Year: 2025
Type: preprint
Abstract: Autonomous driving technology has seen significant advancements in recent years, yet safety and reliability remain major barriers to large-scale deployment. Traditional testing methods rely heavily on mileage accumulation and synthetic simulation, which fail to capture the rare but critical human errors that lead to real-world accidents. This paper proposes a testing and validation framework driven by the analysis of human-related accidents and conflicts. By integrating accident causality modeling, near-miss scenario mining, and behavior-driven simulation, the framework aims to construct high-risk, high-value testing scenarios that accelerate the safety verification process of autonomous vehicles (AVs).
Author: Arif Hossain
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414171601
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