Abstract: A vehicle may include a primary system and a secondary system to validate operation of the primary system and to control the vehicle to avoid collisions. For example, the secondary system may receive multiple trajectories from the primary system, such as a primary trajectory and a secondary, contingent, trajectory associated with a deceleration or other maneuver. The secondary system may determine if a trajectory is associated with a potential collision, if the trajectory is consistent with a current or previous pose, if the trajectory is compatible with a capability of the vehicle, etc. The secondary system may select the primary trajectory if valid, the secondary trajectory if the primary trajectory is invalid, or another trajectory generated by the secondary system if the primary trajectory and the secondary trajectory are invalid. If no valid trajectory is determined, the vehicle may decelerate at a maximum rate.

Abstract: Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, CPU-usage associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined CPU-usage. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomalous event.

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: Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, CPU-usage associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined CPU-usage. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomalous event.

Abstract: A vehicle may include a primary system and a secondary system to validate operation of the primary system and to control the vehicle to avoid collisions. For example, the secondary system may receive multiple trajectories from the primary system, such as a primary trajectory and a secondary, contingent, trajectory associated with a deceleration or other maneuver. The secondary system may determine if a trajectory is associated with a potential collision, if the trajectory is consistent with a current or previous pose, if the trajectory is compatible with a capability of the vehicle, etc. The secondary system may select the primary trajectory if valid, the secondary trajectory if the primary trajectory is invalid, or another trajectory generated by the secondary system if the primary trajectory and the secondary trajectory are invalid. If no valid trajectory is determined, the vehicle may decelerate at a maximum rate.

Abstract: An electrical system may include a power circuit configured to provide a power output, first and second batteries, and first and second switches configured to connect and disconnect the first and second batteries, respectively, to the power output in parallel with one another. The electrical system may also include a controller electrically connected to the first and the second switches, and configured to control operation of the first switch and/or the second switch. The electrical system may also include a load predictor in communication with the controller and configured to predict power demands of an electric load on the power circuit and send a signal indicative of the predicted power demands to the controller, which may activate the first switch and/or the second switch to connect the first battery and/or the second battery to the power output based at least in part on the signal indicative of the predicted load.

Abstract: A secondary system operates on a vehicle to avoid a collision when a problem occurs with a primary system. For example, the secondary system may operate independently from the primary system to take over control of the vehicle from the primary system when the secondary system detects a potential collision, when an error occurs with the primary system, and so on. In examples, the primary system may implement first techniques, such as Artificial Intelligence (AI) techniques, to understand an environment around the vehicle and/or instruct the vehicle to move within the environment. In examples, the secondary system may implement second techniques that are based on positioning, velocity, acceleration, etc. of the vehicle and/or objects around the vehicle.

Abstract: A vehicle may include a primary system and a secondary system to validate operation of the primary system and to control the vehicle to avoid collisions. For example, the secondary system may receive multiple trajectories from the primary system, such as a primary trajectory and a secondary, contingent, trajectory associated with a deceleration or other maneuver. The secondary system may determine if a trajectory is associated with a potential collision, if the trajectory is consistent with a current or previous pose, if the trajectory is compatible with a capability of the vehicle, etc. The secondary system may select the primary trajectory if valid, the secondary trajectory if the primary trajectory is invalid, or another trajectory generated by the secondary system if the primary trajectory and the secondary trajectory are invalid. If no valid trajectory is determined, the vehicle may decelerate at a maximum rate.

Abstract: Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, latency associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined latency. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomaly.

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: A vehicle may include a primary system for generating data to control the vehicle and a secondary system that validates the data and/or other data to avoid collisions. For example, the primary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and generate a trajectory for the vehicle. The secondary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and evaluate a trajectory generated by the primary system. The secondary system may also monitor components of the vehicle to detect an error. If the secondary system detects an error with a trajectory generated by the primary system and/or an error with a component of the vehicle, the secondary system may cause the vehicle to perform a maneuver, such as decelerating, changing lanes, swerving, etc.

Abstract: Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, CPU-usage associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined CPU-usage. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomalous event.

Abstract: Performance anomalies in complex systems can be difficult to identify and diagnose. In an example, latency associated with one or more of the systems can be determined. An anomalous event can be determined based on the determined latency. In some examples, based at least in part on determining the event, the system may be controlled in a safe state and/or reconfigured to obviate the anomaly.

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: A vehicle may include a primary system and a secondary system to validate operation of the primary system and to control the vehicle to avoid collisions. For example, the secondary system may receive multiple trajectories from the primary system, such as a primary trajectory and a secondary, contingent, trajectory associated with a deceleration or other maneuver. The secondary system may determine if a trajectory is associated with a potential collision, if the trajectory is consistent with a current or previous pose, if the trajectory is compatible with a capability of the vehicle, etc. The secondary system may select the primary trajectory if valid, the secondary trajectory if the primary trajectory is invalid, or another trajectory generated by the secondary system if the primary trajectory and the secondary trajectory are invalid. If no valid trajectory is determined, the vehicle may decelerate at a maximum rate.

Abstract: A secondary system operates on a vehicle to avoid a collision when a problem occurs with a primary system. For example, the secondary system may operate independently from the primary system to take over control of the vehicle from the primary system when the secondary system detects a potential collision, when an error occurs with the primary system, and so on. In examples, the primary system may implement first techniques, such as Artificial Intelligence (AI) techniques, to understand an environment around the vehicle and/or instruct the vehicle to move within the environment. In examples, the secondary system may implement second techniques that are based on positioning, velocity, acceleration, etc. of the vehicle and/or objects around the vehicle.

Abstract: A method includes receiving, by a processor of a data collector, a request for sensor data related to an event. The method also includes sending a plurality of requests for the sensor data to a plurality of on-board units (OBUs), respectively, where the plurality of OBUs is associated with a plurality of vehicles, respectively. The method further includes receiving a plurality of responses from the plurality of OBUs, respectively, wherein each response of the plurality of responses includes a sensor data item related to the event. In more specific embodiments the plurality of requests are sent to the plurality of OBUs based on the plurality of OBUs being located within a certain proximity to the event. In yet further embodiments, each sensor data item of the plurality of responses is encapsulated with a respective tag.

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: 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: 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.