模仿大脑结构设计自动驾驶系统，提升环境感知与决策能力。
A Brain-Inspired Perception-Decision Driving Model Based on Neural Pathway Anatomical Alignment
- 基于大脑神经通路结构构建感知-决策一体化模型
- 在复杂交通场景下实现端到端自动驾驶成功落地
- 为可解释性与鲁棒性提升提供生物启发新思路
在自动驾驶领域，传统方法依赖传感器输入与规则算法进行车辆感知与决策，但在复杂交通场景中常因缺乏可解释性和鲁棒性而表现不佳。为此，本文提出一种新型脑启发驾驶（Brain-Inspired Driving, BID）框架。不同于传统方法，该框架利用脑启发式感知技术实现更高效、鲁棒的环境感知，并结合脑启发式决策机制支持智能决策。实验结果表明，该模型在多种自动驾驶任务中性能显著提升，成功实现了端到端自动驾驶。本研究不仅增强了系统的可解释性与鲁棒性，还为自动驾驶技术的进一步发展提供了新颖的理论见解与方法路径。
原文摘要 · Abstract (English)
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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