Nissan North America, Inc.

Renault S.a.s.

University of Massachusetts

United States of America As Represented by the Administrator of Nasa

Omar Bentahar

Stefan Witwicki

Kyle Hollins Wray

Arec Jamgochian

Shlomo Zilberstein

Julius S Gyorfi

Siddharth Thakur

Najamuddin Mirza Baig

Laura Cesafsky

Aude Laurent

Astha Vagadia

Kevin Poulet

Sparsh Sharma

Mark Allan

Armelle Guerin

Hillary Abraham

An apparatus for post-processing of a decision-making model of an autonomous vehicle receives a decision-making model including a plurality of states. The model is processed using multivariate data that comprises values for at least three observations of a vehicle operational scenario. A slice of the model decision space is generated by fixing values of all except two observations, and modifying the values of the two observations to obtain multiple alternative solutions for the model. The alternative solutions and the modified values form the slice. Each alternative solution is associated with a respective first value of a first observation and a respective second value of a second observation. The apparatus also generates a solution to a modified decision-making model that is the model modified by, for at least one state and at least one of the two observations, modifying a probabilistic transition matrix, a probabilistic observation matrix, or both.

Apparatus and method for post-processing a decision-making model of an autonomous vehicle using multivariate data

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 identify distinct vehicle operational scenarios; instantiate decision components, where each of the decision components is an instance of a respective decision problem, and where the each of the decision components maintains a respective state describing the respective vehicle operational scenario; receive respective candidate vehicle control actions from the decision components; select an action from the respective candidate vehicle control actions, where the action is from a selected decision component of the decision components, and where the action is used to control the AV to traverse a portion of the vehicle transportation network; and generate an explanation as to why the action was selected, where the explanation includes respective descriptors of the action, the selected decision component, and a state factor of the respective state of the selected decision component.

Explainability of autonomous vehicle decision making

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.

A method of exception handing for an autonomous vehicle (AV) includes identifying an exception situation; identifying relevant sensors for the exception situation; identifying relevant tools to the exception situation, the relevant tools usable by a tele-operator to resolve the exception situation; and presenting, on a display of the tele-operator, data from the relevant sensors and the relevant tools.

System to recommend sensor view for quick situational awareness

An apparatus for remote support of autonomous operation of a vehicle is described. The apparatus includes a processor that performs a method including receiving a query to define a path for the vehicle while the vehicle is traveling along a route from an origin to a destination in a vehicle transportation network, the query comprising multiple points including at least a start point and an end point of the path, obtaining a satellite image of a portion of a geographical area that encompasses the multiple points, determining, using the satellite image, static obstacles within the portion of the geographical area, the static obstacles defining navigable areas within the vehicle transportation network, generating the path through each of the multiple points such that the path is located within at least one navigable area of the navigable areas, and transmitting the path for use by the vehicle.

Pathfinding assistance system for teleoperation

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Sunnyvale, CA, United States of America

Omar Bentahar