Perceived risk in automated driving is often measured as discrete scores that summarise riding experience but this obscures volatile peaks from sustained elevation. Here we treat discrete clipwise ratings as constraints on an unobserved inferred evolution and apply a kernel constrained inverse model to infer the temporal evolution of perceived risk. Across 2,164 participants and 141,628 discrete clipwise ratings spanning 236 hours of scripted motorway interactions, we infer evolutions under kernel constraints whose shapes follow priors from independent handset-based ratings and whose timing is fixed by scripted manoeuvre markers. The inferred perceived risk evolutions differentiate accumulated perceived risk from within clip concentration, revealing scenario differences that are not identifiable from peak judgements alone. We then map these inferred evolutions from observable vehicle and relative motion cues under strict event level holdout using a deep neural network, enabling interpretable attribution analyses. Attribution shows distinct patterns between risk rising and falling segments, with a shift toward conflict cues in the rising phase, and a rebound toward stability cues in the falling phase. Attribution concentration increases only modestly at high perceived risk levels. These results move beyond treating perceived risk as a single severity score by characterising within episode dynamics and phase dependent cue associations in scripted motorway interactions.

翻译：自动驾驶中的感知风险通常以离散评分形式测量，这些评分虽能概括驾乘体验，却掩盖了持续高位状态下的波动峰值。本研究将离散片段评分视为对未观测推断演化过程的约束，应用核约束逆模型来推断感知风险的时间演化。通过对2,164名参与者产生的141,628个离散片段评分（涵盖236小时脚本化高速公路交互场景）进行分析，我们在核约束下推断出感知风险演化曲线：其形态遵循基于独立手持设备评分的先验分布，其时序由脚本化操作标记点确定。推断出的感知风险演化区分了累积感知风险与片段内风险集中度，揭示了仅凭峰值判断无法识别的场景差异。随后，我们通过深度神经网络在严格事件级别留出验证下，将车辆可观测特征与相对运动线索映射到这些推断演化过程，从而实现可解释的归因分析。归因结果显示风险上升段与下降段存在明显差异模式：上升阶段向冲突线索偏移，下降阶段则向稳定线索回调。在高感知风险水平下，归因集中度仅呈现适度增长。这些成果突破了将感知风险视为单一严重性评分的局限，通过刻画脚本化高速公路交互中感知风险的动态演变过程及相位依赖的线索关联特征，实现了方法论层面的推进。