, and generate a set of intention outputs using a light output system of the SDV based on the set of autonomous driving actions, where the set of intention outputs indicating the set of autonomous driving actions prior to the SDV executing the set of autonomous driving actions. The control system can then execute the set of autonomous driving actions using acceleration, braking, and steering systems of the SDV. While executing the set of autonomous driving actions, the control system can generate a corresponding set of reactive outputs using the light output system to indicate the set of autonomous driving actions being executed, where the corresponding set of reactive outputs replacing the set of intention outputs.

Light output system for a self-driving vehicle

A backend communications system can manage communications, using transmission control protocol (TCP), between a fleet of AVs in a given region and a backend system that manages transportation for the fleet of AVs. For each of the fleet of AVs, the backend communications system can select a designated one of a plurality of communication channels to transmit and receive transmission acknowledgments (ACKs). The backend communications system can transmit data packets to the fleet of AVs using any number of communication channels and receive ACKs from each of the fleet of AVs over the designated communication channel.

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William Payne Ross