Objective: In the context of the swift progression of autonomous driving technology, the widespread reliance of current systems on uniform behavioral models for decision-making and path planning is a crucial concern. This generalized approach often disregards variations in driving behavior among different drivers, making it challenging to achieve driving behavior that aligns with drivers' expectations in complex and dynamic traffic scenarios. Consequently, a decrease in comfort and trust is observed in autonomous vehicles. This study focuses on lane changing, a common yet critical driving maneuver, aiming to optimize planning strategies by incorporating drivers' characteristics to match individual driving styles. Methods: This study comprehensively analyzes data derived from naturalistic driving experiments. Kalman filtering is used to detect and eliminate anomalies in raw data, thereby reducing noise interference. The integration of temporal constraints into the fuzzy C-means clustering algorithm ensures the preservation of chronological order in the clustered data, which is essential for analyzing sequential events such as lane change maneuvers. Lane changing requires lateral and longitudinal vehicle control with distinct operational characteristics across different phases of the maneuver. By clustering the entire lane-changing process data into three major categories, C1, C2, and C3, representing the preparation, execution, and completion stages of lane changing, respectively, this study aims to analyze disparities in driver behavior during these distinct phases. According to the characteristics of lane-changing scenarios, relevant variables are selected for in-depth examination. Independent sample t-tests are then conducted among different drivers for each variable, and variables with a high proportion of insignificant t-values are eliminated. This process helps identify personalized indicators that reflect driver-specific traits during lane changing. Subsequently, an artificial potential field (APF) model is established for the lane-changing scenario. The APF method uses virtual attractive and repulsive forces to guide the vehicle toward a path of decreasing potential energy, effectively avoiding obstacles while moving toward the target position. Variations in the APF parameters lead to different planning paths. By leveraging the extracted personalized indicator, the APF model for lane changing is customized, yielding paths that align with individual driving styles. Another pivotal consideration is the planning of lane-changing speeds. Given the notable variations in the speed preferences of drivers, this study proposes a lane-changing speed planning algorithm based on a quintic polynomial function. This ensures that the mean duration of acceleration and the maximum acceleration limit during the execution phase align with each driver's speed control habits and that a smooth velocity profile is maintained throughout the lane-changing maneuver. Conclusions: This study proposes a lane-changing planning method for autonomous vehicles that considers driver differences. The simulation results confirm that the proposed personalized lane-changing planning approach not only produces paths that align with individual driving styles but also regulates lane-changing velocities in accordance with each driver's operational habits. By quantifying behavioral variations, developing personalized APF models, and implementing customized speed planning strategies, this study exemplifies how to tackle individualization challenges in autonomous driving. This study represents a step forward in advancing autonomous vehicle technology toward a human-centric and intelligent future.