Criteria hierarchy judgment matrix.

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.传统驾驶员技能考核主要评估考生是否在规定考核任务中达到既定标准，却难以全面反映其在真实交通场景中的整体驾驶表现，进而可能导致驾驶结果未达最优。车道保持能力（lane-keeping ability）是评估驾驶员综合驾驶能力的核心指标，其可反映驾驶员在车辆操控、道路环境感知与应急处置方面的熟练程度。但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领域尚缺乏有效的车道保持能力评估手段。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，为适配考核场景中的真实车道保持工况，本文从真实低速自然驾驶数据（naturalistic driving data）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与稳定性。随后，通过K均值聚类（K-means clustering）方法分析这些指标的分布特征，识别出特征相似的指标群组。进一步结合尤登指数（Youden index）、箱线图（Boxplot）与统计量确定各指标的阈值，以此提升评估准确性。最后，基于层次分析法（Analytic Hierarchy Process, AHP）结合主客观权重，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MIF的车道保持评估方法，通过自然驾驶数据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。