Using a long-term recurrent convolutional network to plan a sequence of lateral controls in autonomous driving

Abstract

Systems and methods for planning and executing a sequence of lateral controls for a vehicle comprise determining a set of parameters comprising road lane lines, a pose of the vehicle, a movement state of the vehicle, and actions by a driver of the vehicle and, while operating the vehicle in an autonomous driving mode, obtaining a long-term recurrent convolutional network (LRCN) model that has been trained with training data captured during controlled test driving by a human driver, using the LRCN model and the set of parameters as inputs, determining a sequence of a plurality of desired steering wheel angles for the vehicle, and when the sequence of the plurality of desired steering wheel angles is verified via comparison to one or more mathematical models, controlling steering of the vehicle according to the sequence of a plurality of desired steering wheel angles.