Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.

Abstract: 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.

Abstract: Navigating a vehicle from an origin location to a destination location includes receiving, from a requester, the origin location and the destination location; determining a transition point between the origin location and the destination location that partitions a portion of a route between the origin location to the destination location into a first route segment and a second route segment; and providing the transition point, the first route segment, and the second route segment to the requester. The vehicle is to be driven according to a first drive mode in the first route segment that ends at the transition point and according to a second drive mode in the second route segment that starts at the transition point.

Abstract: A device may receive a request to initiate an audio test and may generate, based on the request, one or more instructions to control a bone transducer device and a microphone device. The device may send the one or more instructions to a control device and may receive, after sending the one or more instructions, audio data from the control device. The device may process the audio data to determine whether the audio data includes an otoacoustic emission (OAE) and may cause at least one action to be performed based on determining whether the OAE is included in the audio data.

Abstract: 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.

Abstract: 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.

Abstract: Methods, computer program products, systems are provided. The method comprises receiving a request to install an application. A middle-share container corresponding to the application is then created and provisioned upon a shared middleware layer. Middleware components that the application needs are pre-installed and pre-configured in the shared middleware layer.

Abstract: A method for screening materials may include obtaining materials from a database. The method may include screening the materials to obtain a one or more screened materials. The method may include generating a training set based on the screened materials, validated experimental data, or both. The method may include establishing a machine learning screening model based on the training set, one or more target parameters, or both. The method may include applying the machine learning screening model to uncharacterized materials. The method may include outputting one or more materials having characteristics matching the target parameters.

Abstract: A device may receive a request to initiate an audio test and may generate, based on the request, one or more instructions to control a bone transducer device and a microphone device. The device may send the one or more instructions to a control device and may receive, after sending the one or more instructions, audio data from the control device. The device may process the audio data to determine whether the audio data includes an otoacoustic emission (OAE) and may cause at least one action to be performed based on determining whether the OAE is included in the audio data.

Abstract: Methods, computer program products, systems are provided. The method comprises receiving a request to install an application. A middle-share container corresponding to the application is then created and provisioned upon a shared middleware layer. Middleware components that the application needs are pre-installed and pre-configured in the shared middleware layer.