Abstract: Techniques are discussed herein for determining truncated simulation regions within a parameter space of simulation scenarios, such as driving scenarios used to analyze and evaluate the responses of autonomous vehicle controllers. Using non-sampling-based parameter selection techniques, parameterized scenarios may be executed as simulations to determine the truncated simulation region. Sampling-based parameter selection techniques may be used to determine additional parameterized scenarios, which may be compared to the truncated simulation region. Parameterized scenarios within the truncated simulation region may be executed as simulations and scenarios outside of the truncated simulation region may be excluded, and the aggregated results may be analyzed to determine scenario/vehicle performance metrics across the scenario parameter space.

Abstract: A control system includes one or more levels of control of power and energy. At one level, a first controller optimally divides power between two or more processes, to maximize instantaneous production, for a given amount of currently available power. In the case of EDR desalination, electric power is optimally divided between ion exchange membranes and pumps to maximize instantaneous production of desalinated water for a given amount of available electric power. Optionally, at another level, a second controller divides time-varying power between the processes fed by the first level controller and an energy storage unit, based on a prediction of future power availability and a function. In the EDR case, power generated by a photovoltaic array is divided between the EDR desalination process and a battery, based on a prediction of future PV power availability and a function, to ensure reliable water production in the future.

Abstract: Localization error monitoring using vehicle state information is described. A computing system associated with a vehicle may determine a current state of the vehicle, such as a location, position, orientation, velocity, acceleration, or the like. The vehicle computing system may determine one or more metrics associated with the state of the vehicle. The metrics may include a variance associated with the state, a residual associated with a measurement corresponding to the state, or a correction factor applied to correct an input error. The computing system may determine whether the metric exceeds a threshold value. Based on a threshold exceedance, the computing system may determine one or more errors associated with a state of the vehicle. The vehicle computing system may control the vehicle based on the one or more errors.

Abstract: Localization error monitoring using vehicle state information is described. A computing system associated with a vehicle may determine a current state of the vehicle, such as a location, position, orientation, velocity, acceleration, or the like. The vehicle computing system may determine one or more metrics associated with the state of the vehicle. The metrics may include a variance associated with the state, a residual associated with a measurement corresponding to the state, or a correction factor applied to correct an input error. The computing system may determine whether the metric exceeds a threshold value. Based on a threshold exceedance, the computing system may determine one or more errors associated with a state of the vehicle. The vehicle computing system may control the vehicle based on the one or more errors.