Task-Driven Domain-Agnostic Learning with Information Bottleneck for Autonomous Steering

Environments for autonomous driving can vary from place to place, leading to challenges in designing a learning model for a new scene. Transfer learning can leverage knowledge from a learned domain to a new domain with limited data. In this work, we focus on end-to-end autonomous driving as the target task, consisting of both perception and control. We first utilize information bottleneck analysis to build a causal graph that defines our framework and the loss function; then we propose a novel domain-agnostic learning method for autonomous steering based on our analysis of training data, network architecture, and training paradigm. Experiments show that our method outperforms other SOTA methods.