Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems
  Autonomous driving has attracted great attention from both academics and
industries. To realise autonomous driving, Deep Imitation Learning (DIL) is
treated as one of the most promising solutions, because it improves autonomous
driving systems by automatically learning a complex mapping from human driving
data, compared to manually designing the driving policy. However, existing DIL
methods cannot generalise well across domains, that is, a network trained on
the data of source domain gives rise to poor generalisation on the data of
target domain. In the present study, we propose a novel brain-inspired deep
imitation method that builds on the evidence from human brain functions, to
improve the generalisation ability of deep neural networks so that autonomous
driving systems can perform well in various scenarios. Specifically, humans
have a strong generalisation ability which is beneficial from the structural
and functional asymmetry of the two sides of the brain. Here, we design dual
Neural Circuit Policy (NCP) architectures in deep neural networks based on the
asymmetry of human neural networks. Experimental results demonstrate that our
brain-inspired method outperforms existing methods regarding generalisation
when dealing with unseen data. Our source codes and pretrained models are
available at
https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems}{https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems.
