Open Access

ARTICLE

Development of the Framework for Traffic Accident Visualization Analysis (F-TAVA) Based on the Conceptualization of High-Risk Situations in Autonomous Vehicles

Heesoo Kim¹, Minwook Kim¹, Hyorim Han², Soongbong Lee², Tai-jin Song^1,*

1 Department of Urban Engineering, Chungbuk National University, Cheongju-si, 28644, Republic of Korea
2 Department of Transport Big Data, The Korea Transportation Institute, Sejong-si, 30147, Republic of Korea

* Corresponding Author: Tai-jin Song. Email:

(This article belongs to the Special Issue: AI-Driven Big Data Analytics for Sustainable Mixed Traffic and Mobility Systems)

Computers, Materials & Continua 2026, 87(2), 36 https://doi.org/10.32604/cmc.2026.074802

Received 18 October 2025; Accepted 25 December 2025; Issue published 12 March 2026

Abstract

Autonomous vehicles operate without direct human intervention, which introduces safety risks that differ from those of conventional vehicles. Although many studies have examined safety issues related to autonomous driving, high-risk situations have often been defined using single indicators, making it difficult to capture the complex and evolving nature of accident risk. To address this limitation, this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process. High-risk situations are described using three complementary indicators: accident likelihood, accident severity, and accident duration. These indicators explain how risk emerges, increases, and persists over time. Based on this concept, a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization. The framework combines accident-phase information with factor-level risk contributions, allowing systematic identification of key factors and their interactions across different accident stages. Using combinations of the three indicators, high-risk situations are classified into twenty-seven distinct types, providing a clear typology for complex accident scenarios involving autonomous vehicles. The applicability of the proposed framework is demonstrated through two representative accident scenarios with different risk characteristics. The results show that the framework effectively captures interactions among multiple risk factors, explains how risk levels change from pre-crash to post-crash phases, and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods. Overall, the proposed framework offers a practical basis for autonomous vehicle accident analysis, safety evaluation, and policy-related decision-making.

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Keywords

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