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

--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. T o 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. Autonomous driving [1], [2] is an advanced technology that intelligent vehicles perceive road environments through onboard sensor systems, autonomously plan driving routes, and control vehicles to reach predetermined destinations. Its technical system generally includes three major parts: environmental perception, decision planning, and vehicle control [3], involving multiple research fields such as computer science, mathematics, mechanical engineering, control science, and psychology [4]. However, the current autonomous driving systems still suffer from insufficient interpretability due to the existence of "black box" nature of deep learning models [5], greatly limiting the credibility and widespread application of various perception and decision-making methods in practical engineering. Even though the use of generative adversarial networks [6] to generate explanatory data related to decision-making has been attempted, the quality of such data is often substandard, and the training process is quite challenging.