Overall importance of hierarchy B.

Traditional driver’s skill tests primarily assess whether candidates meet specific standards in prescribed tasks, which often fails to fully reflect their overall driving performance in real-world scenarios. This can lead to suboptimal driving outcomes. Lane-keeping ability is a key indicator for evaluating a driver’s overall competence, as it reflects their proficiency in vehicle control, road environment perception, and emergency handling. However, due to the complex and varied factors influencing lane-keeping ability, there is currently a lack of effective methods for assessing this skill during drive skill tests. To address this gap, this paper proposes a multi-indicator fusion (MIF) method for evaluating lane-keeping ability in driver skill tests. First, to accommodate real-world lane-keeping scenarios in drive skill tests, multidimensional indicators representing lane-keeping ability are extracted from real low-speed naturalistic driving data, considering both lateral and longitudinal safety and stability. Next, by analyzing the distribution characteristics of these indicators using the K-means clustering method, groups of indicators with similar characteristics are identified. Furthermore, the Youden index, Boxplot, and statistical measures are then employed to determine the threshold values for each indicator, enhancing the accuracy of the evaluation. Finally, a comprehensive evaluation model for lane-keeping ability is constructed using the Analytic Hierarchy Process (AHP) based on a combination of subjective and objective weightings. The proposed MIF-based lane-keeping assessment method for drive skill tests was effectively validated in terms of its rationality and feasibility using naturalistic driving data. This study provides valuable reference points for assessing lane-keeping ability in the context of future autonomous driving environments.

传统机动车驾驶员技能考核主要以验证应试者是否满足指定任务中的特定标准为核心，但往往无法全面反映应试者在真实场景中的整体驾驶表现，进而可能导致实际驾驶表现欠佳。车道保持能力是评估驾驶员综合驾驶能力的关键指标，因其能够体现驾驶员在车辆操控、道路环境感知以及应急处置方面的熟练程度。然而，由于影响车道保持能力的因素复杂多样，当前驾驶员技能考核中尚缺乏针对该能力的有效评估方法。为填补这一研究空白，本文提出一种面向驾驶员技能考核的车道保持能力多指标融合（Multi-indicator Fusion, MIF）评估方法。首先，为适配驾驶员技能考核中的真实车道保持场景，本文从真实低速自然驾驶数据中提取表征车道保持能力的多维指标，同时兼顾横向与纵向的安全及稳定性要求。随后，借助K-means聚类方法分析上述指标的分布特征，可识别出特征相似的指标群组。进一步地，本文采用尤登指数、箱线图（Boxplot）与统计量来确定各指标的阈值，以此提升评估的准确性。最后，本文基于主客观权重结合的方式，借助层次分析法（Analytic Hierarchy Process, AHP）构建车道保持能力综合评估模型。本文所提出的面向驾驶员技能考核的基于多指标融合的车道保持评估方法，通过自然驾驶数据有效验证了其合理性与可行性。本研究可为未来自动驾驶场景下的车道保持能力评估提供重要参考依据。