According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

Aspects of the disclosure provide for controlling an autonomous vehicle using no block costs in space and time. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A set of no-block zones through which the trajectory traverses may be identified. A no-block zone may be region where the autonomous vehicle should not stop but can drive through in an autonomous driving mode. For each given no-block zone of the set, a penetration cost that increases towards a center of the no-block zone and decreases towards edges of the no-block zone may be determined. Whether the autonomous vehicle should follow the trajectory may be determined based on the penetration cost. An autonomous vehicle may be controlled in the autonomous driving mode according to the trajectory based on the determination of whether the autonomous vehicle should follow the trajectory.

Aspects of the disclosure provide for controlling an autonomous vehicle using no block costs in space and time. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A set of no-block zones through which the trajectory traverses may be identified. A no-block zone may be region where the autonomous vehicle should not stop but can drive through in an autonomous driving mode. For each given no-block zone of the set, a penetration cost that increases towards a center of the no-block zone and decreases towards edges of the no-block zone may be determined. Whether the autonomous vehicle should follow the trajectory may be determined based on the penetration cost. An autonomous vehicle may be controlled in the autonomous driving mode according to the trajectory based on the determination of whether the autonomous vehicle should follow the trajectory.

This disclosure provides methods, devices and systems for a vehicle user equipment (VUE) to obtain extrinsic information about an object or location. The VUE may transmit a request for information about the object or the location to a road side unit (RSU). The RSU may receive the request and determine a set of extrinsic information for the first UE regarding the object or the location based on a set of information from one or more other UEs. The extrinsic information includes information that is not provided by the VUE. The RSU may transmit the set of extrinsic information to the VUE. The VUE may determine whether to accept a feature of the object or the location in the extrinsic information based on the set of extrinsic information and a set of intrinsic information detected by the VUE, The VUE may select an autonomous driving action based on the accepted feature.

A vehicle traveling control system according to the example in the present disclosure communicates with an automatic operation control system which drafts a traveling plan of the vehicle, and performs an automatic traveling control for automatically running the vehicle along the traveling plan received from the automatic operation control system. The vehicle traveling control system predicts a risk based on information about surrounding environment of the vehicle, and performs, when the risk is predicted, a risk avoidance control to intervene in the automatic traveling control in order to avoid the risk. When the risk avoidance control is executed, the vehicle traveling control system transmits information on the risk avoidance control to the automatic operation control system.

Systems and methods for curiosity development in an agent located in an uncertain environment are provided. In one embodiment, the system includes a goal state module, a curiosity module, and a planning module. The goal module is configured to calculate a goal state of a goal associated with the environment. The curiosity module is configured to determine an uncertainty value for the environment and calculate a curiosity reward based on the uncertainty value. The planning module is configured to update a motion plan based on the goal state and the curiosity reward.

Aspects of the disclosure relate to distributing vehicles to perform scouting. This may involve receiving a request for a scouting objective for a vehicle, and in response, identifying a set of scouting objectives that the vehicle is eligible to visit. Each scouting objective of the set is associated with one or more scouting quests, and each scouting quest is associated with a plurality of scouting objectives. For each given scouting objective in the set of scouting objectives, an overall weight may be determined using combined weights for the given scouting objective and any scouting quests with which the given scouting objective is associated. One or more scouting objectives of the set of scouting objectives may be selected using the determined overall weights. The one or more selected scouting objectives may be provided to the vehicle.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

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.

Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.