Abstract: Techniques and methods for crosswalk handling for vehicles. The vehicle may identify a crosswalk and an object associated with the crosswalk in the environment. The vehicle may determine a heatmap based on sensor data and map data. The vehicle may determine a factor associated with the crosswalk relative to one or more of the vehicle and/or object based on the heatmap associated with the crosswalk. The vehicle may determine a cost associated with the crosswalk based on the factor. The vehicle may determine a trajectory for the vehicle to operate in the environment based on the cost associated with the crosswalk and follow the trajectory to travel in the environment.

Abstract: Techniques for generating a driving surface cost landscape for determining costs for vehicle positions in an environment are described herein. A planning component within a vehicle may determine a non-preferred surface associated with a type of non-preferred area of an environment along a route of a vehicle, determine a preferred surface associated with a preferred area of the environment, determine an adjusted non-preferred surface by removing an overlapping area of the non-preferred surface that overlaps the preferred surface and determine a cost associated with a vehicle position based at least on the preferred surface and the adjusted non-preferred surface. The planning component may then determine a control trajectory for the autonomous vehicle based at least in part on the cost associated with the vehicle position.

Abstract: Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Techniques for enabling a vehicle to navigate around a blocking object, such as double-parked vehicle (“DPV”), are discussed herein. In some examples, a vehicle may receive sensor data representative of an environment. The vehicle may analyze such sensor data to determine that a DPV is located proximate the vehicle. When determining whether to follow the trajectory, the vehicle may determine one or more cost values corresponding to the trajectory. Further, the vehicle may receive weighted heatmap(s) that cover the region proximate the DPV. The vehicle may modify some or all cost values of the trajectory based on the weighted values of the heatmap(s). In some examples, the vehicle may follow the trajectory based at least in part on the cost values.

Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a group of candidate trajectories based at least in part on sensor data. This group of candidate trajectories is clustered into two or more clusters and one or more representative trajectories may be determined for each cluster. The representative trajectory may be one of the trajectories in the cluster or a separately determined trajectory that represents the trajectories in a cluster, such as a mean or median trajectory. The representative trajectory may be used to predict how a dynamic object would react to the representative trajectory. This prediction may be used to determine potentially different costs associated with the different trajectories of the cluster with which the representative trajectory is associated. These costs may include costs to induce following behavior by the autonomous vehicle and/or cost(s) associated with conditionally available roadway portions.

Abstract: Techniques for generating more accurate determinations of object proximity by using vectors in data structures based on vehicle sensor data are disclosed. Vectors reflecting a distance and direction to a nearest object edge from a reference point in a data structure are used to determine a distance and direction from a point of interest in an environment to a nearest surface. In some examples, a weighted average query point response vector is determined using the determined distance vectors of cells neighboring the cell in which the point of interest is located and nearest to the same object as the query point, providing a more accurate estimate of the distance to the nearest object from the point of interest.

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 representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

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 modeling the probability distribution of errors in perception systems are discussed herein. For example, techniques may include modeling error distribution for attributes such as position, size, pose, and velocity of objects detected in an environment, and training a mixture model to output specific error probability distributions based on input features such as object classification, distance to the object, and occlusion. The output of the trained model may be used to control the operation of a vehicle in an environment, generate simulations, perform collision probability analyses, and to mine log data to detect collision risks.

Abstract: Techniques for generating more accurate determinations of object proximity by using vectors in data structures based on vehicle sensor data are disclosed. Vectors reflecting a distance and direction to a nearest object edge from a reference point in a data structure are used to determine a distance and direction from a point of interest in an environment to a nearest surface. In some examples, a weighted average query point response vector is determined using the determined distance vectors of cells neighboring the cell in which the point of interest is located and nearest to the same object as the query point, providing a more accurate estimate of the distance to the nearest object from the point of interest.

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 representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Abstract: A vehicle computing system may implement techniques to determine an action for a vehicle to take based on a cost associated therewith. The cost may be based in part on the effect of the action on an object (e.g., another vehicle, bicyclist, pedestrian, etc.) operating in the environment. The vehicle computing system may detect the object based on sensor data and determine an object trajectory based on a predicted reaction of the object to the vehicle performing the action. The vehicle computing system may determine costs associated with safety, comfort, progress, and/or operating rules for each action the vehicle could take based on the action and/or the predicted object trajectory. In some examples, the lowest cost action may be selected for the vehicle to perform.

Abstract: A vehicle computing system may implement techniques to determine an action for a vehicle to take based on a cost associated therewith. The cost may be based in part on the effect of the action on an object (e.g., another vehicle, bicyclist, pedestrian, etc.) operating in the environment. The vehicle computing system may detect the object based on sensor data and determine an object trajectory based on a predicted reaction of the object to the vehicle performing the action. The vehicle computing system may determine costs associated with safety, comfort, progress, and/or operating rules for each action the vehicle could take based on the action and/or the predicted object trajectory. In some examples, the lowest cost action may be selected for the vehicle to perform.