类脑计算让自动驾驶更智能高效，实时响应且省电。
Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions
- 用脉冲神经网络模拟生物神经，提升系统感知与决策能力。
- 结合事件相机，实现低延迟、高能效的动态视觉感知。
- 适合研究智能机器人、无人飞行器等对能效敏感的场景。
随着机器人、无人机及自动驾驶车辆等领域对智能、自适应与低功耗自主系统的需求增长，类脑计算日益受到关注。受生物神经系统的启发，类脑方法为提升自主平台的感知、决策与响应能力提供了新路径。本文综述了类脑算法、专用硬件及跨层优化策略的最新进展，重点探讨其在真实自主场景中的部署。特别关注事件驱动型动态视觉传感器在实现快速、高效感知中的作用。新方法通过将脉冲神经网络融入系统架构，显著提升了能效、鲁棒性、适应性与可靠性。文章融合机器学习、机器人学、神经科学与类脑工程视角，全面呈现该领域现状。最后，探讨了实时决策、持续学习以及安全可靠自主系统构建中的新兴趋势与开放挑战。
原文摘要 · Abstract (English)
The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving interest in neuromorphic computing. By drawing inspiration from biological neural systems, neuromorphic approaches offer promising pathways to enhance the perception, decision-making, and responsiveness of autonomous platforms. This paper surveys recent progress in neuromorphic algorithms, specialized hardware, and cross-layer optimization strategies, with a focus on their deployment in real-world autonomous scenarios. Special attention is given to event-based dynamic vision sensors and their role in enabling fast, efficient perception. The discussion highlights new methods that improve energy efficiency, robustness, adaptability, and reliability through the integration of spiking neural networks into autonomous system architectures. We integrate perspectives from machine learning, robotics, neuroscience, and neuromorphic engineering to offer a comprehensive view of the state of the field. Finally, emerging trends and open challenges are explored, particularly in the areas of real-time decision-making, continual learning, and the development of secure, resilient autonomous systems.
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