# Predicting Driver Takeover Time in Conditionally Automated Driving

arXiv: https://arxiv.org/abs/2107.09545 · HTML: https://mlsanity.com/paper/2107.09545/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving](https://mlsanity.com/paper/2001.04509/) (2020)
- [Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving](https://mlsanity.com/paper/2103.14792/) (2021)
- [Psychophysiological responses to takeover requests in conditionally automated driving](https://mlsanity.com/paper/2010.03047/) (2020)

## Built on by (most cited first, 4 of 4)

- [Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines](https://mlsanity.com/paper/2304.04273/) (2023)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline](https://mlsanity.com/paper/2111.03511/) (2021)
- [A quantitative model of takeover request time budget for conditionally automated driving](https://mlsanity.com/paper/2408.15682/) (2024)

## Abstract (arXiv)

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.