Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.

Abstract: Described herein are compounds that are inhibitors of autophagy and their use in the treatment of disorders such as cancers.

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 described herein relate to lane handling, for instance to enable vehicles to perform turns without colliding into oncoming vehicles and/or bicycles in other lanes. System(s) associated with a vehicle can access sensor data and/or map data associated with an environment within which the vehicle is positioned in a first lane. The system(s) can determine that the vehicle is to perform a turn and a start of a second lane associated with the turn or a merging zone associated with the second lane. The system(s) can determine a location of the vehicle relative to the start of the second lane or the merging zone associated with the second lane and, based partly on the location, can cause the vehicle to merge into the second lane prior to performing the turn.

Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.

Abstract: The present disclosure provides BET inhibitors of the formula: wherein the variables are defined herein, as well as pharmaceutical compositions thereof. The present disclosure also provides methods of treating a patient comprising administering a bromo- and extra-terminal (BET) domain inhibitor for the treatment of FSHD which modulates DUX4 expression. In some embodiments, the present methods comprise using one or more BET inhibitors as a therapeutic agent for the treatment of FSHD patients including patients who are being treated with one or more palliative treatments such as therapy and/or agents which lead to increased muscle mass.

Abstract: A vehicle computing system may implement techniques to determine an action for a vehicle to take based on a cost associated therewith. The cost may be based in part on the effect of the action on an object (e.g., another vehicle, bicyclist, pedestrian, etc.) operating in the environment. The vehicle computing system may detect the object based on sensor data and determine an object trajectory based on a predicted reaction of the object to the vehicle performing the action. The vehicle computing system may determine costs associated with safety, comfort, progress, and/or operating rules for each action the vehicle could take based on the action and/or the predicted object trajectory. In some examples, the lowest cost action may be selected for the vehicle to perform.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Described herein are compounds that are inhibitors of autophagy and their use in the treatment of disorders such as cancers.

Abstract: In autonomous driving, it is often useful to plan trajectories in a curvilinear coordinate frame with respect to some reference trajectory, like a path produced by a hi-level route planner. This disclosure includes techniques for developing efficient approximate path coordinate motion primitives appropriate for fast planning in autonomous driving scenarios. These primitives are approximate in that particular quantities, like the path length, acceleration, and track offset trajectory, are known with some degree of certainty, and values that depend on the curvature of the reference path can be bound. Such approximate motion primitives can be used to control the autonomous vehicle to follow the trajectory in an environment.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

Abstract: Techniques described herein relate to lane handling, for instance to enable vehicles to perform turns without colliding into oncoming vehicles and/or bicycles in other lanes. System(s) associated with a vehicle can access sensor data and/or map data associated with an environment within which the vehicle is positioned in a first lane. The system(s) can determine that the vehicle is to perform a turn and a start of a second lane associated with the turn or a merging zone associated with the second lane. The system(s) can determine a location of the vehicle relative to the start of the second lane or the merging zone associated with the second lane and, based partly on the location, can cause the vehicle to merge into the second lane prior to performing the turn.

Abstract: Techniques described herein are directed to classifying lanes in an environment of a vehicle for, for example, performing lane handling. In an example, system(s) of a vehicle can determine a signal indicative of a presence of the vehicle in a lane of a drivable surface in an environment within which the vehicle is located. The system(s) can determine, based at least in part on the signal, a classification of the lane as at least one of occupied (an object is at least partially in the lane), unoccupied (no object in the lane), and/or established (e.g., where an object has established a priority in the lane). The system(s) can control the vehicle based at least in part on the classification of the lane to improve safety in scenarios, for example, including merging.

Abstract: A vehicle computing system may implement techniques to determine an action for a vehicle to take based on a cost associated therewith. The cost may be based in part on the effect of the action on an object (e.g., another vehicle, bicyclist, pedestrian, etc.) operating in the environment. The vehicle computing system may detect the object based on sensor data and determine an object trajectory based on a predicted reaction of the object to the vehicle performing the action. The vehicle computing system may determine costs associated with safety, comfort, progress, and/or operating rules for each action the vehicle could take based on the action and/or the predicted object trajectory. In some examples, the lowest cost action may be selected for the vehicle to perform.

Abstract: A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

Abstract: A vehicle computing system may identify an obstruction along a route of travel and may connect to a service computing device for guidance. The service computing device may include a guidance system configured to receive waypoint and/or orientation input from an operator. The operator may evaluate the scenario and determine one or more waypoints and/or associated orientations for the vehicle to navigate the scenario. In some examples, the guidance system may validate the waypoint(s) and/or associated orientation(s). The service computing device may send the waypoint(s) and/or associated orientation(s) to the vehicle computing system. The vehicle computing system may validate the waypoint(s) and/or associated orientation(s) and, based on the validation, control the vehicle according to the input. Based on a determination that the vehicle has navigated the scenario, the guidance system may release vehicle guidance back to the vehicle computing 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: Described herein are compounds that are inhibitors of autophagy and their use in the treatment of disorders such as cancers.

Abstract: Described herein are compounds that are inhibitors of autophagy and their use in the treatment of disorders such as cancers.

Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.