用符号化AI提升自动驾驶系统的可测性与可验证性
NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
- 用符号逻辑替代随机层，实现确定性决策
- 支持多传感器融合与系统自适应，提升可靠性
- 适合需要高安全认证的自动驾驶系统研发
自主网络物理系统（CPS）利用人工智能进行感知、规划和控制，但因内在不确定性面临可信度与安全认证难题。神经符号范式将随机层替换为可解释的符号化AI，实现确定性推理。尽管前景可观，多传感器融合、适应性及验证仍是挑战。本文提出NeuroStrata框架，通过集成符号逻辑与神经网络，增强自主CPS的测试与验证能力。阐述了其核心组件，展示初步结果，并规划后续发展方向。
原文摘要 · Abstract (English)
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust and safety certification challenges due to inherent uncertainties. The neurosymbolic paradigm replaces stochastic layers with interpretable symbolic AI, enabling determinism. While promising, challenges like multisensor fusion, adaptability, and verification remain. This paper introduces NeuroStrata, a neurosymbolic framework to enhance the testing and verification of autonomous CPS. We outline its key components, present early results, and detail future plans.
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