1. A system for implementing a control algorithm for an autonomous vehicle, the system comprising: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive one or more symbols, the one or more symbols including at least one static symbol or at least one dynamic symbol; determine, based at least in part on the one or more symbols, one or more features; determine, based at least in part on the one or more symbols or the one or more features, one or more predicates; determine, based at least in part on the one or more symbols, one or more features, or one or more predicates, one or more linear temporal logic (LTL) formulas; determine, based at least in part on the one or more LTL formulas, one or more automaton; utilize a Monte Carlo Tree Search (MCTS) to generate one or more candidate trajectories; evaluate the one or more candidate trajectories using the one or more automaton, wherein the one or more automaton verifies that the one or more candidate trajectories satisfies the one or more LTL formulas associated with the one or more automaton; determine, based at least in part on a cost function, a cost associated with a trajectory of the one or more candidate trajectories; select, as a selected trajectory, the trajectory of the one or more candidate trajectories based at least in part on the cost; and control the autonomous vehicle based at least in part on the selected trajectory.
1. A system for implementing a control algorithm for an autonomous vehicle, the system comprising: one or more processors; and one or more computer readable storage media communicatively coupled to the one or more processors and storing instructions that are executable by the one or more processors to: receive one or more symbols, the one or more symbols including at least one static symbol or at least one dynamic symbol; determine, based at least in part on the one or more symbols, one or more features; determine, based at least in part on the one or more symbols or the one or more features, one or more predicates; determine, based at least in part on the one or more symbols, one or more features, or one or more predicates, one or more linear temporal logic (LTL) formulas; determine, based at least in part on the one or more LTL formulas, one or more automaton; utilize a Monte Carlo Tree Search (MCTS) to generate one or more candidate trajectories; evaluate the one or more candidate trajectories using the one or more automaton, wherein the one or more automaton verifies that the one or more candidate trajectories satisfies the one or more LTL formulas associated with the one or more automaton; determine, based at least in part on a cost function, a cost associated with a trajectory of the one or more candidate trajectories; select, as a selected trajectory, the trajectory of the one or more candidate trajectories based at least in part on the cost; and control the autonomous vehicle based at least in part on the selected trajectory. 6. The system of claim 1 , wherein a first node of the MCTS includes a snapshot of an environment including the one or more symbols, the one or more features, the one or more predicates, and the one or more LTL formulas, and wherein the instructions are further executable by the one or more processors to add a second node to the MCTS that includes an update of the environment associated with the snapshot over time.