Abstract: Trajectory generation and/or execution architecture is described. In an example, a first signal can be determined at a first frequency, wherein the first signal comprises information associated with causing the system to move to a location. Further, a second signal can be determined at a second frequency different from the first frequency and based at least in part on the first signal. A system can be controlled to move to the location, based at least in part on the second signal.

Abstract: A vehicle control system includes variable maneuvering limits based at least in part on whether the vehicle is carrying cargo (i.e., passengers or other cargo). The system can include a cargo classification system comprising one or more internal sensors, an imager, and an image interpreter. The cargo classification system can determine if the vehicle is carrying cargo and classify the cargo (e.g., passengers or other cargo). Based at least in part on this classification, the vehicle control system can set various vehicle maneuvering limits. When the vehicle is empty, the vehicle control system can maneuver the vehicle at, or near, the actual maneuvering limits for the vehicle (e.g., maximum longitudinal acceleration, braking and lateral acceleration that can be generated by the vehicle). When the vehicle is carrying cargo, the vehicle control system can maneuver the vehicle at a lower threshold to prevent passenger discomfort and/or cargo damage or discomfort.

Abstract: A friction estimation system for estimating friction-related data associated with a surface on which a vehicle travels, may include a camera array including a plurality of imagers configured to capture image data associated with a surface on which a vehicle travels. The image data may include light data associated with the surface. The friction estimation system may also include an image interpreter in communication with the camera array and configured to receive the image data from the camera array and determine friction-related data associated with the surface based, at least in part, on the image data. The image interpreter may be configured to be in communication with a vehicle control system and provide the friction-related data to the vehicle control system.

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 generating and executing trajectories to guide autonomous vehicles are described. In an example, a first computer system associated with an autonomous vehicle can generate, at a first operational frequency, a route to guide the autonomous vehicle from a current location to a target location. The first computer system can further determine, at a second operational frequency, an instruction for guiding the autonomous vehicle along the route and can generate, at a third operational frequency, a trajectory based at least partly on the instruction and real-time processed sensor data. A second computer system that is associated with the autonomous vehicle and is in communication with the first computer system can execute, at a fourth operational frequency, the trajectory to cause the autonomous vehicle to travel along the route. The separation of the first computer system and the second computer system can provide enhanced safety, redundancy, and optimization.

Abstract: A friction estimation system for estimating friction-related data associated with a surface on which a vehicle travels, may include a camera array including a plurality of imagers configured to capture image data associated with a surface on which a vehicle travels. The image data may include light data associated with the surface. The friction estimation system may also include an image interpreter in communication with the camera array and configured to receive the image data from the camera array and determine friction-related data associated with the surface based, at least in part, on the image data. The image interpreter may be configured to be in communication with a vehicle control system and provide the friction-related data to the vehicle control system.

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 and executing trajectories to guide autonomous vehicles are described. In an example, a first computer system associated with an autonomous vehicle can generate, at a first operational frequency, a route to guide the autonomous vehicle from a current location to a target location. The first computer system can further determine, at a second operational frequency, an instruction for guiding the autonomous vehicle along the route and can generate, at a third operational frequency, a trajectory based at least partly on the instruction and real-time processed sensor data. A second computer system that is associated with the autonomous vehicle and is in communication with the first computer system can execute, at a fourth operational frequency, the trajectory to cause the autonomous vehicle to travel along the route. The separation of the first computer system and the second computer system can provide enhanced safety, redundancy, and optimization.

Abstract: A vehicle control system includes variable maneuvering limits based at least in part on whether the vehicle is carrying cargo (i.e., passengers or other cargo). The system can include a cargo classification system comprising one or more internal sensors, an imager, and an image interpreter. The cargo classification system can determine if the vehicle is carrying cargo and classify the cargo (e.g., passengers or other cargo). Based at least in part on this classification, the vehicle control system can set various vehicle maneuvering limits. When the vehicle is empty, the vehicle control system can maneuver the vehicle at, or near, the actual maneuvering limits for the vehicle (e.g., maximum longitudinal acceleration, braking and lateral acceleration that can be generated by the vehicle). When the vehicle is carrying cargo, the vehicle control system can maneuver the vehicle at a lower threshold to prevent passenger discomfort and/or cargo damage or discomfort.