Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions

no code implementations • 6 May 2024 • Ziye Qin, Siyan Li, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han

The results show that the Personalized Transformer Encoder improves the accuracy of predicting driver decision-making in the dilemma zone by 3. 7% to 12. 6% compared to the Generic Transformer Encoder, and by 16. 8% to 21. 6% over the binary logistic regression model.

Decision Making