SIMULATION-BASED LEARNING OF DRIVER INTERACTIONS THROUGH A VEHICLE WINDOW

A model can be trained to detect interactions of other drivers through a window of their vehicle. A human driver behind a window (e.g., front windshield) of a vehicle can be detected in a real-world driving data. The human driver can be tracked over time through the window. The real-world driving data can be augmented by replacing at least a portion of the human driver with at least a portion of a virtual driver performing a target driver interaction to generate an augmented real-world driving dataset. The target driver interaction can be a gesture or a gaze. Using the augmented real-world driving data set, a machine learning model can be trained to detect the target driver interactions. Thus, simulation can be leveraged to provide a large set of useful training data without having to acquire real-world data of drivers performing target driver interactions as viewed from outside the vehicle.