TY  - JOUR
PY  - 2022//
TI  - Analysis of factors causing traffic near-miss events using deep neural networks trained to simulate human vision
JO  - Transactions of Society of Automotive Engineers of Japan
A1  - Kato, Masataka
A1  - Emura, Koichi
A1  - Watanabe, Eiji
SP  - 1108
EP  - 1113
VL  - 53
IS  - 6
N2  - In this paper, in order to clarify the relationship between human prediction characteristics based on prediction coding theory and traffic near miss incidents, analysis for the front video of drive recorders recorded traffic near miss incidents was conducted using deep learning model which simulates human vision, and two hypotheses were proposed. Using the prediction error indicator based on the hypothesis, it was confirmed that 30 out of 60 near miss video can explained by the hypothesis. It was indicated that the change of the prediction error effects the attention of the unconscious and may lead the traffic near miss incidents.<p />  <p>Language: ja</p>
LA  - ja
SN  - 0287-8321
UR  - http://dx.doi.org/10.11351/jsaeronbun.53.1108
ID  - ref1
ER  -