REINFORCEMENT LEARNING BASED APPROACH FOR SAE LEVEL-4 AUTOMATED LANE CHANGE

摘要

A method for automatically initiating a change of lane in an automated automotive vehicle. Sensory data is combined in a sensory fusion processor to generate a stack of semantic images of a sensed vehicular driving environment. The stack is used in a reinforcement learning system using a Markov Decision Process in order to optimize a neural network of an automated lane change system.