Abstract: Autonomous vehicle operational management may include an autonomous vehicle traversing a vehicle transportation network, which may include operating a scenario-specific operational control evaluation module instance, which is an instance of a scenario-specific operational control evaluation module instantiated for an occurrence of a distinct vehicle operational scenario by allocating computing resources to, and populating, the scenario-specific operational control evaluation module instance with operational environment information corresponding to the occurrence of the distinct vehicle operational scenario.

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: 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: System and method for efficient charge management of an electric vehicle. Historical usage data of the electric vehicle is used to forecast usage patterns and to determine optimal charging schedules according to the forecasted usage patterns and considering factors like charging costs and charging-port availability at forecasted destinations. The optimal charging schedules can be dynamically adjusted based on real-time conditions or user-preferences. In addition to reducing total charging costs, the optimal charging schedules can reduce electrical-power grid load and increase battery lifespan.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

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: Collective action by multiple vehicles traversing a vehicle transportation network is used to improve operation of vehicles and utilization of the vehicle transportation network. Sensor data for multiple monitored vehicles traveling in a common direction along a road in a vehicle transportation network is received and used as input to a traffic estimation model to determine an estimated congestion for the road in the common direction. A cruise control setting for each of several controlled vehicles, at least some of the controlled vehicles traveling behind the monitored vehicles in the common direction, is determined from the estimated congestion. The cruise control setting is transmitted to the controlled vehicles to modify operation of the controlled vehicles using respective cruise control systems of the controlled vehicles.

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 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 method for planning an activation action for an engine of a vehicle is disclosed. The method includes planning, according to a model, an activation action of an engine of a vehicle, and activating the engine according to the activation action. The model includes a state space comprising a current charge level of the battery and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

Abstract: A track based moving object association is described for distributed sensing applications, such as for identifying multiple objects within a vehicle transportation network for use by a vehicle navigating the network. Position information for the multiple objects within a portion of the vehicle transportation network is obtained from multiple sensors within the portion of the vehicle transportation network. Using the position information of respective sensors of the multiple sensors, a respective track for objects of the multiple objects is determined. Similarity measures are determined for multiple tracks, including at least a first track determined using the position information of a first sensor of the multiple sensors and a second track determined using the position information of a second sensor of the multiple sensors. Based on the similarity measures of the tracks, a tracked object track for a tracked object of the multiple objects is determined.

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 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 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 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.