AI 中文总结
该研究提出一种基于集合的通用认知状态估计框架，无需大量数据集或噪声分布假设，在有条件自动驾驶中估计驾驶员的信任、感知风险和工作负荷，其集合值估计一致性达75%以上，多步依赖预测性能优于基线方法。
AI 中文摘要
我们提出一种用于人机自动化交互（HAI）场景中人类认知状态估计的基于集合的框架。与HAI文献中占主导地位的概率方法不同，该框架将过程和测量不确定性视为未知但有界的量，无需大型结构化数据集或噪声的分布假设。我们在有条件自动化（SAE 3级）驾驶场景中验证该框架，在此场景中我们估计三种认知状态（信任、感知风险和工作负荷），这些状态会影响人类在持续、非试验性交互中对自动化系统的依赖。我们利用混合动力学建模框架识别个体特定的过程和测量模型，通过执行可达集一致性系统估计噪声边界，并确定影响每个个体对自动化依赖的认知状态子集。随后，通过融合二元依赖观测值和间歇性、量化的自我报告，生成这些状态的集合值估计。该框架通过在中等保真度驾驶模拟器中开展的线下实验进行评估，共有20名参与者参与。集合值估计器在测试期间对大多数参与者实现至少75%的一致性，且从估计值得出的多步超前依赖预测在所有预测步长（15、30、45、60时间步）下均优于开环基线和粒子滤波器，在较长步长下性能差距更为显著。所提出的估计框架可使自动化系统持续感知并响应人类驾驶员的状态。
英文摘要
We present a set-based framework for estimating human cognitive states in human-automation interaction (HAI) contexts. Unlike probabilistic approaches dominant in the HAI literature, our framework treats process and measurement uncertainties as unknown but bounded, avoiding the need for large structured datasets or distributional assumptions on noise. We demonstrate the framework in the context of conditionally automated (SAE Level 3) driving, where we estimate three cognitive states (trust, perceived risk, and workload) that influence human reliance on the automation during a continuous, non-trial-based interaction. We leverage a hybrid dynamical modeling framework to identify individual-specific process and measurement models, systematically estimate noise bounds by enforcing reachset conformance, and identify the subset of cognitive states that influence each individual's reliance on the automation. Set-valued estimates of those states are then produced by fusing binary reliance observations and intermittent, quantized self-reports. The framework is evaluated through an in-person experiment in a medium-fidelity driving simulator with 20 participants. The set-valued estimator achieves at least 75% consistency for most participants during testing, and multi-step-ahead reliance predictions derived from the estimates outperform both an open-loop baseline and a particle filter across all choices of prediction horizons (15, 30, 45, 60 time steps), with the performance gap more evident at longer horizons. The proposed estimation framework can enable automation systems that are continuously aware of, and responsive to, the human driver's state.
