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: Systems and methods described herein relate to traffic-flow regulation via centralized lateral flow control. One embodiment receives, at a locality manager that regulates traffic flow on a roadway via lateral flow control, aggregated macroscopic traffic state information from a section manager that communicates with one or more connected vehicles in a section of the roadway; processes the aggregated macroscopic traffic state information at the locality manager using a reinforcement-learning-based model to determine target lateral flows for two or more lanes of the roadway in the section of the roadway; and transmits the target lateral flows from the locality manager to the section manager, which converts the target lateral flows to lane-change actions and transmits the lane-change actions to the one or more connected vehicles.

Abstract: Systems and methods described herein relate to coordinated vehicle lane assignment. One embodiment receives from a locality manager, at a section manager that communicates with one or more connected vehicles in a section of a roadway, target lateral flows for two or more lanes of the roadway in the section of the roadway; converts the target lateral flows to a target number of connected vehicles N at the section manager; selects for lane change, at the section manager, a set of N connected vehicles whose ranked distances from a following vehicle in a target lane are greatest among the one or more connected vehicles, when a direction of lane change is uniform among the set of N connected vehicles; and transmits lane-change actions from the section manager to the set of N connected vehicles.

Abstract: Systems and methods described herein relate to coordinated vehicle lane assignment using reinforcement learning. One embodiment receives from a locality manager, at a section manager that communicates with a plurality of connected vehicles in a section of a roadway, target lateral flows for two or more lanes of the roadway in the section of the roadway; receives, at the section manager, traffic state information from the plurality of connected vehicles; processes, at the section manager, the traffic state information and the target lateral flows using a reinforcement-learning-based model to determine lane-change actions for the plurality of connected vehicles, wherein the reinforcement-learning-based model is based on a single neural network with shared parameters for the plurality of connected vehicles; and transmits the lane-change actions from the section manager to the plurality of connected vehicles.

Abstract: A system for determining a lane change decision based on a downstream traffic state can include a processor, a communications device, and a memory. The processor can be disposed on an ego vehicle. The memory can store an acceleration gain module and a communications module. The acceleration gain module can include instructions that cause the processor to: (1) obtain, via the communications device and from a monitoring system, information about an end of stopped or slow moving downstream traffic in a lane and (2) calculate a result of an equation for the lane change decision. The equation can include information about virtual vehicles positioned near the end or a geographical location. The communications module can include instructions that cause the processor to cause a signal, with information based on the result, to be sent to a component of the ego vehicle for an action to be performed by the component.