Trajectory prediction of third-party objects using temporal logic and tree search

Abstract

Techniques for generating trajectories for autonomous vehicles and for predicting trajectories for third-party objects using temporal logic and tree search are described herein. Perception data about an environment can be captured to determine static objects and dynamic objects. For a particular dynamic object, which can represent a third-party vehicle, predictive trajectories can be generated to represent possible trajectories based on available options and rules of the road. Operations can include determining probabilities that a third-party vehicle will execute a predictive trajectory and updating the probabilities over time as motion data is captured. Predictive trajectories can be provided to the autonomous vehicle and commands for the autonomous vehicle can be based on the predictive trajectories. Further, determining a trajectory can include utilizing a Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using Linear Temporal Logic (LTL) formulas to validate or reject the possible trajectories.