让深度学习模型具备可解释性，助力自动驾驶系统安全决策。
Interpretable Neural System Dynamics: Combining Deep Learning with System Dynamics Modeling to Support Critical Applications
- 融合概念与机制解释技术，构建可解释的神经动力学框架。
- 在欧盟AutoMoTIF项目中验证，提升模型因果可靠性与可扩展性。
- 适合关注自动驾驶安全与可解释性的研究者与工程师。
本项目旨在弥合深度学习（DL）与系统动力学（SD）之间的差距，提出一种可解释的神经系统动力学框架。虽然深度学习擅长学习复杂模型并实现高精度预测，但缺乏可解释性与因果可靠性；而传统系统动力学虽具透明性与因果洞察力，却受限于可扩展性且需大量领域知识。为此，本研究引入神经系统动力学流程，融合基于概念的可解释性、机制可解释性及因果机器学习，使模型兼具深度学习的预测能力与系统动力学的可解释性，实现因果可靠性和可扩展性。该框架将在欧盟资助的AutoMoTIF项目中验证，聚焦于自动驾驶多模态交通系统。长期目标是获取可操作的洞见，支持自主系统中的可解释性与安全性集成。
原文摘要 · Abstract (English)
The objective of this proposal is to bridge the gap between Deep Learning (DL) and System Dynamics (SD) by developing an interpretable neural system dynamics framework. While DL excels at learning complex models and making accurate predictions, it lacks interpretability and causal reliability. Traditional SD approaches, on the other hand, provide transparency and causal insights but are limited in scalability and require extensive domain knowledge. To overcome these limitations, this project introduces a Neural System Dynamics pipeline, integrating Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. This framework combines the predictive power of DL with the interpretability of traditional SD models, resulting in both causal reliability and scalability. The efficacy of the proposed pipeline will be validated through real-world applications of the EU-funded AutoMoTIF project, which is focused on autonomous multimodal transportation systems. The long-term goal is to collect actionable insights that support the integration of explainability and safety in autonomous systems.
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