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arXiv · 2502.16027

A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment

Haidong Wang·Pengfei Xiao·Ao Liu·Qia Shan·Jianhua Zhang
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Abstract

In the realm of autonomous driving, conventional approaches for vehicle perception and decision-making primarily rely on sensor input and rule-based algorithms. However, these methodologies often suffer from lack of interpretability and robustness, particularly in intricate traffic scenarios. To tackle this challenge, we propose a novel brain-inspired driving (BID) framework. Diverging from traditional methods, our approach harnesses brain-inspired perception technology to achieve more efficient and robust environmental perception. Additionally, it employs brain-inspired decision-making techniques to facilitate intelligent decision-making. The experimental results show that the performance has been significantly improved across various autonomous driving tasks and achieved the end-to-end autopilot successfully. This contribution not only advances interpretability and robustness but also offers fancy insights and methodologies for further advancing autonomous driving technology.

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Haidong Wang, Pengfei Xiao, Ao Liu, Qia Shan, Jianhua Zhang. 2025-02-22. A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment. https://arxiv.org/abs/2502.16027

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