Autonomous Driving Based on Imitation and Active Inference

We advance a novel computational model of acquiring a hierarchical action sequence and its use for minimizing the divergence between observation and prediction. The model is grounded in a principled framework to learn and cognize the dynamic surrounding of a learning agent, which exemplifies the model's functioning by presenting a simulation of overtaking scenarios in autonomous driving. The learning agent integrates imitation learning and active inference to form hierarchical representations from expert demonstrations. During the online learning phase, the learning agent improves the action selection procedure based on the prior knowledge (exploiting) and the novel interactions with a dynamic environment (exploring). The proposed method applies an active knowledge sampling during the learning agent's movements to make a dynamic inference in the Bayesian structure by message passing through the multi-levels. A line-changing driving scenario with different levels of complexity is organized to verify the proposed framework's efficiency by dealing with single and multiple dynamic objects interacting in the environment.