Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.

Abstract: An acoustic detection system of an aircraft receives a first signal, where the first signal is a multichannel audio signal. The multichannel audio signal is determined to be associated with at least one intruding aircraft based on the multichannel audio signal. An avoidance maneuver is commanded for the aircraft based on a track of the intruding aircraft generated based on the multichannel audio signal and a second signal providing additional information about the intruding aircraft.

Abstract: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

Abstract: An over actuated system capable of controlling wheel parameters, such as speed (e.g., by torque and braking), steering angles, caster angles, camber angles, and toe angles, of wheels in an associated vehicle. The system may determine the associated vehicle is in a rollover state and adjust wheel parameters to prevent vehicle rollover. Additionally, the system may determine a driving state and dynamically adjust wheel parameters to optimize driving, including, for example, cornering and parking. Such a system may also dynamically detect wheel misalignment and provide alignment and/or corrective driving solutions. Further, by utilizing degenerate solutions for driving, the system may also estimate tire-surface parameterization data for various road surfaces and make such estimates available for other vehicles via a network.

Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.

Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.

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.

Abstract: Techniques for communicating feedback to passengers of autonomous vehicles regarding reasons for actions taken by autonomous vehicles to build trust with passengers are described herein. For instance, an autonomous vehicle may associate various objects with symbols and/or predicates while traversing a path to evaluate Linear Temporal Logic (LTL) formulae. Events along the path may require the autonomous vehicle to perform an action. The vehicle may determine to communicate the event and/or action to the passenger to provide a reason as to why the autonomous vehicle took the action, based on evaluation of the LTL formulae. In some examples, the autonomous vehicle may communicate with passengers via one or more of visual cues, auditory cues, and/or haptic cues. In this way, autonomous vehicles may build trust with passengers by reassuring and informing passengers of reasons for taking actions either before, during, or after the action is taken.

Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.

Abstract: Techniques for determining a trajectory for an autonomous vehicle are described herein. In general, determining a route can include utilizing a search algorithm such as Monte Carlo Tree Search (MCTS) to search for possible trajectories, while using temporal logic formulas, such as Linear Temporal Logic (LTL), to validate or reject the possible trajectories. Trajectories can be selected based on various costs and constraints optimized for performance. Determining a trajectory can include determining a current state of the autonomous vehicle, which can include determining static and dynamic symbols in an environment. A context of an environment can be populated with the symbols, features, predicates, and LTL formula. Rabin automata can be based on the LTL formula, and the automata can be used to evaluate various candidate trajectories. Nodes of the MCTS can be generated and actions can be explored based on machine learning implemented as, for example, a deep neural network.