Abstract: An unmanned aerial vehicle and a fail-safe method thereof are provided. The unmanned aerial vehicle includes at least one actuator, a failure processing circuit, and a flight controller. The actuator is configured to drive the flight behavior of the unmanned aerial vehicle. The failure processing circuit is configured to: define a corresponding relationship between the multiple failure states and the multiple protection measures, wherein each protection measure is respectively defined with a priority level and each protection measure is used to correspondingly change the flight behavior of the unmanned aerial vehicle; determine multiple current failure states when the flight behavior takes place; and select, according to the corresponding relationship, the selected protection measure having the highest priority level among the protection measures corresponding to the current failure state.

Abstract: A method, apparatus, and system for adjusting an operational flight envelope for an aircraft. An airspeed of the aircraft is received from a sensor system in the aircraft. An amount of change in the airspeed of the aircraft is determined. A current ceiling of the operational flight envelope for the aircraft is adjusted based on the amount of change in the airspeed of the aircraft.

Abstract: One variation of a method for temporally calibrating sensor streams in an autonomous vehicle includes: deriving a first set of longitudinal velocities of a reference point on the autonomous vehicle from a sequence of inertial data recorded over a period of time; deriving a second set of longitudinal velocities of the reference point based on features detected in a set of LIDAR frames recorded during the period of time; calculating a LIDAR sensor offset time that approximately minimizes a difference between the first set of longitudinal velocities and the second set of longitudinal velocities; and, in response to the LIDAR sensor offset time approximating an previous LIDAR sensor offset time, verifying operation of the LIDAR sensor during the first period of time.

Abstract: A transportation mobility system includes a data storage configured to maintain vehicle data indicating fuel consumption and count of passengers for vehicles of a transportation system, and user data describing movements of the passengers within the transportation system. The system also includes an emissions monitoring portal, programmed to provide, for vehicles of a fleet, estimates of pollutant emissions for the fleet and a percent share of miles completed by zero-emissions transportation for the fleet.

Abstract: The present disclosure is directed toward methods, non-transitory computer-readable media, and systems for trajectory optimization in a high-altitude, long-endurance aircraft. For example, the systems described herein can generate an optimized flight plan for an aircraft during active flight of the aircraft by utilizing a greedy algorithm with buffering. In one or more embodiments, the systems described herein identify a plurality of possible states and select a predetermined number of the top possible states (based on energy change associated with transitioning to each possible state) at each incremental time period within a flight time window starting from an initial state to a plurality of possible states for a final incremental time period. Furthermore, in some embodiments, the systems described herein select a final state based on a final energy associated with the final state and determine a flight plan for the aircraft from the initial state to the selected final state.

Abstract: A method, computer system, and computer program product for using a radio frequency trigger to move an unmanned aerial vehicle from an incoming vehicle path are provided. The embodiment may include receiving, by a processor, a radio frequency signal transmitted from a vehicle or an emergency response center. The embodiment may also include determining whether an unmanned aerial vehicle is in flight within a pre-configured threshold radius from the first responder vehicle or emergency response center. The embodiment may further include in response to determining the unmanned aerial vehicle is within the pre-configured radius, activating a “return home” function. The embodiment may also include commanding the unmanned aerial vehicle to fly out of the threshold radius.

Abstract: A receding horizon state estimator estimates state of a vehicle such as to reduce total communication cost of acquiring external measurements over a prediction horizon, in which state estimation accuracy for a time step is a function of state estimation accuracy for a previous time step. For each time step of the prediction horizon, estimator selects a subset of external sensors with external measurements sufficient to estimate the state with accuracy satisfying the constraint on state estimation accuracy for the corresponding time step while reducing a total communication cost of acquiring the external measurements over the prediction horizon. The estimator requests the external measurements from the subset of external sensors determined for a current time step and estimates the state of the vehicle using the internal and the requested external measurements.

Abstract: A method of generating a path for an autonomous driving vehicle (ADV) includes obtaining a plurality of path inputs including a lateral and a longitudinal starting state, a threshold lateral jerk, and a set of static obstacle boundaries with respect to a reference line, obtaining a plurality of path constraints related to the threshold lateral jerk, avoidance of static obstacles, and a threshold lateral velocity, obtaining a cost function associated with a path objective, the cost function comprising first, second, and third terms relating to cumulative lateral distances, to cumulative first order lateral rates of change, and to cumulative second order lateral rates of change, respectively, generating a plurality of planned ADV states as path results based on the plurality of path inputs, the plurality of path constraints, and the cost function and generating control signals to control the ADV based on the plurality of planned ADV states.

Abstract: Automated driving systems, control logic, and methods execute maneuver criticality analysis to provide intelligent vehicle operation in transient driving conditions. A method for controlling an automated driving operation includes a vehicle controller receiving path plan data with location, destination, and predicted path data for a vehicle. From the received path plan data, the controller predicts an upcoming maneuver for driving the vehicle between start and goal lane segments. The vehicle controller determines a predicted route with lane segments connecting the start and goal lane segments, and segment maneuvers for moving the vehicle between the start, goal, and route lane segments. A cost value is calculated for each segment maneuver; the controller determines if a cost values exceeds a corresponding criticality value. If so, the controller commands a resident vehicle subsystem to execute a control operation associated with taking the predicted route.