Application of a Brain-Inspired Deep Imitation Learning Algorithm in Autonomous Driving

Hasan Bayarov Ahmedov* (Illustrator), Dewei Yi, Jie Sui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 DNN 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 DNN 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.
Original languageEnglish
Article number100165
JournalSoftware Impacts
Early online date30 Oct 2021
DOIs
Publication statusE-pub ahead of print - 30 Oct 2021

Keywords

  • Brain-inspired AI
  • Imitation Learning
  • Autonomous Vehicles

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