Abstract: A control signal generated based on real-time vehicle sensor data from multiple vehicles on a road segment is received by a processor of a vehicle. The processor uses at least a portion of the control signal to generate an adaptive cruise control parameter that includes a speed parameter and a headway parameter. The processor configures a cruise control module of the vehicle to use the adaptive cruise control parameter. The control signal may be generated using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle.

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 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: An electric vehicle charging control device includes an electronic dynamic charging schedule generator, a non-transitory computer readable medium and an electronic communicator. The non-transitory computer readable medium stores vehicle profile data and building profile data. The electronic communicator is configured to receive profile updates to the vehicle profile data from an electronic user interface. The electronic controller is programmed to generate a driver charging profile based on the vehicle profile data and the building profile data. The electronic controller is programmed to update the driver charging profile based on the electric vehicle user's behavior. The electronic controller is programmed to generate a charging schedule for the electric vehicle based on the updated driver charging profile.

Abstract: An electric vehicle charging control device includes an electronic dynamic charging schedule generator, a non-transitory computer readable medium and an electronic communicator. The electronic dynamic charging schedule generator has an electronic controller configured to determine when an electric vehicle user accesses a charging port provided at a building structure being powered by an electric source. The non-transitory computer readable medium stores vehicle profile data for the electric vehicle. The electronic communicator is configured to receive profile updates to the vehicle profile data from an electronic user interface. The electronic controller is programmed to generate a charging schedule for the electric vehicle based on the vehicle profile data. The charging schedule has a charge period in which the electric vehicle is receiving charge from the building structure and a discharge period in which the electric vehicle is providing charge to the building structure.

Abstract: An electric vehicle charging control device includes a charging station and an electronic dynamic charging schedule generator. The charging station has at least a first charging port and a second charging port. The electronic dynamic charging schedule generator has an electronic controller is configured to determine when a first electric vehicle user accesses the first charging port. The electronic controller is further configured to determine when a second electric vehicle user accesses the second charging port. The second electric user accesses the second charging port after the first user accesses the first charging port. The electronic controller is programmed to generate a first charging schedule for the first electric vehicle. The electronic controller is programmed to generate a second charging schedule for the second electric vehicle. The electronic controller is programmed to update the first charging schedule based on the second charging schedule.

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 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: An inversion controller of a vehicle receives a desired acceleration. The inversion controller includes a model of operations of a cruise control module of the vehicle. The inversion controller obtains a cruise control parameter based on the desired acceleration. The inversion controller outputs to the cruise control module the cruise control parameter. The cruise control module is configured to maintain the cruise control parameter for the vehicle.

Abstract: A vehicle on-board unit for a host vehicle comprises a communication system and an electronic controller. The communication system is programmed to electronically receive travel information from one or more preceding vehicles that are traveling ahead of the host vehicle in a lane. The electronic controller includes a vehicle path determination component programmed to determine a travel path of the host vehicle in the lane in which the host vehicle is currently traveling. The electronic controller further includes a target vehicle speed determination component programmed to determine a target vehicle speed for the host vehicle based on the travel information received from the one or more preceding vehicles. The travel information includes traveling speed of a predetermined group of preceding vehicles.

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.