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Open Access

ARTICLE

Machine Learning-Based Analysis of Contributing Factors Affecting Autonomous Driving Behavior in Urban Mixed Traffic

Hoyoon Lee1, Jeonghoon Jee1, Hoseon Kim2, Cheol Oh1,*
1 Department of Transportation and Logistics Engineering, Hanyang University ERICA, Ansan-si, Gyeonggi, Republic of Korea
2 Department of Smart City Engineering, Hanyang University ERICA, Ansan-si, Gyeonggi, Republic of Korea
* Corresponding Author: Cheol Oh. Email: email
(This article belongs to the Special Issue: AI-Driven Big Data Analytics for Sustainable Mixed Traffic and Mobility Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076980

Received 30 November 2025; Accepted 06 January 2026; Published online 26 January 2026

Abstract

Analyzing the driving behavior of autonomous vehicles (AV) in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design, providing infrastructure-based guidance information, and developing capability-enhanced AV perception systems. This study investigated the contributing factors affecting AV driving behavior using the Waymo Open Dataset. Binarized autonomous driving stability metrics, derived via a kernel density estimation, served as the target variables for a random forest classification model. The model’s input variables included 15 factors divided into four types: intersection-related, surrounding object-related, road infrastructure-related, and time-of-day-related types. The random forest classification model was employed to identify the key factors affecting autonomous driving behavior. In addition, the identified factors were further ranked based on feature importance. SHAP analysis was utilized to enhance model interpretability by quantifying the contribution of each factor and identifying their directional impacts. The type of intersection factor was found to have an importance of 0.243 and was the most influential factor on autonomous driving behavior. On average, intersection-related factors had an importance of 0.196, which is approximately a 31.1% margin over the average importance of surrounding object-related factors. Additionally, the surrounding object-related factors that were collected through sensors on the autonomous vehicle had a high degree of feature importance, especially with the number of pedestrians having the highest importance (0.107) of the types of objects. The correlation between these findings can contribute to the development of various treatments to improve more harmonized AVs’ maneuvering with other road users and facilities in urban mixed traffic environments.

Keywords

Waymo open dataset; autonomous driving stability; principal component analysis; random forest; SHAP

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