# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 1 Introduction  
The surge in the number of motor vehicles has enhanced transportation experiences but has also led to greater traffic congestion and environmental pollution. The advancement of intelligent transportation systems has become crucial for achieving efficient, convenient, sustainable, and safe travel. Consequently, autonomous driving and intelligent connected vehicle technology have emerged as prominent trends. Driving style is not only closely related to road safety and environmental protection but also significantly impacts the development of autonomous driving technology. By analyzing driving styles, potential high-risk drivers can be identified, and traffic accidents can be proactively prevented through education and training. Moreover, such research could provide recommendations for eco-friendly and energy-efficient driving practices, thereby reducing energy consumption and costs. Understanding driving styles also helps autonomous driving systems interpret drivers' intentions more accurately, leading to more informed decision-making. This insight contributes to the design of autonomous driving systems that better align with driver expectations, enhancing the overall driving experience. Additionally, it enables the development of effective warning and intervention mechanisms to prevent traffic accidents.  
Driving behaviors such as free driving, car-following, and lane-changing correspond to various driving states throughout the driving process. Drivers may exhibit distinct driving styles depending on different scenarios. To effectively reveal these driving-style differences and obtain a comprehensive evaluation, it is crucial to classify driving styles and assess them across various driving states. Current research, however, mostly focuses on individual driving scenarios and lacks a systematic approach that considers multiple contexts. Therefore, this paper proposes a method for driving-style recognition based on a real and representative dataset of driving behaviors. Using clustering and classification algorithms from machine learning, the study aims to categorize driving styles across different scenarios accurately. The objective is to reflect the diverse driving styles of various drivers and provide a broadly applicable design concept.  
Therefore, this paper proposes a novel method for recognizing driving styles based on naturalistic driving data from the NGSIM dataset. The approach begins by constructing vehicle datasets for car-following and lane-changing scenarios. The vehicle state parameters representing driving style are optimized, followed by dimensionality reduction using correlation analysis and principal component analysis. The K-means clustering algorithm is then applied to categorize the principal components in both car-following and lane-changing scenarios, and the clustering results are analyzed. Additionally, a comprehensive evaluation of driving styles is conducted across these two driving scenarios. Subsequently, a driving-style recognition model is developed using the Support Vector Machine (SVM), which demonstrates superior accuracy compared to models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. The effectiveness of the SVM model is further validated through a comparison of recognition results before and after dimensionality reduction.  
To provide a thorough context for the study, we begin by reviewing the existing literature on driving styles, followed by a detailed description of the methodology, including data sources and analytical techniques. Subsequently, research findings, which include data analysis and modeling, are presented in relation to prior research. Finally, the paper concludes with practical implications and recommendations for future research.