May Mobility, Inc.

Toyota Motor Engineering & Manufacturing North America, Inc.

University of Michigan

Sajan Patel

Edwin B Olson

Collin Johnson

Tom Voorheis

Jeff Sterniak

Melinda Kothbauer

Asheesh Ranjan

Gregory Meyer

Stuart Fairley

Kristofer Kusano

Brent Griffin

Jason J Corso

Ahmed Elshaarany

Mitesh Agrawal

Alan Heirich

A system for operating an autonomous agent with incomplete environmental information can include and/or interface an autonomous operating system and an autonomous agent. A method for operating an autonomous agent with incomplete environmental information includes any or all of: receiving a set of inputs; determining a set of known objects in the ego vehicle's environment; determining a set of blind regions in the ego vehicle's environment; and inserting a set of virtual objects into the set of blind regions; selecting a set of virtual objects based on the set of blind regions; operating the autonomous agent based on the set of virtual objects; and/or any other suitable processes.

Method and system for operating an autonomous agent with incomplete environmental information

The method can include: receiving a set of inputs; determining a set of policies based on the set of inputs; determining a set of scores associated with the set of environmental policies; and evaluating the set of policies. Additionally or alternatively, the method can include operating the ego agent according to a selected policy and/or any other processes. The method functions to facilitate scoring of policies based on 'feasibility' for agents in an environment. Additionally or alternatively, the method can function to facilitate autonomous operation of a vehicle (e.g., based on policy-feasibility of agents in the environment). Additionally or alternatively, the method can function to facilitate intention estimation for agents in an environment.

Method and system for feasibility-based operation of an autonomous agent

A system for dynamic policy curation includes a computing system and interfaces with an autonomous agent. A method for dynamic policy curation includes collecting a set of inputs; processing the set of inputs; and determining a set of available policies based on processing the set of inputs. Additionally or alternatively, the method can include any or all of: selecting a policy; implementing a policy; and/or any other suitable processes.

Method and system for dynamically curating autonomous vehicle policies

System, methods, and other embodiments described herein relate to predicting lane changes for nearby vehicles of a host vehicle. In one embodiment, a method includes, in response to detecting that one or more of the nearby vehicles are present proximate to the host vehicle, collecting pose information about the nearby vehicles. The nearby vehicles are traveling proximate to the host vehicle and in a direction of the host vehicle. The method includes analyzing the pose information of the nearby vehicles using separate recurrent units of a structural recurrent neural network (S-RNN) to generate factors according to the lanes. The method includes generating prediction indicators for the nearby vehicles as a function of the factors for the lanes using the S-RNN. The method includes providing electronic outputs identifying the prediction indicators that specify a likelihood of the nearby vehicles changing between the lanes.

System and method for vehicle lane change prediction using structural recurrent neural networks

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Ann Arbor, MI, United States of America

Sajan Patel