Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems

by   Hasan Bayarov Ahmedov, et al.

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


Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

The decision and planning system for autonomous driving in urban environ...

On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks

This paper proposes a strategy for visual prediction in the context of a...

HVS-Inspired Signal Degradation Network for Just Noticeable Difference Estimation

Significant improvement has been made on just noticeable difference (JND...

Multi-task Learning with Attention for End-to-end Autonomous Driving

Autonomous driving systems need to handle complex scenarios such as lane...

Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents

Autonomous driving is a multi-task problem requiring a deep understandin...

Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula

ML-based motion planning is a promising approach to produce agents that ...

Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

In this paper, we explore the potential of using a large language model ...

Please sign up or login with your details

Forgot password? Click here to reset