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.

翻译：在自动驾驶领域，传统的车辆感知与决策方法主要依赖于传感器输入和基于规则的算法。然而，这些方法通常存在可解释性和鲁棒性不足的问题，尤其是在复杂的交通场景中。为了应对这一挑战，我们提出了一种新颖的脑启发驾驶（BID）框架。与传统方法不同，我们的方法利用脑启发感知技术来实现更高效、更鲁棒的环境感知。此外，它采用脑启发决策技术来促进智能决策。实验结果表明，该模型在各种自动驾驶任务中的性能均得到显著提升，并成功实现了端到端的自动驾驶。这一贡献不仅提升了可解释性和鲁棒性，也为进一步推进自动驾驶技术提供了新颖的见解和方法论。