Abstract: Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.

Abstract: Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Abstract: Techniques described herein are directed to classifying lanes in an environment of a vehicle for, for example, performing lane handling. In an example, system(s) of a vehicle can determine a signal indicative of a presence of the vehicle in a lane of a drivable surface in an environment within which the vehicle is located. The system(s) can determine, based at least in part on the signal, a classification of the lane as at least one of occupied (an object is at least partially in the lane), unoccupied (no object in the lane), and/or established (e.g., where an object has established a priority in the lane). The system(s) can control the vehicle based at least in part on the classification of the lane to improve safety in scenarios, for example, including merging.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.

Abstract: Techniques described herein are directed to classifying lanes in an environment of a vehicle for, for example, performing lane handling. In an example, system(s) of a vehicle can determine a signal indicative of a presence of the vehicle in a lane of a drivable surface in an environment within which the vehicle is located. The system(s) can determine, based at least in part on the signal, a classification of the lane as at least one of occupied (an object is at least partially in the lane), unoccupied (no object in the lane), and/or established (e.g., where an object has established a priority in the lane). The system(s) can control the vehicle based at least in part on the classification of the lane to improve safety in scenarios, for example, including merging.

Abstract: Techniques for determining an uncertainty metric associated with an object in an environment can include determining the object in the environment and a set of candidate trajectories associated with the object. Further, a vehicle, such as an autonomous vehicle, can be controlled based at least in part on the uncertainty metric. The vehicle can determine a traversed trajectory associated with the object and determine a difference between the traversed trajectory and the set of candidate trajectories. Based on the difference, the vehicle can determine an uncertainty metric associated with the object. In some instances, the vehicle can input the traversed trajectory and the set of candidate trajectories to a machine-learned model that can output the uncertainty metric.

Abstract: Techniques described herein relate to lane handling, for instance to enable vehicles to perform turns without colliding into oncoming vehicles and/or bicycles in other lanes. System(s) associated with a vehicle can access sensor data and/or map data associated with an environment within which the vehicle is positioned in a first lane. The system(s) can determine that the vehicle is to perform a turn and a start of a second lane associated with the turn or a merging zone associated with the second lane. The system(s) can determine a location of the vehicle relative to the start of the second lane or the merging zone associated with the second lane and, based partly on the location, can cause the vehicle to merge into the second lane prior to performing the turn.

Abstract: Techniques are discussed for evaluating trajectories based on risk associated with the trajectories with respect to predicted locations of objects in an environment. A vehicle can capture sensor data of an environment, which may include object(s) separate from the vehicle, such as another vehicle or a pedestrian. A prediction system can output a discretized probability distribution comprising prediction probabilities associated with possible locations of the object in the future. Heat maps, as an example discretized probability distribution, can represent one or more objects. Trajectories can be generated for the vehicle to follow in the environment. An overlap between a region of the vehicle along a trajectory and the heat map can be determined, and a probability associated with the overlap can represent a risk associated with a trajectory navigating through the environment. The vehicle can be controlled based on risks associated with the various trajectories.

Abstract: Techniques described herein relate to lane handling, for instance to enable vehicles to perform turns without colliding into oncoming vehicles and/or bicycles in other lanes. System(s) associated with a vehicle can access sensor data and/or map data associated with an environment within which the vehicle is positioned in a first lane. The system(s) can determine that the vehicle is to perform a turn and a start of a second lane associated with the turn or a merging zone associated with the second lane. The system(s) can determine a location of the vehicle relative to the start of the second lane or the merging zone associated with the second lane and, based partly on the location, can cause the vehicle to merge into the second lane prior to performing the turn.

Abstract: Techniques described herein are directed to classifying lanes in an environment of a vehicle for, for example, performing lane handling. In an example, system(s) of a vehicle can determine a signal indicative of a presence of the vehicle in a lane of a drivable surface in an environment within which the vehicle is located. The system(s) can determine, based at least in part on the signal, a classification of the lane as at least one of occupied (an object is at least partially in the lane), unoccupied (no object in the lane), and/or established (e.g., where an object has established a priority in the lane). The system(s) can control the vehicle based at least in part on the classification of the lane to improve safety in scenarios, for example, including merging.

Abstract: Techniques are discussed for evaluating trajectories based on risk associated with the trajectories with respect to predicted locations of objects in an environment. A vehicle can capture sensor data of an environment, which may include object(s) separate from the vehicle, such as another vehicle or a pedestrian. A prediction system can output a discretized probability distribution comprising prediction probabilities associated with possible locations of the object in the future. Heat maps, as an example discretized probability distribution, can represent one or more objects. Trajectories can be generated for the vehicle to follow in the environment. An overlap between a region of the vehicle along a trajectory and the heat map can be determined, and a probability associated with the overlap can represent a risk associated with a trajectory navigating through the environment. The vehicle can be controlled based on risks associated with the various trajectories.