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: 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: 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: A large-scale axle intelligent cross wedge rolling mill for rail transit includes a main transmission device, a memorial arch unit, two worm-gear pressing devices, a roll assembly and two guide devices. The separation sleeves are engaged with the upper slide shaft and the lower slide shaft, respectively. Two lower shaft necks are detachably connected with the left end surface and the right end surface of the lower roller, respectively; two upper shaft necks are detachably connected with the left end surface and the right end surface of the upper roller, respectively, so that the quick disconnection of the upper and lower rollers with the upper and lower shaft necks is able to be achieved, so as to quickly operate and install the roll to meet the requirement of quick mold replacement, thus improving the flexibility of rolling.