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 determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.

Abstract: A hierarchical tree search may determine different costs associated with a same candidate action for different levels of the hierarchy, each of which may be associated with different cost function(s) and respective objective(s), such as safety, progress, comfort, and the like. At each level, the hierarchical tree search may determine an upper and lower bound cost for the candidate action that may be based on the cost function(s) for that level. The hierarchical tree search may mask out for subsequent level(s) any candidate actions at a level of the tree that exceed the lowest upper or lower bound cost of any candidate action at that level by more than a slack amount.

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: Techniques for determining a vehicle trajectory that causes a vehicle to navigate in an environment relative to one or more objects are described herein. For example, the techniques may include a computing device determining a decision tree having nodes to represent different object intents and/or nodes to represent vehicle actions at a future time. A tree search algorithm can search the decision tree to evaluate potential interactions between the vehicle and the one or more objects over a time period, and output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.