Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

The current embodiments include a method and a system in which a variable-configuration powertrain and trailer airbag inflation pressure can be controlled to maintain a desired following distance. By controlling a vehicle transmission, a final drive status, and/or a trailer airbag inflation pressure, a following distance can be maintained in anticipation of deceleration events to enhance vehicle deceleration capabilities. While applicable to vehicle platooning, particularly tractor trailers, the method and the system are equally applicable to essentially any autonomous following vehicle(s) which may or may not be communicatively coupled to a lead vehicle.

In one example, a method performed by a processing system of an autonomous vehicle includes reporting, to a first remote device, at least one of: a position of the first autonomous vehicle or an object detected in an environment surrounding the first autonomous vehicle, receiving from at least one of: the first remote device or a second remote device, data, wherein the data comprises at least one of: a current position of a second autonomous vehicle or an objected detected in an environment surrounding the second autonomous vehicle, and adjusting an operation of the first autonomous vehicle in response to the data.

A method and a device are disclosed for partitioning a widened area of a limited traffic lane bounded by two edges, and through which a vehicle can be driven by a driver in an automated manner along a reference path, said vehicle comprising a lateral positioning aid with respect to the reference path. The method comprises detecting a lane split, determining a widened area (204), determining a plurality of reference paths, and partitions the widened area into a plurality of sub-areas according to the determined reference paths.

By an automated driving control device or a computer-readable non-transitory storage medium storing an automated driving control program capable of performing eyes-off automated driving without periphery monitoring obligation by a driver, a traffic congestion state is recognized, a start of the eyes-off automated driving is permitted or is not permitted.

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.

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.

The present disclosure is directed to apparatus, methods, and non-transitory storage medium for controlling the maximum speed of a vehicle as that vehicle travels along a route. Apparatus and methods consistent with the present disclosure may receive location information from an electronic device that is located at a vehicle and may provide information to the electronic device at that controls the maximum speed of the vehicle as speed limits change along the route.

The present disclosure relates to a monitoring system for a vehicle and a vehicle. The vehicle includes multiple driving modes, and the monitoring system includes: an instruction-generating device configured to generate a control instruction for triggering one driving mode of multiple driving modes of the vehicle, and a communication device configured to send the control instruction to the vehicle so as to trigger the vehicle to travel according to the one driving mode of multiple driving modes.

Among other things, techniques are described for forecasting occupancy of a vehicle location. This includes receiving, by at least one processor, status information of a parking location the status information representing an availability of the parking location; predicting, by the at least one processor, a future status of the parking location based on the received status information; determining, by the at least one processor, a destination based on the predicted future status of the parking location; and providing, by the at least one processor, the predicted future status to a controller of a vehicle for controlling the vehicle to drive to the destination.