Assessing Data Imbalance Correction Methods and Gaze Entropy for Collision Prediction

Driver Readiness (DR) refers to the likelihood of drivers successfully recovering control from automated driving and is correlated with collision avoidance. When designing Driver Monitoring Systems (DMS) it is useful to understand how driver states and DR interact, through predictive modelling of collision probability. However, collisions are rare and generate imbalanced datasets. Whilst rebalancing can improve model stability, reliability of correction methods remains untested in automotive research. The current study therefore examined logistic regression’s reliability when using imbalance-corrected data, and the predictive utility of gaze entropy and pupil diameter in assessing collision risk during critical takeovers from a simulated hands-off SAE L2 driving experiment. Dataset rebalancing reduced prediction accuracy and overestimated collision probabilities, aligning with prior findings on its limitations. Erratic, spatially distributed gaze fixations were associated with higher collision probability. These findings underscore gaze behaviour’s importance in DR estimation and the challenges of dataset rebalancing for predictive DR modelling.