Predicting Driver Takeover Time in Conditionally Automated Driving

TL;DR

This study develops a machine learning model to accurately predict driver takeover time in conditionally automated driving, considering multiple factors simultaneously, and explains the influence of key predictors for safer vehicle automation transitions.

Contribution

The paper introduces a comprehensive prediction model using XGBoost that incorporates multiple factors affecting takeover time and employs SHAP for interpretability, advancing prior research that considered factors in isolation.

Findings

Identified seven key predictors influencing takeover time.

Achieved high prediction accuracy with the proposed model.

Provided insights into how predictors interact to affect takeover time.

Abstract

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle.