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.