Ford Global Technolgoies, LLC

Amazon Technologies, Inc.

Argo Ai Gmbh

Volkswagen Group of America Investments, LLC

John Russell Lepird

Ramadev Burigsay Hukkeri

Andrew Thomas Hartnett

Fabian Andreas Bumberger

Patrick Stirling Barone

Benjamin Charles Eagan

Lorenzo Nardi

Daniel Alfonsetti

Pragati Satpute

Artem Churkin

Alexander Wah Tak Metz

This document discloses system, method, and computer program product embodiments for managing data generated by one or more systems of a vehicle. In various embodiments, a processor onboard a vehicle receives messages generated by one or more onboard systems of the vehicle. The system saves a first set of the messages to a first storage location on the vehicle according to a first data logging policy. The system processes a second set of the messages to reduce data elements and yield offboard data that is designated for offboard use. The first and second sets of messages may or may not overlap with each other. The system saves the offboard data to a second storage location that is onboard the vehicle and subject to a second data logging policy. The second data logging policy differs from the first data logging policy.

Methods and systems for managing data storage in vehicle operations

Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to react to objects posing a risk to the AV. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take can be generated, the plurality of trajectories being based on a plurality of inputs indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated. An error value representing how accurate the forecasted position is versus an observed position of the object can be stored. Error values can be accumulated over a period of time. A risk factor can be assigned for the object based on the accumulated error values. A maneuver for the AV can be performed based on the risk factor.

Integrated trajectory forecasting, error estimation, and vehicle handling when detecting an observed scenario

Disclosed is a system, method, and computer program product embodiments for estimating the state of a school transportation vehicle. For example, the method includes capturing sensor data that includes information about an environment of an autonomous vehicle. The method further includes detecting a school transportation vehicle in the sensor data, receiving a list of indicators associated with a set of candidate states for the school transportation vehicle, and analyzing data from one or more sources to determine indicator values. The method further includes using the indicator values to compute a probability mass function that includes a likelihood that the school transportation vehicle is in each of the candidate states. The method further includes imposing one or more goals on a motion control system of the autonomous vehicle based on the probability mass function and causing the autonomous vehicle to operate according to the one or more goals.

State estimation and response to active school vehicles in a self-driving system

A system includes an on-board electronic device of an autonomous vehicle, and a computer-readable medium having one or more programming instructions. The system receives one or more forecast messages pertaining to a track, where each of the forecast messages includes a unique identifier associated with the track, and receives one or more inference messages pertaining to the track, where each of the inference messages includes the unique identifier. The system aggregates the one or more forecast messages and the one or more inference messages to generate a message set, and applies a set of processing operations to the message set to generate a feedback message. The system identifies one or more events from the feedback message, automatically generates an annotation for one or more of the events that is identified, and embeds the generated annotations in an event log for the autonomous vehicle.

On-board feedback system for autonomous vehicles

Systems, methods, and autonomous vehicles may obtain sensor data associated with an environment surrounding an autonomous vehicle; provide the sensor data to a plurality of plugins; independently determine, with each plugin, based on the sensor data, whether to request a remote guidance session for the autonomous vehicle, each plugin of the plurality of plugins including a different model that is applied by that plugin to the sensor data to determine whether to request the remote guidance session; receive, from at least one plugin, a request to initiate the remote guidance session; and in response to receiving the request to initiate the remote guidance session, communicate with a computing device external to the autonomous vehicle to establish the remote guidance session.

System and method for a modular and continually learning remote guidance system for autonomous vehicles

Techniques for performing spoken language understanding (SLU) processing locally on a user device are described. When a user device is about to present content on a display, the user device may generate one or more SLU models (e.g., one or more ASR models and/or one or more NLU models) specific to the content to be presented. When the user device receives a spoken input while the content is being presented on the display, the user device performs SLU processing on the spoken input using the display-specific SLU model(s).

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Pittsburgh, PA, United States of America

John Russell Lepird