Advancing simulation-based driver training

Abstract

This paper aims to provide recommendations for improving the effectiveness of automatic, student-adaptive, simulation-based driver training. Using experiments and recorded data in driving simulators, three distinct issues are discussed: 1) the student, 2) the virtual driving instructor (VDI), and 3) the student-profile. We found that: first, students seek task-relevant information themselves; not providing them with feedback can be beneficial. Second, an intelligent VDI that emulates a human driving instructor is not favored. To the contrary, regressive instruction -- a relatively simple principle -- was effective in letting students drive away autonomously. Third, constructing a student-profile based on individual characteristics, such as a strength-weakness report, is viable for providing student-adaptive feedback.