# DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

arXiv: https://arxiv.org/abs/2012.15441 · HTML: https://mlsanity.com/paper/2012.15441/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers](https://mlsanity.com/paper/2410.02592/) (2024)
- [Learning from Active Human Involvement through Proxy Value Propagation](https://mlsanity.com/paper/2502.03369/) (2025)
- [Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation](https://mlsanity.com/paper/2107.12932/) (2021)
- [Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components](https://mlsanity.com/paper/2202.03360/) (2022)
- [Data-Efficient Learning from Human Interventions for Mobile Robots](https://mlsanity.com/paper/2503.04969/) (2025)
- [Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles](https://mlsanity.com/paper/2507.04139/) (2025)
- [BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving](https://mlsanity.com/paper/2604.07263/) (2026)
- [F-RDW: Redirected Walking with Forecasting Future Position](https://mlsanity.com/paper/2304.03497/) (2023)
- [AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars](https://mlsanity.com/paper/2306.03115/) (2023)
- [Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions](https://mlsanity.com/paper/2507.08238/) (2025)
- [DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning](https://mlsanity.com/paper/2603.28251/) (2026)
- [Safety-Aware Evaluation of LLM-Generated Driver Intervention Messages through Multi-Task Risk Fusion](https://mlsanity.com/paper/2606.22706/) (2026)

## Abstract (arXiv)

Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems determine the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it. We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. Using features from vehicle data, driver biometrics, and subjective measurements, DeepTake predicts the driver's intention, time, and quality of takeover. We evaluate DeepTake performance using multiple evaluation metrics. Results show that DeepTake reliably predicts the takeover intention, time, and quality, with an accuracy of 96%, 93%, and 83%, respectively. Results also indicate that DeepTake outperforms previous state-of-the-art methods on predicting driver takeover time and quality. Our findings have implications for the algorithm development of driver monitoring and state detection.