| 제목 |
Driver Behavior Analysis in Simulated Jaywalking and Accident Prediction Using Machine Learning Algorithms |
| 분야 |
ADAS, AI, Autonomous Vehicles |
| 언어 |
English |
| 저자 |
Myeongkyu Lee(School of Industrial Engineering, Purdue University), Jihun Choi(Traffic Accident Division, National Forensic Service), Songhui Kim(Traffic Accident Division, National Forensic Service), Ji Hyun Yang(Department of Automotive Engineering, Kookmin University) |
| Key Words |
Accident analysis, Classification, Driver behavior characteristic, Prediction, Automotive Engineering |
| 초록 |
Road safety can be improved if traffic accidents can be predicted and thus prevented. The use of driver-related variables to determine the possibility of an accident presents a new analysis paradigm. We used a driving simulator to create a jaywalking scenario and investigated how drivers responded to it. A total of 155 valid participants were identified across demographics (age group and gender) and participated in the experiment. We collected driver-related data on eight types of perception/reaction times, vehicle-control data, accident occurrence data, and maneuvers used for obstacle avoidance. From the statistical analysis, it was possible to derive six variables with significant differences based on whether a traffic accident occurred. Furthermore, we identified the data’s significant difference according to demographics. Artificial intelligence (AI)-classification models were used to predict whether an accident would occur with up to 90.6% accuracy. The data associated with the dangerous scenario obtained in this study were identified to predict the occurrence of traffic accidents. |
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