Nissan North America, Inc.

Renault S.a.s.

University of Massachusetts

United States of America As Represented by the Administrator of Nasa

Najamuddin Mirza Baig

Liam Pedersen

Viju James

Fang-Chieh Chou

Stefan Witwicki

Astha Vagadia

Shlomo Zilberstein

Abdul Rahman Kreidieh

Omar Bentahar

Simon Tien

Laura Cesafsky

Sparsh Sharma

Xin Yang

Kyle Hollins Wray

Christopher Ostafew

Julius S Gyorfi

Maarten Sierhuis

Shigemasa Kuwata

Balachandran Gadaguntla Radhakrishnan

John W Lawson

Arec Jamgochian

Hideyuki Komatsu

Shreyas Honrao

Mohit Rakesh Mehta

Hillary Abraham

Katsuhiko Hirayama

A system receives GPS data and on-board sensor data from several connected vehicles indicating locations of nearby vehicles and objects. The system processes the data to create a shared-world model that includes locations and velocities of the connected vehicles and nearby vehicles and objects, and the system determines whether driving hazards exist, such as potential collisions. The system may transmit an alert to at least one of the connected vehicles, to cause the connected vehicle or a mobile device to present a warning message to a driver, such as a visual, audio, or haptic message, or to cause the connected vehicle to implement an action to avoid the driving hazard, such as activating emergency braking or altering course. The system may create, and transmit to a connected vehicle or mobile device, a lane-level traffic model indicating traffic density, traffic speed, and traffic throughput.

Real-time traffic condition warning system

A control signal related to a road segment is received by a processor of a vehicle from a remote server. Using at least a portion of the control signal, a cruise control parameter for controlling the vehicle is obtained. Responsive to determining that a cruise control module of the vehicle is enabled, the cruise control module is configured to use the cruise control parameter. The cruise control module operates to maintain the cruise control parameter for the vehicle.

Congestion management with cruise control

A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.

Generation and transmission of control commands for connected vehicles based on predicted future flow, average velocity, or future density of traffic

A method and system for determining risk indicators for intersections. A method includes receiving, by a computing node from intersection proximate sensors at each intersection, intersection data. For each intersection, the method includes determining, by the computing node for each connected vehicle proximate to an intersection, a driver risk score based on driver distraction data from the intersection data for the intersection, determining, by the computing node, a near miss score based on the intersection data for the intersection, and assigning, by the computing node for the intersection, an intersection risk indicator level based on an exponential moving average of intersection risk indicator scores determined from driver risk scores and near miss scores. The method includes providing, by the computing node, intersection risk indicator levels to each connected vehicle to facilitate control decisions by each connected vehicle when approaching intersections.

Intersection risk indicator

Intersection collision avoidance may use a cellular transceiver for a cellular network and a processor located at a cellular access point for multi-access edge computing. The processor includes a shared world model and a conflict detection module. The shared world model is configured to receive, by the cellular transceiver, signals relating to at least two road users in proximity to an intersection within a vehicle transportation network, wherein the at least two road users include an ego vehicle, and the signals conform to a standards-based communication protocol. The conflict detection module is configured to receive object information from the shared world model, determine a potential future collision between the ego vehicle and an other road user of the at least two road users, and transmit a notification of the potential future collision to the ego vehicle over the cellular network.

System and method for intersection collision avoidance

An intersection collision avoidance system determines, for an ego vehicle, a direction indicated by its turn signal, its destination setting, or both, generates, where the direction is determined, a possible intended path relative to an intersection using a high-definition (HD) map and the direction, and generates, where the destination setting is determined, a possible intended path for using the HD map and the destination setting. Where the direction and the destination setting are both determined, the direction indicated by the turn signal is compared to the possible intended path generated using the destination setting, and one of the possible intended paths is selected based on the comparison. The system transmits, to a conflict detection module, a set of drive goals for the ego vehicle relative to the intersection that conforms to the intended path. The module can determine a possible collision with another road user using the drive goals.

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

An all-solid-state battery comprises a lithium anode, a cathode, solid electrolyte and a protective layer between the solid electrolyte and the lithium anode. The protective layer comprises an ion-conducting material having an electrochemical stability window against lithium of at least 1.0 V, a lowest electrochemical stability being 0.0 V and a highest electrochemical stability being greater than 1.0 V. More particularly, when the solid electrolyte is LiSiCON, the electrochemical stability window is at least 1.5 V, the lowest electrochemical stability is 0.0 V and the highest electrochemical stability is greater than 1.5 V. When the solid electrolyte is sulfide-based, the electrochemical stability window is at least 2.0 V, the lowest electrochemical stability is 0.0 V and the highest electrochemical stability is greater than 2.0 V.

Anode interlayer for lithium batteries

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.

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San Jose, CA, United States of America

Najamuddin Mirza Baig