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

TL;DR

This paper introduces a brain-inspired autonomous driving framework that enhances perception and decision-making, improving robustness and interpretability in complex traffic scenarios, and demonstrating successful end-to-end autopilot performance.

Contribution

It presents a novel brain-inspired perception-decision model for autonomous driving, integrating neural pathway alignment to improve robustness and interpretability over traditional methods.

Findings

Significant performance improvements in autonomous driving tasks.

Successful implementation of end-to-end autopilot.

Enhanced robustness and interpretability in complex scenarios.

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.