Machine-learning approach to analysis of driving simulation data

Abstract

In our study, we sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. We collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which we defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, we transformed the data at constant time intervals to generate qualitative data for learning. Finally, we generated rules using a Support Vector Machine (SVM).