Abstract: 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.

Abstract: A system receives, from a plurality of connected vehicles, respective GPS data indicating an absolute location of a respective connected vehicle and on-board sensor data indicating locations of nearby vehicles and objects relative to the respective connected vehicle. The system processes the data to create a shared-world model that includes at least the locations of the plurality of connected vehicles and the nearby vehicles and objects. Some implementations of the shared-world model further include the speeds and trajectories of each of the plurality of connected vehicles and the nearby vehicles and objects. The system transmits a representation of the shared-world model to one or more of the plurality of connected vehicles, which may utilize the shared-world model to provide advanced warnings for drivers or to provide improved path planning for autonomous vehicles.

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 system may receive a video stream from a camera of a vehicle in a transportation network. The system may also receive an input indicating an acceleration and a steering angle of a simulated lead vehicle. The system may determine a pose of the simulated vehicle relative to a home pose based on the acceleration input and the steering input, and display an overlay of a representation of the simulated vehicle in the video stream at pixel coordinates based on the pose. The system may determine, from the pixel coordinates, spatial coordinates of the simulated vehicle in the transportation network, wherein the spatial coordinates are relative to at least one of the transportation network or the camera. The system may transmit the spatial coordinates to the vehicle to cause the vehicle to follow a path based on the spatial coordinates.

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: Vehicle guidance with systemic optimization may include traversing, by a current vehicle, a vehicle transportation network, by obtaining, by the current vehicle, systemic-utility vehicle guidance data for a current portion of the vehicle transportation network and traversing, by the current vehicle, the current portion of the vehicle transportation network in accordance with the systemic-utility vehicle guidance data. Obtaining the systemic-utility vehicle guidance data may include obtaining vehicle operational data for a region of a vehicle transportation network, wherein the vehicle operational data includes current operational data for a plurality of vehicles operating in the region, operating a systemic-utility vehicle guidance model for the region, obtaining systemic-utility vehicle guidance data for the region from the systemic-utility vehicle guidance model in response to the vehicle operational data, and outputting the systemic-utility vehicle guidance data to the current vehicle.

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: A system may receive a video stream from a camera of a vehicle in a transportation network. The system may also receive an input indicating a steering angle and/or a distance to travel. The system may determine a projected path of the vehicle in the transportation network based on the input. The projected path may be determined using spatial coordinates relative to the transportation network or the camera. The system may then determine, from the spatial coordinates, pixel coordinates in the video stream corresponding to the projected path. The system may display an overlay in the video stream based on the pixel coordinates. The overlay may include a visualization of the projected path.

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 method of delivering data to a vehicle including transmitting a request for the data from a vehicle to a database. The data stored in the database is tagged with location information. A location of the vehicle is determined. The data in the database tagged with location information within a predetermined distance of the determined location of the vehicle is determined. The data is delivered to the vehicle based on the determined location of the vehicle. The delivered data is displayed on a display disposed in the vehicle.

Abstract: Engine activation planning in a hybrid electric vehicle (HEV) is disclosed. Historical driving data of the HEV are analyzed to identify recurring driving scenarios and patterns specific to a driver of the HEV. Future driving scenarios are predicted based on the historical driving data. An engine activation policy is generated for the HEV. The engine activation policy optimizes battery charging and usage in response to the future driving scenarios. The engine activation policy is used to control activation of a gasoline engine in the HEV where a control decision is dynamically adjusted based on a comparison of real-time driving conditions with the future driving scenarios.

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: Activating an engine of a HEV to charge a battery includes obtaining a first GPS trace from a first trip of the HEV along a first route, where the first GPS trace includes first trace metadata; obtaining a second GPS trace from a second trip, where the second GPS trace includes second trace metadata; adding, to a navigation map, an aggregation of the first trace metadata and the second trace metadata for edges of the navigation map; using the navigation map to obtain an activation action of the engine, where the activation action is selected from a set that includes a first activation action of turning the engine on and a second activation action of turning the engine off; and activating the engine according to the activation action, where activating the engine using the first activation action causes the engine to turn on to charge the battery of the HEV.

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.