让自动驾驶实时解释超车等操作为何合理，提升安全与信任。
Context-aware, Ante-hoc Explanations of Driving Behaviour
- 用交通序列图形式化驾驶情境与异常行为
- 运行时识别上下文并即时生成解释
- 适合自动驾驶可解释性研究者与工程师
自动驾驶车辆（AV）要获得社会认可并成为日常公共交通的可行选择，必须既安全又可信。解释系统行为有助于提升安全性和信任度。然而，基于AI的驾驶功能决策过程常不透明，解释难度大。解释工程领域通过在设计阶段构建解释模型来应对这一挑战，这些模型基于系统设计文档和利益相关者需求，旨在生成正确且高质量的解释。为此，本文提出一种支持上下文感知、事前生成的运行时解释方法，用于说明（非）预期驾驶行为。采用可视化且形式化的语言——交通序列图（Traffic Sequence Charts），对解释上下文及对应（非）预期驾驶行为进行形式化表达。通过专用运行时监控实现上下文识别，并在运行时实时呈现解释。整体目标是支持生成准确且高质量的解释。方法在模拟超车场景中得到验证。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs. Unfortunately, explaining the system behaviour of AI-based driving functions is particularly challenging, as decision-making processes are often opaque. The field of Explainability Engineering tackles this challenge by developing explanation models at design time. These models are designed from system design artefacts and stakeholder needs to develop correct and good explanations. To support this field, we propose an approach that enables context-aware, ante-hoc explanations of (un)expectable driving manoeuvres at runtime. The visual yet formal language Traffic Sequence Charts is used to formalise explanation contexts, as well as corresponding (un)expectable driving manoeuvres. A dedicated runtime monitoring enables context-recognition and ante-hoc presentation of explanations at runtime. In combination, we aim to support the bridging of correct and good explanations. Our method is demonstrated in a simulated overtaking.
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