Development of Risk-Situation Scenario for Autonomous Vehicles on Expressway Using Topic Modeling

Abstract

Growing interest has recently been paid to the development of autonomous vehicle scenarios, and corresponding research is being conducted on various methodologies and on the generation of scenarios including technological elements. However, most studies have focused on frequently-occurring accident types or representative accident situations; thus, there is a lack of studies on scenarios considering unpredictable accidents. Proper preparation is required for accident situations because even a small traffic accident that is less likely to occur can lead to fatalities if it is difficult to predict. Accordingly, this study established accident situations based on the Pegasus layer model by using unstructured text data to explain traffic accidents on expressways in Korea. The established accident situations were classified into three types (Typical Traffic/Critical Traffic/Edge Case) according to frequency. Topic modeling was applied to the Edge Case class, i.e., the least likely to occur and thus difficult to predict, to analyze the characteristics of groups and develop risk-situation scenarios for autonomous vehicles based on actual accident data.