Abstract: This document describes apparatuses and techniques for enabling a user to authorize access to early boot debugging of hardware issues in a computing system. In general, responsive to a user consenting to early boot debugging, a debug reenable signal is set in nonvolatile storage. Upon reinitialization of the computing system and receipt of an early debugging request, if the debug reenable signal is detected and the early debugging request is authenticated, an early boot debugging system is enabled. The user consent to early boot debugging may circumvent the setting of a fuse that otherwise would prevent performance of early boot debugging.

Abstract: Various embodiments include methods and systems for managing vehicle behavior. In some embodiments, a vehicle processor of the first vehicle may receive dynamic traffic flow feature information relevant to movements of a second vehicle within a predetermined proximity to the host vehicle, determine probabilities of a plurality of potential behaviors of the second vehicle based on the received dynamic traffic flow feature information, predict a future path of the second vehicle, and use the predicted future path of the second vehicle in a vehicle control function. In some embodiments, the vehicle processor of the first vehicle may predict a behavior of a third vehicle based on received intention information about the second vehicle, and may adjust a behavior of the first vehicle based on the predicted behavior of the third vehicle.

Abstract: Various embodiments include methods and devices for training and implementing a tree-based behavior prediction model for use in autonomous vehicle control systems. Some embodiments may include labeling real-world autonomous vehicle run data to indicate an insight of the data, selecting an insight decision tree of the tree-based behavior prediction model for training using the labeled data, training the insight decision tree using the labeled data to classify a probability of an insight associated with the insight decision tree, and updating the tree-based behavior prediction model based on training the insight decision tree. Some embodiments may include selecting an insight decision tree of a tree-based behavior prediction model configured for classifying a probability of an insight associated with the insight decision tree, executing the insight decision tree, and outputting a probability of an insight determined from executing the insight decision tree using the data.

Abstract: This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera.

Abstract: This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera.

Abstract: Some embodiments are directed to a computer-assisted method for identifying a vehicle. The computer-assisted method can include: receiving, from a stationary sensor, sensor data representing a plurality of moving vehicles; receiving, from a particular vehicle, a communication including sensor data representing the particular vehicle, wherein the sensor data includes at least one of velocity and position for the particular vehicle; and identifying, from the sensor data representing a plurality of moving vehicles, a subset of the data representing the particular vehicle, wherein identifying the subset of data comprises analyzing the sensor data received from the stationary sensor in conjunction with the sensor data received from the particular vehicle.

Abstract: Some embodiments are directed to a computer-assisted method for identifying a vehicle. The computer-assisted method can include: receiving, from a stationary sensor, sensor data representing a plurality of moving vehicles; receiving, from a particular vehicle, a communication including sensor data representing the particular vehicle, wherein the sensor data includes at least one of velocity and position for the particular vehicle; and identifying, from the sensor data representing a plurality of moving vehicles, a subset of the data representing the particular vehicle, wherein identifying the subset of data comprises analyzing the sensor data received from the stationary sensor in conjunction with the sensor data received from the particular vehicle.