Nissan North America, Inc.

Renault S.a.s.

University of Massachusetts

United States of America As Represented by the Administrator of Nasa

Astha Vagadia

Stefan Witwicki

Najamuddin Mirza Baig

Shlomo Zilberstein

Omar Bentahar

Laura Cesafsky

Sparsh Sharma

Kyle Hollins Wray

Christopher Ostafew

Julius S Gyorfi

Viju James

Arec Jamgochian

Hillary Abraham

An apparatus for traversing a vehicle transportation network by an autonomous vehicle (AV) includes a human-autonomy teaming manager. The manager includes a first processor configured to initiate a dialogue associated with a predefined dynamic task sequence that is responsive to a condition experienced by the AV while traversing from a starting location to an ending location within the vehicle transportation network. The sequence is one of a plurality of predefined dynamic task sequences that uses multiple agents to resolve the condition. The manager receives, from an interface accessible to a human agent, an input responsive to the dialogue. The input confirms the sequence as a selected predefined dynamic task sequence or selects an other of the plurality of predefined dynamic task sequences as the selected predefined dynamic task sequence. The manager delegates tasks of the selected predefined dynamic task sequence to resolve the condition.

Using plays for human-AI decision-making in remote autonomous vehicle support

A processor is configured to execute instructions stored in a memory to determine, in response to identifying vehicle operational scenarios of a scene, an action for controlling the AV, where the action is from a selected decision component that determined the action based on level of certainty associated with a state factor; generate an explanation as to why the action was selected, such that the explanation includes respective descriptors of the action, the selected decision component, and the state factor; and display the explanation in a graphical view that includes a first graphical indicator of a world object of the selected decision component, a second graphical indicator describing the state factor, and a third graphical indicator describing the action.

Explainability of autonomous vehicle decision making

Resolving an exception situation in autonomous driving includes receiving an assistance request to resolve the exception situation from an autonomous vehicle (AV); identifying a solution to the exception situation; forwarding the solution to a tele-operator; receiving a request for playback data from the tele-operator; receiving, from the AV, the playback data; and obtaining, from the tele-operator, a validated solution based on the tele-operator using the playback data. The playback data includes snapshots nof data related to autonomous driving stored at the AV at respective consecutive times t, for i=1, . . . , N.

Exception situation playback for tele-operators

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Astha Vagadia