A method of maintaining vehicle formation includes receiving a desired formation distance between a lead vehicle and a follower vehicle; receiving a pre-planned path for the follower vehicle; and defining a dynamic zone around a current position of the lead vehicle. The dynamic zone has a boundary characterized by a first radius from the current position of the lead vehicle. The first radius can be substantially equal to the desired formation distance. The method further includes determining a next speed of the follower vehicle based on a current position of the follower vehicle with respect to the boundary of the dynamic zone; determining a commanded curvature of the follower vehicle based on the current position of the follower vehicle with respect to the pre-planned path; and outputting the next speed and the commanded curvature to a control system of the follower vehicle for navigation of the follower vehicle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using anchor trajectories.

A system for an autonomous vehicle that predicts a location-based maneuver of a remote vehicle located in a surrounding environment includes one or more vehicle sensors collecting sensory data indicative of one or more vehicles located in the surrounding environment. The system also includes one or more automated driving controllers in electronic communication with the one or more vehicle sensors. The one or more automated driving controllers execute instructions to compare a lane of travel of the remote vehicle with a current lane of travel of the autonomous vehicle. In response to determining the lane of travel of the remote vehicle is a different lane than the current lane of the autonomous vehicle, the one or more automated driving controllers predict the location-based maneuver of the remote vehicle based on aggregated vehicle metrics that are based on historical data collected at the specific geographical location.

Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

An autonomous vehicle (AV) implements a notification system that provides notifications to user devices in nearby vehicles to alert drivers of the nearby vehicles of the AV's planned behavior. The notifications are delivered wirelessly and output by the user device to the drivers. The planned behaviors of the AV may not be conveyed by existing mechanisms, such as turning signals or hazard lights. The AV or the receiving user device may determine when an AV's planned behavior may impact a particular driver, and provide relevant notifications to the driver.

A traffic congestion determination unit is configured to determine whether a traffic congestion occurs in an automatic travel period in which the vehicle travels by an automatic driving function. A cut-in prediction unit is configured to detect a sign of cut-in of an adjacent vehicle, which is adjacent to a subject vehicle as the vehicle, on determination by the traffic congestion determination unit that a traffic congestion occurs. A provision restricting unit is configured to restrict display of content provided in the automatic travel period on detection of a sign of cut-in of the adjacent vehicle by the cut-in prediction unit.

A system and method for implementing reward based strategies for promoting exploration that include receiving data associated with an agent environment of an ego agent and a target agent and receiving data associated with a dynamic operation of the ego agent and the target agent within the agent environment. The system and method also include implementing a reward function that is associated with exploration of at least one agent state within the agent environment. The system and method further include training a neural network with a novel unexplored agent state.

A method for predicting a future action of an object for a driving assistance system for a highly automated mobile vehicle. At least one sensor signal from at least one vehicle sensor of the vehicle is read in, the sensor signal representing at least one piece of kinematic object information concerning the object that is detected by the vehicle sensor at an instantaneous point in time. A planner signal from a planner of the autonomous driving assistance system is read in, the planner signal representing at least one piece of semantic information concerning the object or the surroundings of the object at a point in time in the past. The kinematic object information is fused with the semantic information to obtain a fusion signal. A prediction signal is determined using the fusion signal, the prediction signal representing the future action of the object.

In various embodiments, methods, systems, and vehicles are provided for executing a lane change for a host vehicle. In various embodiments, a method includes: receiving, by a processor, an indication that a lane change from an initial lane to an intended lane is desired for the host vehicle; defining, by the processor, an initial lane center target, a negotiation target, and an intended lane center target based on the desired lane change; and controlling, by the processor, the host vehicle to at least one of the initial lane center target, the negotiation target, and the intended lane center target based on a finite state machine, wherein the initial lane center target is at or in proximity to a determined center of the initial lane, wherein the intended lane center target is at or in proximity to a determined center of the intended lane, and wherein the negotiation target is offset from the initial lane center target and within the initial lane.