| Title |
Selection of Evaluation Metrics for Grading Autonomous Driving Car Judgment Abilities Based on Driving Simulator |
| Authors |
오민종(Oh, Min Jong) ; 진은주(Jin, Eun Ju) ; 한미선(Han, Mi Seon) ; 박제진(Park, Je Jin) |
| DOI |
https://doi.org/10.12652/Ksce.2024.44.1.0063 |
| Keywords |
자율주행차; 드라이빙 시뮬레이터; 판단능력; K-평균 군집분석 Autonomous vehicle; Driving simulator; Judgment abilility; K-means cluster analysis |
| Abstract |
Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research. |