L3级自动驾驶中驾驶人接管失效是制约行车安全的关键瓶颈，而行人冲突场景下的接管安全问题尤为突出。本研究基于高仿真驾驶模拟平台，考虑场景紧急程度与非驾驶相关任务（Non-Driving Related Tasks, NDRTs）两类因素，设计低、中、高三种紧急程度的行人冲突接管场景（场景1为无信控路口行人过街事件，场景2为直行路段行人过街事件，场景3为直行路段盲区行人过街事件），招募42名被试开展驾驶模拟实验以同步获取驾驶行为与生理响应数据。从生理敏感性、反应及时性、操控稳定性、乘坐舒适性和避让安全性五个维度构建评价指标体系，对场景紧急程度与NDRTs影响下驾驶人接管变化规律展开统计分析，并提出一种综合生理及行为表现的接管绩效评价方法。结果表明：场景紧急程度及NDRT的执行显著降低接管绩效水平，两者存在显著交互效应，三个场景的接管绩效得分（Take-over Performance Index, TPI）呈梯度下降，由场景1的0.601降至场景3的0.523，其中场景3的较差等级占比高达39.2%；执行NDRT使整体下降约11.4%，且在盲区场景下降幅最大。整体接管绩效TPI得分中，避让安全性指标min TTC权重最高为0.227，生理敏感性指标权重较低，驾驶行为类指标在综合评价中占主导地位，生理指标虽能敏感捕捉应激唤醒与认知负荷变化，但在最终分级中的区分度相对有限，可以作为解释驾驶人状态波动与风险机制的补充信息。本研究成果可为有条件自动驾驶接管策略设计与安全风险评估提供方法支撑和理论支持。

Takeover failures in Level 3 (L3) automated driving constitute a critical bottleneck for roadway safety, and the challenge is particularly acute in pedestrian-conflict scenarios. This study examined two key factors, i.e., scenario criticality and non-driving-related tasks (NDRTs) by designing three pedestrian-conflict takeover scenarios using a high-fidelity driving simulator. (Scenario 1 involved a pedestrian crossing at an unsignalized intersection, Scenario 2 involved a pedestrian crossing on a straight road segment, and Scenario 3 involved a pedestrian suddenly appearing from a blind spot on a straight road segment.) A total of 42 participants were recruited to complete the experiment, during which drivers’ driving behavior and physiological responses were synchronously recorded. An evaluation indicator system was established across five dimensions: physiological sensitivity, response timeliness, control stability, ride comfort, and evasive safety. Statistical analyses were conducted to characterize takeover behavior under varying levels of scenario criticality and NDRT engagement, and an integrated takeover-performance assessment method combining physiological and behavioral measures was proposed. Results show that both higher scenario criticality and NDRT engagement significantly degrade takeover performance, with a significant interaction effect between the two factors. The Takeover Performance Index (TPI) exhibited a gradient decline across the three scenarios, decreasing from 0.601 in Scenario 1 to 0.523 in Scenario 3, with 39.2% of participants classified as "poor" performance in Scenario 3. NDRT engagement reduced overall TPI by approximately 11.4%, with the largest decrement observed in the blind-spot scenario. Among TPI components, the evasive safety indicator (minimum TTC) carried the highest weight (0.227), whereas physiological sensitivity indicators had relatively lower weights. The largest performance decrement occurred when participants performed an NDRT in the occluded (blind-spot) pedestrian sudden-appearance scenario. For the TPI, behavioral indicators dominated the composite evaluation. Although physiological measures sensitively captured changes in stress-related arousal and cognitive workload, their discriminative power in the final performance grading was comparatively limited. Thus, they are better positioned as complementary information for interpreting driver state fluctuations and underlying risk mechanisms. Collectively, these findings provide methodological and theoretical support for takeover strategy design and safety-risk assessment in conditionally automated driving.