用意图感知图解释自动驾驶决策，让复杂行为变透明。
Explaining Autonomous Vehicles with Intention-aware Policy Graphs
- 基于意图感知策略图提取可解释的驾驶行为路径。
- 在nuScenes数据集上验证了行为解释的有效性与可靠性。
- 适合关注自动驾驶安全与合规性的研究人员和开发者。
自动驾驶有望提升道路安全、减少人为驾驶错误并促进环境可持续性，近年来发展迅速。得益于人工智能，特别是深度学习的进步，自动驾驶车辆性能显著提升。然而，其决策过程因依赖高精度但复杂的AI模型而难以理解，阻碍了社会信任与监管认可，亟需可解释性。本文提出一种后置、模型无关的解决方案，为城市环境中自动驾驶车辆的行为提供目的性解释。基于意图感知策略图，该方法能从全局与局部视角提取nuScenes数据集中的可解释、可靠行为解释。实验证明，这些解释可用于评估车辆是否符合合法边界，并识别自动驾驶数据集与模型中的潜在漏洞。
原文摘要 · Abstract (English)
The potential to improve road safety, reduce human driving error, and promote environmental sustainability have enabled the field of autonomous driving to progress rapidly over recent decades. The performance of autonomous vehicles has significantly improved thanks to advancements in Artificial Intelligence, particularly Deep Learning. Nevertheless, the opacity of their decision-making, rooted in the use of accurate yet complex AI models, has created barriers to their societal trust and regulatory acceptance, raising the need for explainability. We propose a post-hoc, model-agnostic solution to provide teleological explanations for the behaviour of an autonomous vehicle in urban environments. Building on Intention-aware Policy Graphs, our approach enables the extraction of interpretable and reliable explanations of vehicle behaviour in the nuScenes dataset from global and local perspectives. We demonstrate the potential of these explanations to assess whether the vehicle operates within acceptable legal boundaries and to identify possible vulnerabilities in autonomous driving datasets and models.
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