# Lane-keeping ability evaluation for driving skill tests: A multi-indicator fusion approach

## TL;DR

This paper introduces a new method to evaluate a driver's ability to stay in their lane using multiple indicators, improving driving skill assessments for real-world scenarios.

## Contribution

A novel multi-indicator fusion approach is proposed to assess lane-keeping ability using naturalistic driving data and statistical methods.

## Key findings

- Multidimensional indicators for lane-keeping ability were extracted from real low-speed naturalistic driving data.
- A comprehensive evaluation model was constructed using the Analytic Hierarchy Process with subjective and objective weightings.
- The proposed method was validated for rationality and feasibility using naturalistic driving data.

## Abstract

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.