Abstract: Example embodiments relate to self-supervisory and automatic response techniques and systems. A computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. The computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. The computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. The computing system may then adjust a control strategy for the vehicle based on the comparison.

Abstract: Example embodiments relate to self-supervisory and automatic response techniques and systems. A computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. The computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. The computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. The computing system may then adjust a control strategy for the vehicle based on the comparison.

Abstract: Example embodiments relate to self-supervisory and automatic response techniques and systems. A computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. The computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. The computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. The computing system may then adjust a control strategy for the vehicle based on the comparison.

Abstract: The systems and methods described herein provide a mechanism for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, or precipitable water and other atmospheric-based phenomena based upon these sensor readings, and predicting future precipitation and atmospheric-based phenomena.

Abstract: This disclosure provides methods of manufacturing large-scale blended preparations comprising one or more amino acids wherein the preparations have certain properties.

Abstract: The systems and methods described herein provide a mechanism for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, or precipitable water and other atmospheric-based phenomena based upon these sensor readings, and predicting future precipitation and atmospheric-based phenomena.

Abstract: The systems and methods described herein provide a mechanism for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, or precipitable water and other atmospheric-based phenomena based upon these sensor readings, and predicting future precipitation and atmospheric-based phenomena.

Abstract: This disclosure provides compositions and methods for improving the dispersion of a hydrophobic amino acid entity in an aqueous suspension. This disclosure further provides compositions and methods for improving the stability of N-acetylcysteine in the presence of acetyl acceptors, e.g., carnitine.

Abstract: The systems and methods described herein provide a mechanism for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, or precipitable water and other atmospheric-based phenomena based upon these sensor readings, and predicting future precipitation and atmospheric-based phenomena.

Abstract: This disclosure provides methods of manufacturing large-scale blended preparations comprising one or more amino acids wherein the preparations have certain properties.